qtl/0000755000175100001440000000000012567121773011103 5ustar hornikusersqtl/inst/0000755000175100001440000000000012566656321012061 5ustar hornikusersqtl/inst/BUGS.txt0000644000175100001440000004102412424414457013356 0ustar hornikusersBugs and bug fixes for R/qtl ---------------------------------------------------------------------- This file is intended to contain known bugs in the R/qtl package, version by version. The bugs marked with ">>" are not yet fixed. See STATUS.txt for further information on changes to the package. The list is not comprehensive. I don't do a terribly good job of keeping track of things I find and fix. The git log gives an explicit of source code changes, and contains descriptions of bug fixes. If you find a potential bug in R/qtl, please send an email, with as many details as possible and possibly example data, to Karl Broman, . ---------------------------------------------------------------------- Version 1.33 bugs: 1. read.cross with format="mapqtl" is not working in some cases. [Fixed in version 1.34.] Version 1.32 bugs: >> 1. mqmscan has problems when there are just two markers on a chromosome. It gives a seg fault if you scan just that chromosome, and it gives Infs in the "info" column in the results if you scan that and other chromosomes. Version 1.23 bugs: 1. Bug in scantwo permutations if markers on chr span < step and one uses incl.markers=FALSE. [Fixed in Version 1.24-9.] 2. Bug in checkcovar (in util.R) regarding omitting individuals with missing phenotypes when there's an ID column with numeric values. The wrong individuals get omitted from the cross. [Fixed in Version 1.24-9.] Version 1.16 bugs: 1. Bug in estimates from fitqtl for RIL: they need to be divided in half. The estimates for a backcross are correct, and we're not making the appropriate correction in RIL. [Fixed in Verison 1.16.] Version 1.11 bugs: 1. Problem in cim() in the case that multiple marker covariates are within the "window". [Fixed in Version 1.12.] >> 2. fitqtl doesn't handle covariates with the X chromosome appropriately in the drop-one-term analysis. When the X chromosome is omitted, the special covariates we need are also omitted, and so the LOD score for the X chromosome includes the effects of these special covariates. Also, if such covariates were included specifically in a call to fitqtl, the LOD score attached to the X chromosome would be correct, but the model df in the "full model result" is wrong, as these covariates get double-counted. Version 1.08 bugs: 1. LOD curves from plotLodProfile are misaligned in the case of linked QTL. [Fixed in Version 1.09.] 2. refineqtl doesn't use the correct range in the case of more than two QTL on a chromosome. [Fixed in Version 1.09.] Version 1.06 bugs: 1. Bug in fitqtl. Incorrect results can be obtained if covariates are placed before QTL terms in the formula. [Fixed in Version 1.07.] Version 1.05 bugs: 1. Bug in sim.cross regarding the QTL effects for a backcross. [Fixed in Version 1.06.] 2. Bug in write.cross concerning X chromosome genotypes in an intercross with all males and all pgm==1. [Fixed in Version 1.06.] 3. Bug in 2d scans via scanqtl; the first row of LOD scores are wrong. [Fixed in Version 1.06.] Version 1.04 bugs: >> 1. scanone with method="ehk" still shows convergences problems in the presence of interactive covariates, in some cases. 2. I don't completely trust the results of calc.errorlod. It seems to me that the LODs should be larger with a larger error probability, but this is not the case. I think I need to re-write this so that all genotypes but that under test are assumed to be correct. [Fixed in version 1.06; that the LOD scores decrease with increases in error probability appears to be correct, but the previous version missed*R cases of the following form: 1-1-1-...-1-1-1-2-1-2-2-2-...-2-2-2.] 3. Bug in scanone and scantwo permutations: covariates aren't permuted to match the phenotypes. [Fixed in Version 1.05.] Version 1.03 bugs: 1. In the "batch mode" permutations in scanone and scantwo with method="hk" or ="imp", sex and pgm were stripped off, and so the results were wrong in the presence of an X chromosome if there are some individuals of each sex and/or direction. [Fixed in version 1.04.] Version 1.01 bugs: 1. plot.pxg for X chr and autosome: results can be messed up, depending on the order of the markers. [Fixed in version 1.03.] 2. plot.geno() can give a bus error. This is due to a problem in locate.xo(). [Fixed in version 1.02.] >> 3. read.cross for "qtx" sometimes doesn't seem to take the genotype pattern appropriately; read in a backcross as if it were an F2. >> 4. An NA in the mapmaker data file caused an error in read.cross; the line became too long. Maybe this is true whenever an item doesn't match what is expected. 5. summary.scantwo doesn't work if there's just two positions on a chromosome (or maybe it's for one position). [Fixed in version 1.05.] Version 0.99 bugs: 1. est.rf() treats the X chromosome in an intercross incorrectly. [Fixed in version 1.00.] Version 0.98 bugs: 1. makeqtl() dies if the cross object contains QTL genotype probabilities and one seeks a QTL on the X chromosome. [Fixed in version 0.99.] 2. scanone gives incorrect results for the X chromosome with method="imp", when sex is included as an additive or interactive covariate. [Fixed in version 0.99.] Version 0.97 bugs: 1. In read.cross.qtlcart, it seems to print out the number of individuals as the number of phenotypes read. (A problem with "nrow" rather than "ncol".) [Fixed in version 0.98.] >> 2. In read.cross for the mapmaker format, when one uses a mapmaker map file, if a marker in that map file is indicated as unlinked (not placed on a chromosome), the data are read incorrectly. 3. In plot.scantwo, if the chromosomes are given in a different order, they are re-ordered to the standard one in the plot, but the chromosome labels and lines are not. An even weirder thing happens in plot.scanone. [Fixed in version 0.98.] 4. calc.errorlod dies for the X chromosome in an intercross. [Fixed in version 0.98.] 5. In read.cross for the mapmaker format, an error results if a marker name is listed on a line without any genotype data. [Fixed in version 0.98.] 6. Bug in read.cross for the QTL Cartographer format when there is exactly one individual. [Fixed in version 0.98.] Version 0.96 bugs: 1. Bug in summary.cross: when there are duplicate marker names, it fails to find them. [Fixed in version 0.97.] 2. Bug in read.cross: problem if a phenotype has values like "1x2". [Fixed in version 0.97.] 3. Bug in scanone for X chromosome with EM algorithm when there is a "sex" column in the phenotype data but all individuals have the same sex. [Fixed in version 0.98.] 4. There's likely an error in write.cross.qtlcart. See the line with "lo <- seq(1,n.ind-1,by=40)" [Fixed in version 0.97.] 5. Problem in makeqtl in the case of a four-way cross: the function doesn't take into account of the result of create.map() being a vector. [Fixed in version 0.97.] 6. Problem in fitqtl: it doesn't seem to recognize the case of there being no available "draws" data. [Fixed in version 0.97.] 7. fitqtl: doesn't work when you give only one qtl. [Fixed in version 0.97.] 8. scanone: when model="binary" and method="mr", a warning message should be displayed, and method="em" should be used. [This isn't really a bug; the software does do the binary trait analysis with just the single marker genotype information.] Version 0.95 bugs: 1. scantwo is not working well for the EM algorithm, in the case of the X chromosome [Fixed in version 1.02.] 2. I suspect that the treatment of the X chromosome is still not correct for scanone and scantwo. I think the null model needs to take sex into account. 3. In read.cross for format "csv", if not all lines have equal numbers of fields, a somewhat cryptic error message is given. Similarly, if there are missing values in the marker names, chromosome IDs, or marker poisitons, there are very cryptic error messages. (Thanks to Leo Schalkwyk for reporting this error.) [Fixed in version 0.96.] Version 0.94 bugs: 1. There was a bug in read.cross; the order of "C" and "D" in the genotypes argument needed to be reversed. [Fixed in version 0.95.] 2. For read.cross with format="csv", if there is a missing phenotype in the first individual (and a genetic map is not included) the function halts with an error. [Fixed in version 0.95.] Version 0.93 bugs: 1. There is a bug in summary.scanone for the case where several positions jointly share the maximum LOD score...all are given; it'd be best to just pick one. [Fixed in version 0.98.] 2. In ripple, error if window < 2. [Fixed in version 0.94.] 3. Make sure error.prob and similar arguments are always between 0 and 1 (e.g., in sim.cross and est.map) [Fixed in version 0.94.] Version 0.92 bugs: 1. There was a problem with read.cross with format="csv"; if the marker positions were missing, the first individual was skipped. [Fixed in version 0.93.] 2. Should use file.path() in the read.cross and write.cross functions. [Fixed in version 0.93.] 3. sim.cross gives incorrect results when the specified QTLs are not in order by chromosome. The QTLs are reordered, but their effects are not. [Fixed in version 0.93.] Version 0.91 bugs: 1. There was a problem with read.cross with method="csv" in R version 1.4.0. This was partly a bug in R, which has been fixed in version 1.4.1. But by changing the use of as.is=TRUE to colClasses="character", the new package should work in R ver 1.4.0 as well. [Fixed in version 0.92.] Version 0.90 bugs: 1. The X chromosome is not treated properly in either read.cross or, most importantly, in scanone and scantwo. I should have written this down some time ago, as this bug has been known for a long time. [Fixed in version 0.95, except for EM in scantwo. For now, we just use Haley-Knott regression in place of EM, if there is X chromosome data and multiple sexes and/or cross directions.] Version 0.88 bugs: 1. In the windows version, Rprintf in the C code only print after all of the calculations are complete. We should flush the print buffer after each line. [Not really a bug; the issue with Rgui in Windows could be fixed by simply unchecking the "buffered output" option in "Misc" on the menu bar.] 2. scantwo.mr gives really bad results for the hypertension data. Lots of lod scores are extremely large. [Fixed in version 0.89-9.] >> 3. scantwo with method EM can give interactive LOD scores < 0. This is a multiple modes problem. In my experience so far, this only occurs at uninteresting loci, and so probably is not a big deal. It'd be best to have some automatic selection of multiple starting points, at least as an option. Version 0.87 bugs: 1. Problem with subset.cross when one pulls out just one individual: cross$geno[[1]]$data is a vector rather than a matrix. Look at $prob, $argmax, $draws, $errorlod, too. Similar problem for more than 1 individual if the $draws argument has one one simulation replicate. [Fixed in version 0.88-12.] 2. plot.scanone puts the chromosome numbers in the wrong place. Something to do with the argument gap. [Fixed in version 0.88-12.] 3. There is apparently a problem reading in data when a chromosome has only one marker. This suggests a study of *all* functions to ensure that they work in the unusual case of a single marker. [Fixed in version 0.88-12.] 4. sim.cross.f2 wasn't capturing an X chromosome in the input map appropriately. [Fixed in version 0.88-11.] Version 0.86 bugs: 1. I suspect that there is a bug in sim.geno. I have no evidence, to support this, but I'll be doing my best to verify that it is working correctly. [There isn't a bug in sim.geno, as far as I can see. The problem I'd noticed was in the imputation method in scanone. The return value was mean{L}+var{L}/2, but should be mean{L}+log(10)*var{L}/2. This is fixed in version 0.86-12.] Version 0.85 bugs: 1. There really was a bug in argmax.geno! [Fixed in version 0.86.] 2. scanone.perm is wrong: I was doing independent permutations for each chromosome and then maximizing across chromosomes. I've scrapped the C code and am now just doing the simple thing: repeated calls of the R function scanone. [Fixed in version 0.86.] 3. There's a problem with the convergence criteria in vbscan: The function is faster with tol=1e-8 than with tol=1e-5. [Fixed in version 0.86.] Version 0.84 bugs: 1. The "hyper" data has some markers out of order. [Fixed in version 0.85] Version 0.83 bugs: >> 1. In scanone, when there is a spike in the phenotype, sometimes the LOD curve has huge peaks and sometimes not. I believe this is due to multiple modes in the likelihood surface, and with floating point errors, sometimes you go to one mode and sometimes another. (I've seen the same problem in vbscan as well.) 2. There seems to be an error in summary.scanone when there is only one chromosome. Also, if there is more than one location sharing the maximum LOD, it returns all of them. [Fixed in version 0.85.] Version 0.81 bugs: 1. I often getting the following warning message when reading data with read.cross in "karl" format: "no finite arguments to min/max; returning extreme. in: max(..., na.rm = na.rm)" [But who ever uses the "karl" format? I've not fixed this, but it's not worth emphasizing.] 2. read.cross.karl can give an error when it's trying to determine the number (and thus autosomal/sex-linked status) of each chromosome using the marker names, if the marker names are not of the usual mouse form. [I believe this is fixed in version 0.84.] 3. Regarding the function read.cross.mm (for reading data in mapmaker format): a. It gives an error when the map file has *'s in front of each of the marker names. [Fixed in version 0.82.] b. It leaves 0's where there is missing data; these should be NAs. [Fixed in version 0.82.] c. It fails to assign the correct chr type ("A" vs "X") to each chromosome. [Fixed in version 0.82.] d. It doesn't give marker names to the columns in the data matrices. This causes major problems in argmax.geno. [Fixed in version 0.82.] 4. summary.cross should ensure that there are marker names in the appropriate places. [Fixed in version 0.82.] Version 0.80 bugs: 1. Slight bug in replace.map for 4way crosses. [Fixed in version 0.81.] Version 0.78 bugs: 1. There is a bug in argmax.geno, in the case step > 0. I saw some data like 2-2-2-2-2-1, where with error.prob=0.01, it gave argmax=2-2-2-2-2-2 when step=1 but not when step=0. [This isn't really a bug, but rather a sad truth in result of the Viterbi algorithm. The current code does, however, incorrectly chose among the most likely sequences, if there are several possible such.] [NOTE: See version 0.85 above; there really was a bug, though Viterbi is still not to be trusted in the presence of appreciable missing data when error.prob > 0.] version 0.77 bugs: 1. In create.map (used in calc.genoprob), there's a "names" problem (resulting in an error) when the markers are equally-spaced and the "step" argument is at exactly that spacing. [Fixed in version 0.78.] version 0.76 bugs: 1. In sim.cross, there's a problem with the dimnames for the error indicator component, when simulating genotyping errors with a QTL present. [Fixed in version 0.77.] ---------------------------------------------------------------------- end of BUGS.txt qtl/inst/CITATION0000644000175100001440000000121412566656241013215 0ustar hornikuserscitHeader("To cite R/qtl in publications use:") citEntry(entry="article", title = "R/qtl: {QTL} mapping in experimental crosses", author = personList(person(c("Karl", "W."), "Broman"), person("Hao", "Wu"), person("Saunak", "Sen"), person(c("Gary", "A."), "Churchill")), journal = "Bioinformatics", year = 2003, volume = 19, pages = "889-890", key = "Broman2003", textVersion = paste("Broman et al. (2003) R/qtl: QTL mapping in", "experimental crosses. Bioinformatics 19:889-890") ) qtl/inst/TODO.txt0000644000175100001440000002711112565362110013356 0ustar hornikusers"To do" list for R/qtl ---------------------------------------------------------------------- This file is intended to contain a list of many of the additions and revisions that are planned for the R/qtl package. If you any additions or revisions to suggest, please send an email to Karl Broman, . ---------------------------------------------------------------------- SHORT TERM: o Write a function to simplify formula like y~Q1*Q2*Q3*Q4 ...rather than halt with an error, just drop all but the pairwise interactions. o functions as.scanone (for converting matrix or data frame to scanone format) and as.scanoneperm (for converting matrix or two to scanoneperm format) o Bug in mqmscan: it has problems when there are just two markers on a chromosome. It gives a seg fault if you scan just that chromosome, and it gives Infs in the "info" column in the results if you scan that and other chromosomes. o Include genetic map as an attribute in scanone results and use that to plot marker positions. o effectplot and effectscan with method="hk" as well as "imp" o calc.penalties could take the result of summary.scanone in place of permutation results. o mapmaker format: if genetic map file is missing, create a bogus one. o Fix documentation for fitqtl: estimated effects work except such and such cases (X chromosome, and 4-way crosses). Might not work right for all other cases, but if that's true we should fix it. o Revise rqtltour and rqtltour2 to emphasize the use of RStudio. Suggest using download.file() and then loading with File -> Open rather than using url.show() o calc.genoprob: calculate genotype probabilities at a pre-specified map o Add pheno.col type arguments to all of the MQM functions o Bug in scantwo: occasionally lod scores not matching what I get from lm(); see example data from Quoc Tran o Scantwo permutations: currently interaction LOD is obtained by taking max(full) - max(add) within chromosome pair and then maximizing across chr pairs. It should be max(full) overall - max(add) overall. The latter will be a bit smaller than the former o GMendel-type option for bootstrap to assess marker order for closely spaced markers. o CIM-type analysis with proper treatment of missing data, using fitqtl o h^2 due to QTL in results of scanone o Add argument to refineqtl to scan full chromosomes o Separate function to get LOD profiles from a fixed model, not necessarily the max? (Or can we use refineqtl for this, but with no iterations?) o stepwiseqtl: Need a trap for the case that LODs are all NA o plot.scanone might take LOD score column *names* (as pheno.col does) o Bug in replacemap.cross in case of sex-specific map and genoprob/draws maps that need interpolation o Bugs in summary.scanone in the case of multiple phenotypes and multiple thresholds for formats other than allpheno and allpeaks. I get warnings like In lod[, i] > threshold : longer object length is not a multiple of shorter object length Also, I need to use as.numeric() on the thresholds o Use library(parallel) with orderMarkers o beeswarm package for plot.pxg o In sim.cross, to get a QTL on the X chromosome, you need to use chr=20 rather than chr="X". o Function to calculate recombination fraction and LOD score for just adjacent markers. o geno.crosstab seems unnecessarily slow o Function to get no. qtl in a QTL object; also no. interactions (via the formula) o Look at possible global options to see if any might interfere with things. o Saunak reported a problem in summary.scanone: Some time in the last month, the summary.scanone function does not work (for one data set) when I ask it for p-values with pvalues=TRUE. It works fine when I don't ask for the p-values. ------------------------------------------ Error in calcPermPval(peaks, perms) : NA/NaN/Inf in foreign function call (arg 4) ------------------------------------------- o checkcovar gives messed up column numbers re "Following phenotypes are not numeric" o Column names in output of addpair summary (particularly regarding the 6x15 and 7x15 scans in the hyper demo I did). o in stepwiseqtl and refineqtl: include an argument that is the distance between qtl (or number of markers between qtl) and somehow prevent qtl from getting too close [to avoid artifacts] o Bug in plot.scantwo: contours=TRUE gives warning: In any(contours) : coercing argument of type 'double' to logical Also: maybe change the color of the contour. o max.scantwo could take a chr argument o In sim.cross, do the qtl get sorted by genomic position? And then do the qtlgeno need to be reordered back? o Effect plot on X chromosome: be sure that males and females get split properly. o replacemap when you have calc.genoprob results but from step=0; seems to give an error o Revise calc.errorlod to do things in parallel (by chromosome or individual? ... I'd guess in batches of individuals) o Use format.pval in printing pvalues, so they're never strictly 0. o In c.cross, include an argument for "flipping" a backcross, so that a backcross to (AxB)xB can be combined with an intercross o implement the "controlAcrossCol" argument within summary.scanone, to calculate p-values? o qtl x covar interactions in stepwiseqtl o scantwo: ability to scan just specific chromosome pairs o special treatment of X chromosome in scantwo permutations and in stepwiseqtl ---------------------------------------------------------------------- MEDIUM-SHORT TERM: o write version of formLinkageGroups for the case of very large numbers of markers. Rather than calculate all pairwise rf in advance, do that for one marker against all others, as we go along. o cim(): Haley-Knott-based approach to deal with missing genotypes at marker covariates. o cim(): rather than forward selection to a fixed number of markers, do forward selection until either a marker is not significant or the fixed maximum. o get scanone and scantwo to work with a single marker on a chromosome. o function for getting x axis location in scantwo picture (like the new xaxisloc.scanone) o In mqmsetcofactors(), we might allow cofactors() to be marker names rather than just marker indices. o In the various MQM functions, pheno.col might be a phenotype name rather than just numeric index. Also, it might be a vector of phenotype values. (Both, as in scanone etc.) o Better way to make use of ... in plot functions that allows unmentioned defaults. o method for having alternative genetic maps and for storing a physical map; linear interpolation for plotting scanone output in Mbp. o make various functions work appropriately with class "special" (eg, plot and summary) o Fix convergence problems in scanone with method="ehk", especially in the case of covariates and interactions. o Plotting scanone results for *many* phenotypes as an image plot. (Perhaps threshold the really high LODs and eliminate curves that fail to meet a given threshold.) o Allow plot.geno (or other cases in which subset.cross is used to pull out individuals) to refer to individual IDs. o Go through all of the various plot functions and make sure that the x- and y-axis labels are created with axis() rather than text() and segments(). This way, the size of the labels can be modified with par(cex.axis) o Finish off the work to get coefficient estimates by imputation in fitqtl for the X chromosome in BC and F2. o Revise c.cross so that you can combine crosses even if there are different numbers of chromosomes o 2 traits vs genotype at one QTL (with regression lines) o Documentation on RI lines. ---------------------------------------------------------------------- MEDIUM TERM: o eQTL-related stuff: - Mb positions within cross object; multiple genetic maps - Positions in phenotype object + other annotations o For the stepwiseqtl function: - should be able to save all models with plod within some value of the best model - QTL x covariate interactions in countterms - it'd be nice to be able to have all models that are one term away from the optimal model (maybe this could be a separate function) - it'd be nice to be able to easily make a plot of the final model plus information about all models that are one term away o Revise plot.rf so that it can have different color schemes, as in plot.scantwo. Allow a zscale. o Simpler methods to get at interesting bits in the est.rf results. o cM coordinates in scantwo plot for multiple chromosomes: Fix plot.scantwo for the case that incl.markers=TRUE, so that positions are not equally spaced, but are according to the genetic map. (This is working if just one chromosome is plotted, but should be made to work generally.) o scanone (or is it scantwo?) with method="hk": Major memory problems in the permutations with multiple phenotypes. o X chromosome for 4- and 8-way RIL by sibling mating (also 2-way RIL). o pull all of the genoprob and imputation information out of the qtl objects and replace it with indices to the information in the cross object. (This stuff takes up too much space!) o lodint/bayesint as option to summary.scanone. o conditional LOD scores from scantwo output. o scanqtl: if formula symmetric w/ respect to two QTL that are on the same chromosome, only scan the triangle (rather than the square) o write tools for converting the output from scanqtl() to the format for scanone() or scantwo(), according to whether it's a 1-d or 2-d search, or print a warning otherwise. o pairprob at markers + putative QTL? For linked loci in scanqtl by HK/EM. o Include Bjarke's code on eHK for scantwo. o revise read.cross with format="mm" to deal with ril by selfing and/or sib mating o read.cross for "qtx" sometimes doesn't seem to take the genotype pattern appropriately; read in a backcross as if it were an F2. o An NA in the mapmaker data file caused an error in read.cross; the line became too long. Maybe this is true whenever an item doesn't match what is expected. o Speed up read.cross.mm; deliver meaningful errors if map/genotypes don't match, and if too many genotypes in a row. o scanone with additive alleles at QTL o Pull out results for an interval from scanone. o Add additional HMM functions for the X chr in RIL, as the marginal genotype distribution is 2:1 rather than 1:1 and the transition matrix is not symmetric Pr(BB|AA) = 2r/(1+4r) and Pr(AA|BB) = 4r/(1+4r) o Add appropriate functions to analyze advanced intercrosses (AILs) and advanced backcross (BCn). o Allow phenotypes on multiple individuals (esp for recombinant inbred lines). ---------------------------------------------------------------------- LONG TERM: o covariates with 2part model in scanone o X chromosome in cim(). o Analysis of censored phenotypes. o Analysis of residuals. o Imprinting/parent-of-origin effects. o Treating a covariate as a random effect. o Multiple phenotypes (esp. regarding pleiotropy). o Take the fit of the null model outside of the C code for the imputation method in scanone and scantwo, so that it only has to be done once (rather than for each chr or chr pair). o Generalized linear models in scanone and scantwo. o Incorporate code from Brian Yandell, Fei Zou and Amy Jin on semi-parametric QTL mapping methods. o Analysis functions such as scanone and scantwo might assign an attribute to their output which identifies the input data and/or function call. o Re-write the C code for EM underneath scanone and scantwo so that it is not so tedious. ---------------------------------------------------------------------- end of TODO.txt qtl/inst/STATUS.txt0000644000175100001440000043766112566656241013667 0ustar hornikusersRevision history for the R/qtl package ---------------------------------------------------------------------- copyright (c) 2001-2015, Karl W Broman http://www.rqtl.org The R/qtl package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation. This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details. A copy of the GNU General Public License, version 3, is available at http://www.r-project.org/Licenses/GPL-3 ---------------------------------------------------------------------- Version 1.37, 2015-08-24 Major changes: None. Minor changes: In read.cross with format="csv", "mm", or "tidy", don't let it reorder the chromosomes (which it would do if there were chromosomes named other than numbers < 1000, "X", or "x"). drop.markers now gives an error if you try to drop *all* of the markers. If cross object contains no genotype data, totmar() and nmar() now give more meaningful errors. Fixed a bug in scantwo and scantwopermhk in which an error would occur if reduce2grid had been called and assumeCondIndep=FALSE. Now forcing assumeCondIndep=TRUE in this case. Fixed a bug in plot.pxg, in the case that not all possible genotypes were observed at a marker. Fixed a bug in stepwiseqtl, where if covar is not a data frame, they don't get considered in the model. Fixed a bug in fill.geno for method="maxmarginal" (wasn't putting NAs in genotypes with probability < min.prob) Fixed a bug in refineqtl that arises when multiple QTL are at exactly the same position, which can arise in stepwiseqtl. Version 1.36, 2015-03-05 Major changes: None. Minor changes: Added a function flip.order() for flipping the order of markers on selected chromosomes. Added scanonevar.meanperm and scanonevar.varperm (from Robert Corty) for permutation tests with scanonevar(). Revised plotPheno (aka plot.pheno) so that one can control the x-axis label and title (also, in a histogram, the breaks). plotPXG: if infer=FALSE and there are no fully-informative genotypes (e.g., in a 4-way cross), give a more informative error. geno.image: allow control of x- and y-axis labels; allow suppression of axes. Removed some warnings about missing end-of-line characters, in read.cross with MapQTL format. Fixed a bug in scanonevar; was failing with an error about coercing class "A" to a data.frame Dropped the name summary.scantwo.old(); still available as summaryScantwoOld(). Version 1.35, 2014-12-15: Major changes: None. Minor changes: Fix an important bug in summary.cross. Change a couple of abs() to fabs() in C code. Version 1.34, 2014-10-30: Major changes: Added ability to do X-chr-specific permutations in scantwo (argument perm.Xsp, as in scanone). Separate thresholds are obtained for the regions A:A, A:X, and X:X regions, maintaining control of the overall false positive rates. Added a function scantwopermhk that just performs scantwo permutations with Haley-Knott regression; faster and with lower memory usage than scantwo. With X-chr-specific scantwo permutations, calc.penalties will give separate main effect for autosome and X chromosome, and separate interaction penalties for A:A, A:X, X:X. For A:A interactions, we still use "light" and "heavy" penalties; for A:X and X:X interactions, only the "heavy" penalty is used. These penalties may be used in stepwiseqtl for better treatment of the X chromosome in automated inference of multi-QTL models. Added scanonevar() function, for a single-QTL genome scan for QTL affecting not just the mean phenotype but also the variance. (Code from Lars Ronnegard; method in Ronnegard and Valdar Genetics 188:435-447, 2011.) Added "tidy" format to read.cross and write.cross. This separates the data into three comma-delimited files, for genotypes, phenotypes, and the marker map. Separating the data in this way allows each file to be in a simpler format. Minor changes: Add another option to fill.geno: impute using maximum marginal probability. Add function map2table (output like pull.map with as.table=TRUE, but starting with a map rather than with a cross). Fixed a bug in est_map_ri8self.c (thanks to Rohan Shah) Fixed a bug in scanone/scantwo stratified permutations in batch, with multiple phenotypes, some with missing values (thanks to John Lovell). Fixed some bugs in read.cross with format="mapqtl". Version 1.33, 2014-08-12: Major changes: Can read/write 4-way cross data in MapQTL format (thanks to Timothee Flutre). Minor changes: read.cross with format="qtlcart" can now read doubled haploids (dh/Ri0). Fixed potential problem in read.cross with format="csv" or "csvs" when there are many empty cells in the phenotype data. Fixed bug in read.cross for format="csv" that shows up in some rare cases: markers not ordered by linkage group, no positions provided, and chromosome IDs non-numeric. It was a pretty bad bug, as marker genotypes got scrambled. Fixed some memory leaks in MQM code. Version 1.32, 2014-05-28: Major changes: None. Minor changes: fitqtl with model="normal" now returns residuals as an attribute. Added an additional argument to plot.scanone, bgrect, for making the background of the plotting region a different color. Revised cleanGeno to work with any cross having two possible genotypes (i.e., not just bc but also riself, risib, dh, haploid). Revised summary.cross so that overall genotype frequencies are given separately for autosomes and the X chromosome. Fixed typo in a warning in add.threshold. Fixed a bug in reduce2grid, regarding format of attributes Fixed a bug in MQM: in some circumstances, the last marker was always included as cofactor; other cleanup in MQM code. Version 1.31, 3/19/2014: Major changes: Added a function reduce2grid() for subsetting the genotype probabilities (from calc.genoprob) or imputations (from sim.geno) to the evenly spaced grid of pseudomarkers, for use in the case of very dense markers when it is too computationally intensive to perform a genome scan at both the markers and the grid of pseudomarkers. Minor changes: Fixed a problem in write.cross with format="csv" (for cross types other than BC and F2, it would use incorrect genotype codes if there was no "allele" attribute for the cross). Add a couple of arguments to plotLodProfile: showallchr, to show all chromosomes (and not just those with QTL), and textsep, to control the separation between the QTL labels and the LOD curves. Fix a bug in bcsft.c, regarding potentially over-running an array Fix problem with plotLodProfile when it's maximized at multiple locations. Fix problem in refineqtl and stepwiseqtl; map attribute in qtl object would get unintentionally subsetted if the qtl object needed to be re-created. In addqtl, addint, and addcovarint, have require.fullrank=FALSE be the default; require.fullrank=TRUE remains the default in stepwiseqtl. Fixed bug in which summary.scantwo was re-ordering the chromosome factor levels. Version 1.30, 2/10/2014: Major changes: Revised parallel code (in scanone, scantwo, mqmpermutation, and mqmscanall) to use a different function for Windows, as mclapply() doesn't work there. Minor changes: Fixed problem in formLinkageGroups when used with results of markerlrt(): no recombination fractions so use max.rf=Inf Added arguments type, cex, pch, and bg to plot.scanone, to be passed to lines() in making the plot. Thus, you can use type="p" to get points only. Fixed a bug in scanqtl that showed up if there were missing phenotypes and no covariates. Version 1.29, 12/9/2013: Major changes: None. Minor changes: Slight change to summary.qtl to deal with QTL objects with no QTL. For the QTLRel package, now "export" reviseXdata() in NAMESPACE. Version 1.28, 9/23/2013: Major changes: Added cross type "haploid". Like backcross ("bc") or doubled haploids ("dh") but with genotype labels like "A" and "B" instead of "AA" and "AB"/"BB". Added crosstype argument to read.cross, to force a particular cross type (such as "riself"). For parallel processing, replaced reliance on the snow library with the use of the parallel library. Minor changes: Added formMarkerCovar(), to facilitate use of markers as covariates in QTL analysis. Added function addmarker() for adding genotypes for a marker to a cross object. Added function nqtl() for counting number of QTL in QTL object. Added examples to help files for plotPXG and effectplot on getting the output. Slight change to way to handle random number generation for cluster-based computing. Fix bug in fitqtl-link functions for model="binary" (re matrix rank) Fixed write.cross to allow use for BCsFt crosses. Fixed an out-of-bounds error in the C++ code mqmscan.cpp. In stepwiseqtl with verbose=FALSE, the initial LOD score is no longer printed. Version 1.27, 4/11/2013: Major changes: Implemented HMM algorithms for advanced backcross/intercross. Minor changes: Improvement in addqtl, addint, scanqtl, stepwiseqtl to better handle collinearity in the design matrix that could give spurious evidence for QTL in large QTL models. This can still be a problem with addpair (and stepwiseqtl with scan.pairs=TRUE). Slight change to help file for read.cross, to be more explicit about the "csvs" format. Fixed a bug in read.cross for format="qtlcart" with RIL data. Made cross type in "qtlcart" files case insensitive in read.cross. For example, any of Ri1, RI1, rI1, or ri1 will be treated the same. Version 1.26, 11/27/2012: Major changes: We changed the treatment of the ind argument in subset.cross to avoid problems that occurred when the cross contained numeric individual IDs. Now, we match the values in ind against the individual IDs only if ind values are character strings. If the ind values are numeric, we treat them as numeric indices and they are not compared against the individual IDs. Minor changes: In plot.rfmatrix, include marker name in title (unless main is provided as an argument). Add warning message to find.marker if there's no match for a given chromosome name. Slight change in find.markerpos(), to speed it up. Slight change to locateXO to save genotypes to left and right of each crossover. Small addition to the "A shorter tutorial of R/qtl" (rqtltour2.pdf). Fixed slight bug in summary.scanone for format="tabByChr" or format="tabByCol". Fixed bug in fitqtl for case that individuals have missing phenotypes or covariates and there's a QTL on the X chromosome Fixed bug in effectplot, regarding coding of genotypes on the X chromosome. Fixed bug in mqmscan for case when estimate.map=TRUE but plot=FALSE. Fixed bug in c.cross for case that there are different sets of markers. Fixed bug in xaxisloc.scanone for case that chromosomes don't start at 0. Fixed bug in locateXO; gave core dump if there was just one marker on the chromosome. Fixed bug in scanone and scantwo for case that weights are used but there are individuals with missing phenotype; the weights weren't being subsetted appropriately. Revised example help file for multitrait data to use mqmscanall rather than scanall, since the latter function has no help file. scanPhyloQTL now gives warning if there are different marker maps. Fixed bug in mqmaugment; with one phenotype, its name was getting changed to "pheno". Version 1.25, 8/13/2012: Major changes: None. Minor changes: Revised write.cross to output data in "qtab" format; see https://github.com/qtlHD/qtlHD/blob/master/doc/input/qtab.md Revised summary.scanone to be more consistent about only picking one row per chromosome even when there are multiple positions sharing the maximum LOD on that chromosome. Fixed bug in stepwiseqtl; backward deletion steps were not dealing with the drop-one-qtl results from fitqtl appropriately. Revised fitqtl to include formulas and lod scores as attribute in drop-one-qtl analysis. Fixed a bug in fitqtl regarding the adjustment for the X chromosome. The problem shows up in stepwiseqtl; if X chr enters model and is then removed, the covariates adjusting for sex and pgm continue to be used. Added argument to refineqtl, fitqtl, scanqtl, addqtl, addpair, to force X-related covariates into model. In stepwiseqtl, include penalties as attribute in the output. Slight change to checkcovar(): omit individuals with phenotypes/covariates that are +/- Inf, as well as those that are missing. Handle missing values in mf.stahl and imf.stahl. Fixed rare bug in fitqtl regarding X chr loci with interactions. Fixed bug in sim.cross for type="bc" that resulted in loss of "X" chromosome type. Speed up some of the examples in the help file, so that R CMD check doesn't take so long. Version 1.24, 5/25/2012: Major changes: Fixed a major bug in checkcovar, used by scanone and scantwo to omit individuals with missing phenotypes. If there is an "ID" column that is numeric, the wrong individuals could be omitted, and genotypes and phenotypes would be misaligned. Changed the names of a number of functions, in order to avoid the "Note" in R CMD check, and because Prof. Brian Ripley asked me to. plot.map -> plotMap plot.missing -> plotMissing plot.errorlod -> plotErrorlod plot.geno -> plotGeno plot.info -> plotInfo plot.pheno -> plotPheno plot.pxg -> plotPXG plot.rf -> plotRF summary.map -> summaryMap summary.scantwo.old -> summaryScantwoOld Revised tutorials to use the new naming scheme. Revised the emission probabilities for dominant markers in an F2, for the HMM calculations. Previously, we had Pr(O = not A | g = AA) = Pr(O = not B | g = BB) = epsilon/2 these have been changed to Pr(O = not A | g = AA) = Pr(O = not B | g = BB) = epsilon This corresponds to "not A" being "H or B"; similarly for not B. Results will change only for an intercross with dominant markers, and generally only slightly. Minor changes: Changes to the format of the output of summary.scanPhyloQTL for format="lod". The final column is now the maximum LOD score across partitions; the difference between the maximum and the second-highest is now third-to-last; the threshold argument is applied to the overall maximum rather than to that difference. Revisions to read.cross (for the csv formats) from Steffen Moeller, to give some more informative warnings/errors. Fixed a bug in scantwo permutations in case that a chromosome has multiple markers but they span < step from calc.genoprob. Fixed a bug in interpPositions; problems if the input had missing rownames. Renamed the README.txt file as INSTALL_ME.txt; added a new README.txt that provides a brief description of the package. Version 1.23, 3/6/2012: Major changes: None. Minor changes: Added functions pull.genoprob, pull.argmaxgeno, and pull.draws, to pull out those bits from a cross object as a single big matrix or array. Added a function inferFounderHap() for crudely inferring founder haplotypes in multi-parent RIL, using groups of adjacent markers. Added function nullmarkers() for identifying markers with no genotype data. Revised sim.cross so that founder genotypes are included in output, for 4- and 8-way RIL. Added HMM functions to handle a special design for MAGIC lines from BioGemma (http://www.biogemma.fr/indexuk.php). Revised subset.cross and clean.cross so that cross information in 4- and 8-way RIL don't get lost. Revised plot.pxg, effectplot and effectscan to give a more informative error if the selected phenotype is not numeric. Revised qtlversion() to use packageVersion(). Fixed bug in summary.map: class included the function data.frame; not just the character string "data.frame". Revised various utility functions to retain the "onlylod" attribute in cross$rf, if it's there. Version 1.22, 11/28/2011: Major changes: Revised plot.map to deal with a pair of maps with markers in different orders (or with some markers appearing in one map and not the other). We still require that the two maps have the same chromosomes and chromosome names (with chromosomes in the same order). Revised scantwo to allow analysis of individual chromosome pairs, and reorganized the way that scantwo permutations are done (first summarizing each chromosome pair and then overall); this should eliminate the memory problems we've had with scantwo permutations. Minor changes: Added warning to help file for fitqtl() regarding the estimated percent variance explained in the case of linked loci: the values are misleading. Fixed problem in comparecrosses() regarding Inf/-Inf phenotypes. Fixed a bug in scantwo, method="hk", for multiple phenotypes or permutation tests in batch. This showed up only when there were missing genotypes at one or both putative QTL. write.cross with format="csv" now exports genotypes as AA/BB for RIL (previously, genotypes were written as AA/AB). Fixed bug in addint() and addcovarint() Revised typingGap to have an argument 'terminal'; if TRUE, just look at the gap from the terminal markers to the first typed interior marker, giving 0 if the terminal markers are both typed. Fixed bug in calc.genoprob with stepwidth="max" in the case that no pseudomarkers are to be added. Fixed bug in geno.table for the case that X chromosome has just one observed genotype. No longer allow "" in na.strings in read.cross for csv files. Revised all calls to data.frame() and as.data.frame() to override global option of stringsAsFactors, so that we know what's going to happen. Revised scantwo so that if the 'chr' argument is a list, we do just the scans of the chr in the first component against those in the second component. Slight change in plot.info to deal with inclusion of 'main' argument. Added NAMESPACE file. Slight changes to avoid some R warning messages. Slight change to imf.cf to give more accurate results. Fixed warning message in replacemap. Fixed warning message in +.scanone. Fixed bug in mqmfind.marker. Fixed slight bugs in print.addint and print.addcovarint. Removed a bunch of unused variables from C code. Version 1.21, 3/21/2011: Major changes: None. Minor changes: Add "addchr" argument to find.pseudomarker. The default is TRUE, and returned non-marker locations have names like "c5.loc25" (as in the output of scanone). If FALSE, that initial "c5." part is left off, to return just strings like "loc25" (as in the genotype probabilities from calc.genoprob). Revise calls to rainbow() in plot.rf and plot.scantwo so that they no longer use the 'gamma' argument, which is being removed from future versions of R. Slight change to format of verbose output in est.map with m>0 (that is, under interference). Version 1.20, 2/18/2011: Major changes: Enabled est.map to use multiple processors via snow; added argument n.cluster to indicate the number of cluster nodes to use. Added the option stepwidth="max" in calc.genoprob, sim.geno, and argmax.geno. This inserts the minimal number of pseudomarkers so that the maximum step between points is as indicated by the "step" argument. Minor changes: Fixed a bug in refineqtl() that kept it from working for a 4-way cross. (The bug also broke stepwiseqtl().) Fixed a problem in the internal function dropXcol() that led to a crash in scantwo() for 4-way crosses with an X chromosome. Fixed a bug in mqmscan() regarding the chromosome names in the output. Trap cases of X chromosome for crosses other than bc/f2 in stepwiseqtl, makeqtl, addqtl, and addpair. Added a function phenames() for pulling out the names of the phenotypes. Small revisions and enhancements to some of the MQM plots. Revised subset.cross so that if the cross contains QTL genotypes (from sim.cross), these are also subsetted. Revised replacemap.cross (aka replace.map) so that it will also replace the maps in results from calc.genoprob, sim.geno and argmax.geno, using interpolation if necessary. Fixed a couple of minor bugs in mqmscan: one giving duplicate row names, another resulting in pseudomarkers outside the terminal markers even when off.end=0. Fixed bugs in scanone and scantwo regarding batch mode for model != "normal". Fixed a bug in refineqtl; it was including all possible covariates refered to in the data frame 'covar', even if they weren't referred to in the formula. Fixed a bug in plot.geno, introduced in version 1.19, that made it not work with horizontal=FALSE. Version 1.19, 11/29/2010: Major changes: Added a tutorial on genetic map construction; find it within the package at docs/geneticmaps.pdf, or (more simply, probably), find it on the web at http://www.rqtl.org/tutorials/geneticmaps.pdf Added two additional formats to summary.scanone(), "tabByCol" and "tabByChr". These produce tables of LOD peaks organized by LOD score column or by chromosome. The tables include approximate confidence intervals for QTL location (as calculated by the lodint() or bayesint() function; which one is indicated by the new argument ci.function). Here's an example: bp: chr pos ci.low ci.high lod pval c1.loc44.5 1 47.8 35.5 85.0 3.56 0.007 D4Mit164 4 29.5 18.8 30.6 8.09 0.000 sqrt: chr pos ci.low ci.high lod pval c1.loc44.5 1 47.8 35.5 84.8 3.63 0.007 D4Mit164 4 29.5 17.2 30.6 8.08 0.000 Minor changes: Revised summary.scanoneperm to include a new argument, controlAcrossCol. If TRUE, LOD thresholds will control error rate not just across the genome but also across the LOD score columns. Added function droponemarker, with the aim of identifying problematic markers by dropping one marker at a time and calculating a LOD score and a change in the estimated genetic length of the respective chromosome. Added a function pull.rf for pulling out either the estimated recombination fractions or the lod scores, as calculated by est.rf(), from a cross object. Also added a function plot.rfmatrix, for plotting a slice through these. Added a function cleanGeno for removing genotypes that are possibly in error (as indicated by apparent tight double-crossovers). Use this function with caution. Added a function typingGap, which calculates, for each individual and each chromosome, the maximum distance between typed markers. Revised MQMscan so that the output contains covariate information, to be plotted with add.cim.covar(). Revised c.scanone(), c.scanoneperm, c.scantwo, and c.scantwoperm so that the input "..." can be a list of scanone/scanoneperm/scantwo/scantwoperm objects. Revised plot.geno() so that, for the X chromosome in a backcross or intercross, the genotypes appear appropriately, with females being homozygous or heterozygous and the males hemizygous, though we plot the hemizygous genotypes as if they were homozygotes. Revised subset.scanoneperm and subset.scantwoperm so that one may pull out a subset of replicates (not just columns). Added functions [.scanoneperm and [.scantwoperm so that one can use [] to subset. Revised locateXO() so that the output contains a column with the number of typed markers between adjacent crossovers. In orderMarkers(), if verbose is numeric and > 1, even more information on the progress of the calculations is provided. Fixed a problem in subset.cross where, in the case that a cross contained numeric IDs, subsetting the individuals resulted in them being sorted according to their IDs. Added a manual page for the function getid(), which was not previously documented. (It is used internally a great deal, and it may be useful more generally.) Revised effectplot so that if mname2 or mark2 are given but mname1 and mark1 are not, the arguments get switched. Fixed a bug in shiftmap(). Added an argument, force, to reviseXdata(), to force a change in the genotypes; this is for use within plot.geno(). Slight change to top.errorlod, so that the output columns are not factors but character strings. Slight change in plot.geno() so that we use filled circles (pch=23) rather than calling points() twice for each point. Small changes to mqmplot.circle(). Subtle changes to tutorials new_multiqtl.pdf, new_summary_scanone.pdf, new_summary_scantwo.pdf; added .R files with the code for these tutorials. Fixed slight bug in getid(). Version 1.18, 8/18/2010: Major changes: None. Minor changes: Revised geno.table so that, for 4-way crosses, it gives P-values in most cases. (Previously, it just did so for fully informative markers.) Changed the default format for max.scanPhyloQTL and summary.scanPhyloQTL from format="lod" to format="postprob". Added function inferredpartitions for pulling out the inferred partitions for a specified chromosome from the output of scanPhyloQTL. Fixed a problem in ripple with method="likelihood". (The problem arose in revisions in version 1.17.) Fixed a problem in est.map that resulted in NAs. (The problem arose in revisions in version 1.17.) Slight changes to "inverse" map functions imf.k, imf.h, imf.cf, so that recombination fractions >= 0.5 return large map distances rather than NAs. Fixed a bug in summary.scanPhyloQTL so that it works when the input has just one chromosome and so the colnames remain like "AB|CD" rather than getting converted to "AB.CD". Version 1.17, 7/28/2010: Major changes: Implemented the fit of models for binary traits in fitqtl(), by Haley-Knott regression and multiple imputation. This model can also be used in refineqtl, scanqtl, addqtl, addpair, addint, stepwiseqtl, and addcovarint. Implemented Haley-Knott regression for binary traits in scanone and scantwo. In mqmscan(), replaced the arguments step.min and step.max with a single argument, off.end (to be more like scanone). Added functions for the joint analysis of multiple crosses, in order to map QTL to a phylogenetic tree. (A paper describing the methods is in preparation.) The key function is scanPhyloQTL. simPhyloQTL is used to simulate data. plot.scanPhyloQTL, max.scanPhyloQTL, and summary.scanPhyloQTL are the plot, max, and summary functions for the output from scanPhyloQTL. Minor changes: Added 'offset' argument to est.map, which defines the starting position for each chromosome. If missing, we use the starting positions that are currently present in the input cross object. Added function shiftmap, for shifting the starting points of a genetic map (in a map or cross object). Added function switchAlleles, for switching the alleles at selected markers in a cross object. (For example, in a backcross, switching AA and AB at a marker; in an intercross, exchanging AA for BB.) Revised geno.table to have an additional argument, scanone.output. If scanone.output=TRUE, the output is as produced by scanone(), so that one may use plot.scanone() to plot the results. Added the ability to have ripple() run in parallel, if the snow package is installed. The added argument n.cluster indicates the number of parallel nodes to use. This is reapply only useful with method="likelihood"; with method="countxo", it can be slower than just using one CPU. Added a function pull.markers, which is the opposite of drop.markers. Added a function drop.dupmarkers, for dropping markers with duplicate names. Added an argument 'bandcol' in plot.scanone, to specify a color for alternating bands to indicate chromosomes. The default (bandcol=NULL) is to not plot such bands. A good choice might be bandcol="gray70". Added an argument 'chr' in est.map, to estimate maps for just a subset of chromosomes. Revised replace.map (and replacemap.cross) so that the map can have just a subset of the chromosomes in the cross, in which case only the maps for that subset are replaced. Changed the default in plot.info from method="both" to method="entropy". Also, added an argument "fourwaycross", so that one can look at the missing information just for the alleles of the first parent (A vs B) or the second parent (C vs D). Finally, added an argument "include.genofreq"; if TRUE, the results will include estimated genotype frequencies at each position. Added argument 'simple' to summary.fitqtl, addint, and addcovarint; if TRUE, output includes neither p-values nor sums of squares. Added a function allchrsplits(), for testing whether to split a linkage group/chromosome into two, by calculating a LOD score for each interval comparing the full linkage group to the split into two groups at that interval. Added a function nqrank() for transforming a numeric vector into the corresponding normal quantiles. Fixed a problem in MQM code regarding negative or really large marker positions. Fixed a bug in mqmpermutation(), where it was using the wrong phenotype name in the output, if pheno.col is something other than 1. Revised est.map for 4-way crosses slightly. We'd previously randomized the map before starting EM, which seemed a bad idea. Fixed a bug in scantwo() for the case that multiple phenotype columns are considered but they have different patterns of missing data. An error occurred due to a number of stupid small mistakes. [Thanks to Ricardo Verdugo for reporting the problem.] Revised summary.cross so that if markers are at the same location, the warning message indicates which chromosomes are involved. Revised summary.scanone so that if there is one LOD score column in the output, but the permutation results have more than one, then the first column in the permutation results is used and the others are ignored (and a warning, rather than an error, is issued). Revised calc.plod() to allow penalties to be infinite. Revised read.cross with format="csv" to give an error if the 2nd row has all blanks. Revised plot.scanoneboot, plot.scanoneperm, and plot.scantwoperm so that they can use the ... argument more flexibly. [I use a scheme suggested by Brian Yandell.] Revised plot.geno so that the ... argument can include xlim and ylim. Fixed bug in convert2sa for case of chromosome with just 1 or 2 markers. Fixed bug in refineqtl that resulted in sometimes the rownames in the lod profile attribute being messed up. Revised scanone and scantwo to trap error if perm.strata is not of the correct length. Added argument 'ind.noqtl' to scanone, which indicates individuals that are assigned no QTL effect. This is for rare (largely internal) use for the case that one is combining multiple crosses. Fix bug in internal function create.map, for case of a sex-specific map with very small length. Version 1.16, 5/23/2010: Major changes: None. Minor changes: Revised fitqtl() so that estimated QTL effects in RIL or doubled haploid are as they should be (half the difference between the two homoyzogotes). Added a function rescalemap for rescaling a genetic map (as for the case that a cross object has marker positions in basepairs and one wishes to convert them to Mbp or some approximation of cM locations). Added a warning message to summary.cross for the case that there are chromosomes > 1000 cM in length (which might indicate that they're really in basepairs). Revised pull.map to have argument as.table; if as.table=TRUE the map is returned as a simple table with chromosome assignments and positions. Revised fill.geno to have a third option, method="no_dbl_XO", which fills in missing genotypes between markers with exactly the same genotype. Revised est.map so that the output with verbose=TRUE is less verbose and more informative. Changes in C++ code, to fix problems that prevented the package from being compiled on Solaris. Version 1.15, 5/2/2010: Major changes: In collaboration with Danny Arends, Pjotr Prins, and Ritsert Jansen, we have incorporated Ritsert Jansen's MQM mapping software within R/qtl. (Previously, it was available only through the commercial software package, mapqtl). See the tutorial at http://www.rqtl.org/tutorials/MQM-tour.pdf Minor changes: Added a function transformPheno for transforming one or more phenotypes in a cross object. Added a function convert.map for converting a genetic map from one map function to another. Added functions convert2riself and convert2risib, for converting a cross object to be treated as RIL by selfing or sib mating, respectively. Added function simulateMissingData, for omitting genotypes at random from a cross object. Revised write.cross so that it can handle non-numeric phenotypes. Also changed the default for the "digits" argument to NULL, so that phenotypes and map positions are not rounded. Revised read.cross to have arguments error.prob and map.function, to be used if est.map is called. Revised locateXO: it no longer shifts the first marker to position 0, and it has a new argument, full.info; if this is TRUE, the output includes not just the estimated crossover locations but also the endpoints of the intervals to which they are known to reside. Revised summary.cross so that if there are > 30 phenotypes, we don't show the percent missing phenotypes for all traits but just overall. Revised stepwiseqtl so that if additive.only=TRUE, you only need to give one penalty. Revised read.cross with format="csv" so that initial fields in 2nd row need not be completely empty, but can have white space. (This was a common problem for users importing csv files.) In plot.qtl, added argument "justdots", so that one can plot just dots at the QTL rather than arrows and QTL names, and "col", the color used to indicate the QTL. Fixed a bug in xaxisloc.scanone for the case of multiple chr/pos in the input. Fixed an apparent bug in read.cross for format="qtlcart"; need to treat negative genotype codes as missing. Fixed a bug in sim.cross for type="4way"; previously it was just using the female map for both female and male meioses. Fixed a bug in find.pseudomarkerpos in the case of sex-specific maps (as in a 4-way cross). lodint and bayesint now stop with an error if chr or qtl.index have length > 1. Before, they gave weird results and a meaningless error. In read.cross with format="csv", give a better error message if there are odd values in the marker positions. Fixed a bug in scanone/scantwo for RIL on the X chromosome; the X chromosome needs to be treated like to autosomes. We can't really deal with the sexes properly here. Revised scanone and scantwo so that, if using multiple CPUs via snow and calculations are stopped early, the cluster nodes are stopped on exit. Revised summary.cross so that, for cross type "riself", there's no warning about a chromosome named "X" having class "A". Removed some odd erroneous code from the plot.qtl function. Fixed a bug in summary.scanone that shows up in the case of multiple LOD columns with permutations and format="allpeaks". Fixed a bug in scanone permutations in the case of multiple phenotypes with missing data. Fixed a problem in effectplot that arose from an apparent bug in weighted.mean in R version 2.10.1 Version 1.14, 9/30/2009: Major changes: None. Minor changes: Slight changes to read.cross for formats "mm" and "qtlcart", regarding use of the function grep(). Version 1.13, 9/10/2009: Major changes: Fixed a bug in fitqtl regarding the case of a locus on the chromosome...the cited LOD scores for X chromosome loci were not right. Had to make slight modifications to scanqtl, addqtl, addpair, addint, addcovarint, refineqtl. Minor changes: Revised makeqtl, addtoqtl, dropfromqtl, replaceqtl, and reorderqtl so that qtl objects now include a "chrtype" component, indicating whether QTL are autosomal or for the X chromosome. Revised scanone and scantwo for multiple phenotypes with missing data so that, with method="hk" or method="imp", the phenotypes are grouped into batches with matching patterns of missing data, rather than just doing each one at a time. Added a function locateXO (formerly the internal function locate.xo) to estimate the locations of crossovers on a given chromosome. Added a function, c.scantwo (aka cbind.scantwo), for concatenating multiple scantwo results. Revised summary.map to deal with the case of a single marker on a chromosome. Revised scantwo so that phenotype names are in dimnames of the lod component of the output. Fixed bugs in read.cross with format="mm" to deal with changes to grep. [replaced grep("^*", ...) with grep("^\\*", ...)] Fixed a bug in reorderqtl; the n.gen component was not getting fixed. Fixed a bug in switch.order; results of est.rf were getting messed up. Fixed some minor issues regarding hyperlinks in help files. Version 1.12, 6/30/2009: Major changes: Added the ability to simulate RIL and multiple-strain RIL in the sim.cross function. For the case of multiple-strain RIL, one needs genotype data on the founder strains, which may be simulated with the new function simFounderSnps. The encoding of genotypes in multiple-strain RIL is quite complicated. See the help file for sim.cross. Added the ability to deal with 4- and 8-way RIL in calc.genoprob, sim.geno, argmax.geno, est.map, ripple, est.rf, tryallpositions, calc.pairprob, and calc.errorlod. Added a function, readMWril, for reading data on 4- or 8-way RIL. Fixed a minor problem in read.cross, with format="csv", in the case of many phenotypes that resulted in *really* slow data import. (To read a file with 200 individuals and 1500 phenotypes, it would take about 60 seconds and now takes about 2 seconds.) Added the ability to have scanone and scantwo permutations run in parallel, if the snow package is installed. The added argument n.cluster indicates the number of parallel nodes to use. Minor changes: Added a CITATION file; type citation("qtl") within R to get information on the citation to use in articles that make use of R/qtl. You can now subset crosses with brackets, [ ], as with a matrix with rows=chromosomes and columns=individuals. See the examples in the help file for subset.cross. Added a utility function, findDupMarkers, for identifying groups of markers with identical genotype data. (This is useful for reducing the genotype data in the case of a very high marker density.) Added a utility function, xaxisloc.scanone, for finding x-axis locations for given genomic positions in a plot of scanone results (useful for adding annotations, such as text or arrows). Added functions subset.map and `[.map` for pulling out selected chromosomes from a map object. The output of tryallpositions, for testing possible positions for a genetic marker, now has class "scanone", so that one may use plot.scanone, summary.scanone, etc. Added an argument mark.diagonal to plot.rf(), to include black lines segments around the pixels on the diagonal. This helps to separate the upper left triangle from the lower right triangle. (The default is FALSE.) In geno.crosstab, the first argument (mname1) can now be a vector with the two marker names; in this case, mname2 should be missing. Revised clean.scantwo so that, by default, positions must have at least one marker *in between* them. Added arguments n.mar (no. markers that must separate two positions) and distance (cM distance between two positions). These arguments were also added to scantwo (as clean.nmar and clean.distance). Revised summary.scanone and summary.scantwo so that the perms argument can contain a single column of permutation results, in which case they are assumed to apply to any LOD score columns. Revised summary.scanone so that the perms argument can be scantwo permutations results; added an internal utility function, scantwoperm2scanoneperm, for pulling the scanone permutations out of the scantwo permutations. In plots of output from addpair with a special formula, plot.scantwo now gives just one set of numbers on the color scale. Fixed a bug in plot.scanone that shows up if the "chr" column is not a factor. Now we convert the column to a factor in advance. Fixed a bug in stepwiseqtl in the case that the inferred model contains no QTL; deparseQTLformula needs to deal with the NULL case. Fixed a bug in c.cross regarding "map" attributes in $prob or $draws. Fixed a bug in cim() [reported by Sandy Taylor] that occurred in the case of multiple marker covariates within a window (and >3 marker covariates on that chromosome. Fixed a bug in summary.scantwo in case that scanoneX component is numeric(0) and not NULL, which resulted in a major crash. Revised +.scantwo and -.scantwo so that if the scanoneX component in the input is NULL, the output has scanoneX that is NULL. Fixed a bug in addpair() in the case that the user gives a formula that includes one but not both of the new QTL. Revised ripple() to give a warning message if the chr argument is not provided. In compareorder(), switch.order(), and ripple(), changed the default value for the tol argument to 1e-6 (as in est.map). Slight change in compareorder() so that the order argument can be of length n.mar+2, but with only the first n.mar items considered. Slight change in checks of chr argument in ripple and switch.order; added a function testchr for checking that a chromosome argument is okay. In the output of c.cross, there is a numeric phenotype "cross" that indicates which individuals are from which cross, as a single column. Revised fixXgeno.f2 so that warnings are given the appropriate allele labels (if they are provided to read.cross). Fixed a few problems in c.cross regarding the attributes to QTL genotype probabilities and imputated genotypes Added a function chrnames, for pulling out the chromosome names from a cross. Revised the software license to the GNU General Public License, version 3. Minor change in plot.cross so that if one types plot(mycross, mymap) it is shipped to plot.map rather than giving an error message. Revised the R/qtl tutorial to refer specifically to the GPL v3. Version 1.11, 3/29/2009: Major changes: Revised effectplot so that one may refer to "pseudomarkers" by their chromosome and position with a construction like "5@32.8", rather than having to first call find.pseudomarker to get the name. Also, in the actual plot, we use the form "5@32.8" rather than, for example, "c5.loc47". Minor changes: Revised the software license statements throughout the source code (and above), for clarity and consistency. Revised write.cross so that it may write doubled haploid (type "dh") data. Fixed a problem with movemarker regarding the treatment of chromsome names. Slight change to makeqtl so that QTL names of the form "5@30.0" have ending 0's left in, if appropriate (so that if one QTL is referred to as "1@12.23", then another like "5@30" will be given as "5@30.00", so that all have equal precision. Added a function find.pseudomarkerpos for finding the position corresponding to a "pseudomarker" name (similar to find.markerpos). Added an internal function charround() for rounding numbers, turning them into character strings with ending 0's preserved. Fixed a slight bug in lodint() regarding the case of multiple positions sharing the maximum LOD score. In dropfromqtl, addtoqtl, replaceqtl, and reorderqtl, attributes "formula" and "pLOD" are now stripped. In replaceqtl, changed the argument "indextodrop" to just "index", as in dropfromqtl. Added a more clear error message in plot.map in the case that a sex-specific map and a sex-averaged map are input. In plot.info and plot.geno, we now allow one to use main as an argument for producing a self-defined title. Slight change in plot.qtl, regarding placement of text and size of arrows. In summary.fitqtl, print.addint, and print.addcovarint, eliminated an extraneous blank line after the model formula. Added code from Pjotr Prins enabling R/qtl to be linked against Perl and Ruby, as part of biolib. Version 1.10, 1/11/2009: Major changes: Revised the way that the 'chr' argument is treated in functions such as scanone, scantwo, etc., to give greater consitency. Numbers are interpreted as character strings to be matched to the chromosome names. Negative numbers and character strings that start with "-" are interpreted as omitting the corresponding chromosomes, matched by name. One may also use a logical vector (TRUE/FALSE), of the same length as there are chromosomes, indicating which chromosomes are to be considered. So if an object has three chromosomes named "1", "3", "4", using chr=2 will result in an error, while chr=3 will give the second chromosome (named "3"). Also revised the way that the 'ind' argument is treated in subset.cross and plot.geno, in the case that the input cross contains individual identifiers in the phenotype data. The 'ind' argument can still be a logical vector, but otherwise we first seek to match the values against individual identifiers. For identifiers that are character strings, one may use "-" at the beginning of each to indicate all individuals except those given. Added an argument 'batchsize' to scanone and scantwo, so that in the case that multiple phenotypes (or permutations) are to be run as a batch (with method "hk" or "imp"), they can be run in smaller batches (indicated by batchsize). This can speed things up quite a bit in the case of a very large number of phenotypes (or permutations). Added two functions for the de novo construction of a genetic map. formLinkageGroups uses pairwise marker linkage information (calculated with est.rf) to partition markers into linkage groups. orderMarkers uses a quick but not very good algorithm for ordering the markers on a chromosome (minimizing the number of obligate crossovers). Added a function addcovarint, which is similar to addint, but adds one QTL x covariate interaction at a time. Minor changes: Created functions replacemap.scanone and replacemap.scantwo, which enable one to plot scanone or scantwo results relative to another map (with positions interpolated based on marker locations). These can be used, for example, so that one may plot scanone or scantwo results relative to a physical map. Added a function replacemap.cross, which is the same as the long extant function replace.map. This was so that I could the functions replacemap.scanone and replacemap.scantwo (see above). All can be used with the 'generic' function replacemap. Revised the functions nchr, nmar, totmar, so that they work for map objects as well as cross objects. Separated the help files for nind, nmar, totmar, nphe, nchr from the summary.cross help file, so that the use of the revised functions can be explained. Revised the way in which LOD score columns are renamed in c.scanone and cbind.scanoneperm. If labels are given, these are appended to the end of the names, but if labels are not given and there are no repeats in the column names, the column names are left as they were. Added functions subset.scanoneperm and subset.scantwoperm for pulling out selected LOD columns in the case that permutation tests were run with multiple phenotypes. Revised calc.penalties so that it can deal with the case of scantwo permutations for multiple phenotypes: Added an argument "lodcolumn" for selecting the phenotype; if missing, penalties for all phenotype are calculated. Added a function markernames for pulling the marker names out of a cross object (as one long vector). summary.cross now checks for duplicate chromosome names and chromosome names that start with '-', either of which would cause major problems. Revised summary.cross so that, if the phenotype data are missing column names, an error is given. Revised est.map so that the chromosomes are given classes "A" or "X" according to the chromosome types in the input cross object. Revised geno.crosstab to deal with partially informative genotypes in an intercross or 4-way cross. Added an argument so that (by default) columns and rows with no data will not be printed. Revised comparegeno to have the option what="both", through which the result has the proportion of matches in the lower triangle and the number of matches in the upper triangle. Also, now if what="proportion" the diagonal has all missing values (rather than all 1's); otherwise the diagonal contains the number of typed markers for each individual. Revised movemarker so that one can move a marker onto a totally new chromosome. Revised sim.cross so that if the input map object has no chromosome names, the chromosomes in the output cross object still have names. Revised summary.cross to give a warning if the chromosomes are not named. Added a function convert2sa for converting a sex-specific map object to a sex-averaged map object by pulling out the female marker locations (and issuing a warning if the female and male locations are very different). This is useful for plotting a simpler version of a map estimated for a 4-way cross via est.map with sex.sp=FALSE. Slight change to countqtlterms(), used by stepwiseqtl(), to skip the parsing of interactions in the case that there are 0 or 1 interactions; this might speed things up slightly. Fixed a slight with plotModel; the lines indicating interactions were a bit askew. Slight changes in ripple and bayesint, changing use of rev(order(.)) to order(., decreasing=TRUE) Added a more clear error message in the case that the number of individuals in the cross doesn't match the number of individuals in the QTL object in fitqtl, stepwiseqtl, addqtl, addint, addpair, refineqtl. Fixed a couple of bugs in refineqtl, one concerning convergence and the other concerning dropping individuals with missing covariates or phenotypes in the case that there is just one covariate. Fixed a slight problem in c.cross: if maps are not precisely the same, we don't try to combine the genotype probabilities. Fixed a bug in summary.scanone with format="allpeaks" for the case that there are no peaks meeting the threshold/alpha. Fixed a bug in switch.order related to the change in references to chromosomes. Version 1.09, 7/18/2008: Major changes: Added a function stepwiseqtl() for performing forward/backward selection to identify a multiple QTL model, with model choice made via a penalized LOD score, with separate penalties on main effects and interactions. The documents "Brief tour of R/qtl" and "New functions for exploring multiple-QTL models" were revised to discuss the stepwiseqtl function. calc.penalties() uses permutation results for a 2-dimensional, 2-QTL scan to derive penalties for the penalized LOD scores used by stepwiseqtl(). plotModel() is a new function for creating a simple graphical representation of a QTL model. Added an additional cross type, "dh", for doubled haploids. This is treated like a backcross, though genotypes will be indicated as homozygotes. Changed a whole bunch of functions very slightly to accommodate this. The argument pheno.col in many functions can now be a vector of numeric phenotypes. This could be useful for studying the results with various transformations of a phenotype. The vector has to be numeric, has to have the length equal to the number of individuals in the cross, and has to contain either non-integers or values outside the range 1,2,3,...n.phe. (The revised functions are scanone, scantwo, addqtl, addint, addpair, cim, effectplot, fitqtl, plot.pxg, plot.pheno, refineqtl, scanoneboot, scanqtl, stepwiseqtl.) lodint and bayesint were revised to accept qtl objects output by refineqtl (with keeplodprofile=TRUE). An additional argument, qtl.index, was added to indicate for which QTL (within such qtl objects) the approximate confidence intervals should be derived. For scanone output, the functions were modified so that, if the results concern just a single chromosome, the chromosome argument is not needed. In refineqtl, the default for the argument keeplodprofile is now TRUE. The LOD profiles contained in the output of refineqtl are now of class "scanone". Minor changes: Covariates (argument covar) in fitqtl, scanqtl, addqtl, addpair, addint, and refineqtl can now be a numeric matrix (with column names), and not just a data.frame. Permutation results obtained via scantwo now include the maximum LOD score from a single-QTL scan. This is not used in summary.scantwo, but is included for completeness. Added an argument 'pvalues' to summary.fitqtl and addint; if FALSE, the pvalues are not displayed. Added a function ntyped() which is just like nmissing() except it gives the opposite thing (no. genotypes per individual or marker). est.rf, for a backcross, had been replacing estimated rec frac > 0.5 with 0.5. This is no longer done. The print and summary function for QTL objects now will print the formula and penalized LOD ("pLOD") if they exist as attributes. They also take into account the case of a null QTL model. Revised makeqtl so that QTL names are of the form "1@15.0" rather than "Chr1@15.0"; the "Chr" seemed gratuitous. Changed some of the examples in the help files to use pull.pheno in place of references to cross$pheno. Revised summary.cross so that it pays attention to options("width") and prints things more nicely if there are loads and loads of phenotypes or whatever. We now allow formulas in fitqtl, refineqtl, addqtl, etc., to be character string representations of formulas. (They are then converted.) Added a function cbind.scanone (which is identical to c.scanone). The output of fitqtl now includes an element "lod" containing the LOD score from the fit of the full model. In reorderqtl, if the argument neworder is not provided, the QTL are ordered by chromosome and then by position within a chromosome. We define a new class, "compactqtl", for defining the trace through model space in stepwiseqtl() (if called with the argument keeptrace=TRUE). This is similar to the class "qtl" (of QTL objects created by the makeqtl() function), but containing just chromosome IDs and positions of QTL. Fixed a bug in effectplot in the case that not all possible genotypes are observed in the imputations. Slight change in plot.scanone so that one may use xlab as an argument. Similarly changed plot.map so that one may use xlab and ylab to change the x- and y-axis labels. Similarly changed plot.scantwo so that one may use xlab and/or ylab to change the x- and y-axis labels. (In plot.scantwo, if just one of xlab or ylab is given, the other is assumed to be the same.) Slight change to summary.fitqtl and summary.addint so that very long formulas are split across multiple lines. Slight change to scanqtl to avoid going just outside the defined intervals (specifically, so that refineqtl and plotLodProfile do not go just past the flanking QTL). Slight change in plot.scantwo to avoid affecting par("mfrow") or par("mar") in the case zscale=FALSE. Also fixed a slight bug regarding "any(contours)>0" vs "any(contours>0)". Slight change in the way refineqtl determines convergence, to try to avoid unnecessary iterations. Slight change to plot.scanone so that the chr argument can be logical. Simplied code for 'cat' statements in many places; we'd used cat(paste(...)) and the call to paste wasn't needed. Fixed an error in scanqtl that arose if there are multiple markers at the same position. Now give a warning message. The title of the plot produced by plot.rf can now be modified by including the argument 'main' in the call. In plot.geno, if chr is missing, we plot the genotypes for the first chromosome. Changed the label "Position (cM)" to "Location (cM)" (just for consistency across functions). In geno.crosstab, changed the column and row labels for missing data from "NA" to "-". Made a slight change to plot.pxg regarding the locations of the genotype labels on the x-axis. Revised print.map so that it doesn't print the log likelihood attribute. Fixed a bug in addpair if the formula is missing. Fixed a bug in est.rf for 4-way crosses. Fixed a bug in c.scanoneperm, c.scanone, and c.scantwoperm regarding the "df" (degrees of freedom) attributes. Fixed a bug in read.cross with format "csv" (or "csvs" or "csvr" or "csvsr") so that dec="," can be used as an argument. Fixed a potential bug in read.cross with format "mm" for pulling out the cross type. Fixed a bug in add.threshold for the case of multiple phenotypes. Fixed a bug in scanone permutations in the case of a single covariate with some individuals with missing phenotypes and/or covariates. A more meaningful error message is given in makeqtl in the case that multiple markers are at identical positions, so that qtl locations cannot be determined. Fixed a bug in read.cross with format="csvs" or "csvsr" for the case that there are individuals with phenotypes but no genotypes. Fixed a bug in refineqtl for the case of linked QTL. (Incorrect limits for the search intervals were used.) Fixed a bug in plotLodProfile for the case of linked QTL. (LOD profiles were not placed correctly.) Slight change to the internal function reviseqtlnuminformula(), so that the input formula can be a character string. Fixed slight bug in scanone and scantwo [any(weights)<=0 changed to any(weights<=0)]. Fixed a similar bug in plot.geno [any(errors) changed to any(errors != 0)]. Version 1.08-56, 4/8/2008: Fixed a bug in scanqtl. Version 1.08-55, 4/3/2008: Major changes: For all functions taking a "pheno.col" argument (including scanone and scantwo), this argument can now be a character string indicating the name of a phenotype. (Previously, it had to be a numeric index indicating the phenotype). fitqtl now takes a cross object and pheno.col (as with scanone, scantwo and scanqtl), rather than a column of phenotypes. Implemented Haley-Knott regression for the fit of multiple-QTL models in fitqtl and scanqtl. Added functions addint, addqtl, and addpair, for exploration of multiple QTL models. addint tries adding all possible pairwise interactions, one at a time, to a multiple QTL model. addqtl scans for an additional QTL to be added to a multiple QTL model. addpair scans for an additional pair of QTL to be added to a multiple QTL model. Added functions addtoqtl, dropfromqtl, and replaceqtl, for manipulating a qtl object created by makeqtl(). Added a function refineqtl(), for getting the maximum likelihood estimates of QTL positons (as best we can) in the context of a multiple-QTL model. Added a function plotLodProfile which can create a figure with 1-dimensional LOD profiles for each QTL, in the context of a multiple QTL model, as is commonly created for multiple interval mapping. Added a function, tryallpositions(), for testing all possible positions for a given marker, keeping the order of all other markers fixed. Added a function, compareorder(), for comparing a given order of markers on a single chromosome to the current one contained within a cross object. Added some additional marker genotype codes for the phase-known 4-way cross, for a dominant marker with both parents being heterozygous: 11 = not AC, 12 = not BC, 13 = not AD, 14 = not BD. Revised est.rf, for estimating recombination fractions between all pairs of markers, so that it can give results for many of the incompletely informative markers in a 4-way cross. Minor changes: Added an additional argument (expandtomarkers) to lodint, bayesint, and summary.scanoneboot. If TRUE, the intervals provided are expanded to the nearest flanking markers. Added a function geno.crosstab for creating a cross-tabulation of the genotypes at two markers. Added a function pull.pheno for pulling out the data for a phenotype or phenotypes. Added a function countXO for counting the number of obligate crossovers for each individual across the genome or on individual chromosomes. Added a function plot.qtl, for plotting the locations of QTL in a qtl object against the genetic map. Added a function checkformula for checking the formula in fitqtl/scanqtl, to ensure that it satisfies the hierarchical structure we assume: if a term is involved in an interaction, its main effect should also be included. Added an additional argument to fitqtl, run.checks. If TRUE, we check the input formula and look for individuals with missing phenotype or covariates. This is included so that the checks are not repeated multiple times when scanqtl calls fitqtl. Added the ability to calculate joint QTL probabilities assuming conditional independence of QTL genotypes given markers genotypes. (An approximation, but it speeds up scantwo slightly, for a chr versus itself.) Added an argument assumeCondIndep to scantwo(). Added an argument "zmax" to the plot.rf function, for controlling the color scale of LOD scores. Values at zmax are red; values above zmax are thresholded at zmax. Added functions plot.scanoneperm and plot.scantwoperm for plotting histograms of the permutation results from scanone and scantwo. Added a function plot.scanoneboot, for plotting a histogram of the results of scanoneboot. scanoneboot now stops with an error if the argument pheno.col indicates multiple phenotypes. Revised find.marker to have an argument "index" which may be used in place of the "pos", to find marker names by their numeric order within a chromosome rather than by map position. In read.cross, with formats "csvs" or "csvsr", we now allow that some individuals have phenotypes but no genotypes and vice versa, and the individuals in the genotype and phenotype files are not required to be in the same order. In the getsex() function, for pulling out sex and pgm for all individuals, we now attempt to infer the status of individuals with missing information. Warnings are given. Fixed a bug in read.cross.qtx, concerning the case that genotypes are like H:B or A:B, and need to be converted to A:H. Fixed a bug in effectscan for the X chromosome in the case of an intercross with both directions but just one sex. Slight change to plot.geno() to make individual IDs shown rather than just numbers, if they are available. Slight change to effectscan, to pass the "..." argument to the plot function, so that, for example, you can use the 'main' argument to put a title on the plot. Slight changes to top.errorlod() and getid() to deal with a bug for the case that there is an "ID" phenotype column with names like "1_F1". Fixed some code in scanqtl regarding dropping individuals with missing phenotypes and/or covariates that really slowed things down. Revised the analogous code in fitqtl. Added an argument to est.map: omit.uninformative. If TRUE (which is the default, and which was previously the only option), individuals with fewer than two typed markers are omitted. This was added for use by the new function tryallpositions(). Added a function markerloglik, for calculating the log likelihood for a fixed marker. This was added for the use of the new function tryallpositions(). read.cross now prints the cross type at the end. read.cross (with format="mm" or one of the "csv" formats) gives a more explicit warning message if phenotypes are to be treated as missing. Revised summary.qtl to also indicate the number of imputations, in the case that the qtl object contains them. cim() had previously used a single column for each covariate in an intercross (that is, it assumed additivity of alleles); this is fixed: it now uses two columns. A revised forward selection algorithm for intercrosses was written, to select these pairs of columns together. Fixed a slight bug in lodint and bayesint that changed the marker names if the LOD peak was at one end of the interval. subset.scantwo will now drop X chromosome related stuff from the df attribute if the X chromosome has been omitted. Changed the default tolerance in est.rf and est.map to 10^-6 (rather than 10^-4). Increased the default maxit (maximum no. iterations) to 10000. Added an extra column in the results of summary.map, giving the maximum distance between markers on each chromosome and overall. QTL objects produced by makeqtl now include a component "altname", which will be like "Q1", "Q2", ... Changed fitqtl to look at this rather than at the column names of qtl$geno. Made a slight change to plot.scantwo regarding z-limits when zlim is missing and allow.neg=TRUE. The objects of class "scanoneperm" and "scantwoperm" now have a secondary class (either "matrix" or "list") Fixed a bug in scantwo perms for the use of the argument clean.scantwo. Fixed a bug in summary.cross in the case of invalid genotypes in a cross. Fixed a slight bug in subset.cross regarding the recombination fractions from est.rf(). Fixed slight bugs in effectplot and reviseXdata regarding the X chromosome in an intercross with both sexes and one cross direction. Fixed a bug in CIM for the case of multiple marker covariates on the same chromosome. Fixed a bug in revisecovar() regarding dropping of covariates for the X chromosome. Fixed a bug in add.threshold. Fixed a bug in movemarker for the case of sex-specific maps and with the marker being moved to the middle of a chromosome with exactly two markers. Fixed a bug in geno.table; missing genotypes weren't shown for the X chromosome. Fixed a bug in fit.stahl. Subtle modification to a few of the help pages to conform to a change in R. Changed the default for plot.geno back to a horizontal plot (horizontal=TRUE); changing it to vertical was a bad idea. Fixed a slight bug regarding F2-type markers in 4-way cross. In write.cross with the "qtlcart" format, if there is a previous file that would otherwise be overwritten, it is now moved to a file with extension ".bak" rather than ".mov". Also made some slight revisions to get things in the map file to line up. Made a minor change to plot.map, so that if the "..." contain xlim or ylim, they are used in place of the defaults. Version 1.07, 9/20/2007: Major changes: Completely rewrote the effectscan function, so that it now uses multiple imputation results and deals with the X chromosome appropriately. Fixed an important bug in fitqtl, in which incorrect results could be obtained if covariates were placed before QTL terms in the formula. Minor changes: Added an argument "alternate.chrid" to plot.scanone, plot.scantwo, plot.info, plot.missing, geno.image, plot.map, plot.cross, effectscan, plot.errorlod, and plot.rf. If TRUE, the placement of chromosome ID axis labels is alternated, so that they may be more easily distinguished. For plot.cross, alternate.chrid=TRUE has been made the default; for the other functions, FALSE is the default. In the output from makeqtl, "pos" is now the precise position of the pseudomarkers (rather than just the input values), and the QTL names reflect that (though they are rounded). replaceqtl and addqtl were similarly revised. In makeqtl, added an argument what=c("draws","prob"); we now pull out either the results of sim.geno or the results of calc.genoprob, and not both. (Only the former is needed at this point; the latter will be used once we have implemented EM/HK/eHK in fitqtl. plot.geno now works for a 4-way cross; we changed the default to be a vertical plot (ie, horizontal=FALSE). Added functions print.qtl, summary.qtl, print.summary.qtl, for getting simple information about a QTL object. Fixed slight bugs in pull.map and replace.map. Fixed a slight bug in top.errorlod, for the case that there are IDs (e.g., in cross$pheno$id) that are not numeric. Fixed a slight bug in scanqtl. Changed checks regarding the class of the input to various functions to be a bit more permissive. Fixed a potential problem (which shouldn't be realized) in reviseXdata. Version 1.06, 8/7/2007: Major changes: Revised the method for calculating genotyping error LOD scores. For each individual and each marker, the error LOD score is calculated assuming that all other genotypes for that individual on that chromosome are correct. The new procedure requires much more computation time (especially in the case of dense markers), but identifies many additional potential errors. A new argument, version, allows one to specify use of the "new" or "old" version of the error LOD score calculations. Added a function geno.image for plotting an image of the genotype data. This is much like plot.missing, but gives the genotypes in color, rather than just black/white indicating missing/not. Revised geno.table so that it gives reasonable p-values for the X chromosome and for the case of dominant markers in an intercross. Added a chr argument to obtain results for only selected chromosomes. Added a function cim() for performing composite interval mapping by one of the schemes used in QTL Cartographer: forward selection at the markers, to a fixed number of markers, followed by interval mapping using those marker as covariates, and dropping any markers within some fixed window around the position under test. The results may be plotted or summarized using the functions for output from scanone(). Also added a function add.cim.covar, for adding dots, to a plot from plot.scanone(), to indicate the selected marker covariates. Extended the code for fitting the Stahl model for crossover interference to the case of intercross data. (Modified the functions est.map and fitstahl, and the underlying C code.) Added functions scanoneboot and summary.scanoneboot, for deriving bootstrap confidence intervals for the location of a QTL, but we recommend using lodint or bayesint, instead. Minor changes: Added a function find.markerpos(), for finding the chromosome and position of a marker (or vector of markers). Added arguments 'xlab', 'ylab', and 'col' to effectplot(), so that you can override the defaults. Added a function add.threshold() for adding a significance threshold (estimated via permutation results) to a plot created by plot.scanone(). Revised plot.pxg() so that unobserved genotypes will not be displayed. This was needed for the plot of two-locus genotypes on the X chromosome. Changed the names in two of the columns in the output from the 2d permutation test, to be "fv1" and "av1" rather than "2v1.int" and "2v1.add", to correspond more closely to the names in the summary.scantwo output. Also, we now allow the argument "alphas" to be a single number, in which case it is assumed that the same significance level is to be used for all five LOD scores. Made a slight change in summary.scanone(), so that when p-values are provided, the rownames don't get lost. Revised the hyper data set slightly; changed the "alleles" attribute, to be c("B","A") rather than c("A","B"), as this was a backcross to the B strain. In scanqtl, if no a fixed model (with no scanning) is fitted, the output is now just the LOD score for that fitted model. clean.cross was dropping any "alleles" attribute; this is now fixed. Added a "lodcolumn" argument to lodint() and bayesint(). Fixed a bug in scanqtl; in two-dimensional scans, the first row of the results was wrong. Fixed a bug in c.cross concerning combining backcross and intercrosses. Fixed a bug in effectplot() regarding pseudomarkers with names like "c3.loc42.5". Fixed a bug in discan, for the case of method="mr". Fixed a bug in write.qtlcart regarding RIL. Fixed a bug in max.scanone for the case that there is more than one locus with the maximum LOD score. (In that case, we print a random locus, among those having the maximum LOD.) Revised summary.scanone so that the rule is to pick out LOD scores > (rather than >=) the threshold. Revised -.scanone, -.scanoneperm so that very small differences get set to 0. Added ability to halt calculations via Ctrl-c in many of the C routines. (Previously, you'd have to wait for an exit from the C code.) There was a bug in sim.cross() regarding the QTL effects for a backcross. Fixed a bug in drop.markers, for a 4-way cross. Fixed a bug in est.map, for a 4-way cross and the case of sex-averaged maps. Made a slight change regarding estimating rec fracs in 4-way cross (possibly immaterial). A slight change in the C code for imf_stahl. Modified locate.xo() slightly...when there is no crossover, it gives numeric(0) rather than NULL. Fixed a slight problem with the format of the degrees of freedom in scanone permutation results. Slight change in the color scheme in effectplot() Fixed a slight bug in fitstahl() that made it crash if none of m, p, and error.prob were specified. Also revised the function so that we look only for error.prob <= 0.5. Fixed a slight bug regarding sex-specific maps in the create.map() function. Slight revision in plot.geno, so that if the "ind" argument contains duplicates of individuals, only the unique individuals are plotted. Slight revisions to scanone, scantwo, and discan, so that, in the midst of permutations, just one instance of various warnings is printed. Changed the names of some of the sample data files. Revised the fitstahl function to use of the estimated map for one value of m as the starting point for the next value. This can really cut down on the required EM iterations and so speeds things up. In the "map" component of the output from scantwo, the name of the second column is now "pos" rather than "map", to correspond more closely to the scanone output (and because it is more appropriate). Fixed a bug in write.cross; in an intercross with all males and all pgm==1, all X chromosome genotypes got converted to AA. Added a few more verbose error messages in read.cross for the "csvs" format (contributed by Steffen Moller, University of Lubeck). Fixed a slight bug in scanone with model="2part" or model="binary", that showed up if one first used jittermap(). Added arguments maxit, tol and sex.sp to switch.order() Fixed a bug in movemarker() for the case of a 4-way cross. In write.cross.gary(), changed a couple of uses of 'T' and 'F' to 'TRUE' and 'FALSE', respectively. Fixed find.markerpos so that it works with a 4-way cross. Revised plot.scantwo so that the upper and lower arguments can take values "fv1" and "av1" as aliases for "cond-int" and "cond-add", respectively. Version 1.05, 11/8/2006: Major changes: Fixed a problem with permutations in scanone and scantwo: covariates weren't being permuted to match the phenotypes. Minor changes: Fixed a slight bug in scanone for model="binary". Revised write.cross for the "qtlcart" format; there was a problem in the case that there were many markers on a chromosome. Revised the C code for imf_stahl, to better deal with void pointers. Version 1.04, 10/28/2006: Major changes: R/qtl has a new web site: www.rqtl.org Revised the format for the output from scantwo. Added a function convert.scantwo for converting from the previous format to the new format. For scantwo results calculated with R/qtl version 1.03 and earlier, you'll need to use convert.scantwo to convert them to the new format in order to use the summary.scantwo and plot.scantwo functions. (In the previous format, joint and epistasis LOD scores were stored; now we store the joint LOD and the LOD from the additive QTL model. This is so that, if there is a problem with the joint model, it won't corrupt the results for the additive model.) Eliminated the 'run.scanone' argument from the scantwo() function. scanone is always run. The summaries and permutation tests require these results. plot.scantwo now has an 'upper' as well as a 'lower' argument, for complete control over what gets plotted. Completely revised the summary.scanone and summary.scantwo functions. I have written documents to explain the use of the new functions. These are distributed with the code and are also available at the R/qtl website (http://www.rqtl.org), under "Tutorials". summary.scanone: There is now a format argument, useful for the case that the scanone result contains multiple LOD score columns (for example, for multiple phenotypes). We may focus on a single LOD column (format="onepheno"), as was done before; include different rows for the peaks in each LOD column (format="allpheno"); or have one row per chrosome, containing the the position and LOD score for each the peak from each LOD column (format="allpeaks"). The function now also can take permutation results in order to automatically calculate LOD thresholds or to calculate genome-scan-adjusted p-values. summary.scantwo: This was quite radically changed. For each pair of chromosomes (including a chromosome with itself), we calculate five LOD scores: the maximum LOD for the full model (2 QTL + interaction), the maximum LOD for the additive model, the difference between these (which concerns a test of whether the two loci interact), and two LOD scores concerning 2 vs 1 QTL: the difference between the full LOD and the best single-QTL LOD for the pair of chromosomes, and the difference between the additive LOD and the best single-QTL LOD for the pair of chromosomes. This is the recommended output, indicated via the argument what="best". One may also set the 'what' argument to "full", "add", or "int". (See the help file for summary.scantwo.) The 'thresholds' argument now requires five values, or one may provide permutation results plus a set of five 'alphas' (significance levels). There is also an argument 'allpairs'; the default is TRUE, in which case all pairs of chromosomes are considered. If allpairs=FALSE, only the self-self chromosomes are considered, so that one may look more easily for cases of possible linked QTL. summary.scanone and max.scanone now can just just one object, rather than multiple such, as before. However, we have added functions c.scanone and cbind.scanoneperm for combining the columns in multiple runs of scanone (generally either multiple phenotypes or multiple methods). Completely revised the summary.scantwo function. The old version is saved as the function summary.scantwo.old(). Added an argument perm.strata to scanone and scantwo, to allow stratified permutation tests. If provided, it should be a vector of length the number of individuals in the cross; unique values in perm.strata will specify the strata in which the permutations should be performed. (For example, this could be an indicator of the sexes of the individuals, in which case the individuals will be shuffled separately within males and within females.) Permutations in scanone can now be done to give separate thresholds for the autosomes and the X chromosome. The argument perm.Xsp is used to indicate that this should be done, in which case many more permutations will be run for the X chromosome than for the autosomes, to ensure similar accuracy. The output of scanone when n.perm>0 is now given class "scanoneperm", and we've written a function summary.scanoneperm for getting LOD thresholds. (This is necessary, since the calculation autosome- and X-chromosome-specific thresholds is a bit complicated.) We've also added a function c.scanoneperm for combining the results of multiple permutation runs. (This because their combination is not so simple as before.) Added functions -.scanoneperm, +.scanoneperm, -.scantwoperm, and +.scantwoperm, for taking sums or differences of permutation results from scanone or scantwo. This is particularly useful for getting LOD thresholds for QTL x covariate interactions, though one must be careful to ensure that the permutations are perfectly linked, which can be achieved with set.seed. The permutation results from scantwo are completely changed. Rather than keep track of the maximum LOD score for the full model (two QTLs + interaction) and the interaction LOD score, we keep track the genome-wide maxima of the 5 LOD scores calculated in summary.scantwo. (See above.) The output is now given a class scantwoperm, and there is a summary.scantwoperm function for calculating LOD thresholds. There is also a c.scantwoperm function for combining results from multiple runs, largely for the case that multiple sets of permutations were run in parallel. Removed the ability, in scanone and scantwo, to use the snow package to do parallel analysis on a linux cluster. With the new changes in scanone, I found the code to be too cumbersome. Added the extended Haley-Knott method (see Feenstra et al., Genetics 173:2269-2282, 2006) to the scanone function. This is faster and more robust than standard interval mapping, and is a better approximation (but slower) than regular Haley-Knott regression. Revised the sim.cross() function so that, with a backcross, the effect in the "model" argument is to be specified as the difference between the average phenotypes for the heterozygotes and the homozygotes. (Previously, it was 1/2 this, which is different from the typical parameterization.) Added the ability, for a backcross, to estimate a genetic map under the Stahl model for crossover interference (of which the chi-square model is a special case). Also added a function, fitstahl, for getting the maximum likelihood estimates in the Stahl model (or the chi-square model); the genotyping error probability may be treated as known or may also be estimated. Added an argument, "use", to scanone and scantwo, for indicating, in the case that multiple phenotypes are to be run, whether only individuals with complete data on all phenotypes (use="complete.obs") or all individuals (use="all.obs") are to be used. The degrees of freedom are added as attributes in the output from scanone and scantwo, including the case of permutations. Minor changes: In read.cross, the symbol "#" is no longer treated as a comment character by default. The default is to use comment.char=""; that is, no symbol is treated as an indicator of comments. For the comma-delimited file formats, one may have a character interpreted as indicating comments using the comment.char argument. Fixed a bug for the new "batch mode" permutations in scanone and scantwo with method="hk" or ="imp". The trick only works if there is no X chromosome or all individuals have the same sex and cross direction or permutations are done stratified within sex and direction. Added an argument "show.marker.names" to plot.map and plot.scanone, so that marker names can be added to these plots. write.cross can now write data in the "csvr", "csvs" and "csvsr" formats. Added a function plot.pheno for plotting a histogram or barplot of a phenotype distribution. Added a function condense.scantwo for producing condensed versions of scantwo output, containing just the maximum LOD scores on each pair of chromosomes. One can get summaries from these but not plots. In plot.info, added step, off.end, error.prob and map.function arguments. The function now always calls calc.genoprob rather than relying on the values in the data. Changed the default cutoff for genotyping error LOD scores in top.errorlod and plot.geno to 4. Changed the name of the function clean() to clean.cross() and made a new function clean() that will dispactch a cross object to clean.cross. Added a function clean.scantwo() for cleaning up the output of scantwo: any values that are missing or are < 0 are replaced by 0 and any LOD scores for pairs of loci that do not have a marker between them are set to 0. The output of scantwo() now contains the original genetic map as an attribute; this is needed for clean.scantwo(). Slightly revised effectplot() so that if sim.geno hasn't been run, it is run (with a single imputation) before the plot is created. I also changed the names in the output, so that it is "Means" and "SEs". Changed the examples in the help file for effectplot. Added an argument "lodcolumn" to max.scanone, so that it behaves like summary.scanone. Added a function subset.scanone() for pulling out particular chromosomes or LOD score columns from scanone output. Added a function find.pseudomarker() for identifying the name of a pseudomarker that is closest to a specified position. This is useful for the effectplot() function. In scanone and scantwo, there is now a warning printed when individuals with missing phenotype are dropped. Also, a slightly better error message is printed if addcovar or intcovar are not numeric. Also added a warning message if addcovar or intcovar appear to be over-specified (having columns that need to be dropped). Fixed a slight problem in the column names from scanone() in the case of multiple phenotypes; changed the convention for this. Now the columns will just have the phenotype names. Fixed the help file for read.cross concerning the individual identifiers and about which files are used for the csvs and csvsr formats. Revised nmissing() so that if what="ind" and individual IDs are included as a cross phenotype (named "id" or "ID"), these are used as names in the output. Revised summary.cross() to include a check of whether the individual IDs (in a phenotype named "id" or "ID") are unique. If they are not, a warning is issued. Changed the "pheno" argument in plot.cross to "pheno.col", to be more consistent with other functions. Changed the name of the argument "which" in plot.rf and nmissing to "what". Added an argument "verbose" to the ripple function; if verbose=FALSE, the function doesn't print anything. Also modified print.summary.ripple so that it prints no more than 6 rows. Revised plot.missing so that if reorder=TRUE, the reordering of individuals is done by the average of only the numeric phenotypes (rather than all phenotypes, which gave an error). Fixed a slight bug in summary.cross() regarding duplicate marker names. Fixed a bug in scanone() that messed up the X chromosome label in the case of method="imp" with multiple phenotypes. Fixed a bug in scanone() regarding method="mr-imp" with permutations. Revised the listeria data set slightly; added an "alleles" attribute, with alleles "C" and "B", as this was a cross between BALB/cByJ and C57BL/6ByJ, and those strains were coded C and B in the original paper. This takes advantage of a feature added in version 1.03. Also added a phenotype "sex" that indicates all individuals are female. Added a phenotype "sex" in the hyper data, indicating that all individuals are male. Modified checkAlleles() so that it doesn't give an error if one inputs data for only the X chromosome. Revised calc.errorlod() so that it won't give a warning if it has to run calc.genoprob() because such probabilities aren't available. It will still give a warning if it has to re-run calc.genoprob() with a new error.prob value. Slight revision of plot.scanone() to include "Chromosome" as an x-axis label if multiple chromosomes are plotted. Fixed a slight bug in plot.info for the case that results are only at the marker positions. Changed the name of the "cols" argument in plot.pxg to "col". (This argument was added in version 1.03.) Revised summary.cross so that if the "jittermap" warning is printed, it's only printed once. Made a slight change to summary.map and print.summary.map, so that "sexsp" is an attribute. A couple of changes were made to the write.cross.qtlcart(): round the map locations, make sure backcross code is correct, and make sure RIL data is written as 0/2 rather than 0/1. Revised plot.info() so that if method="entropy" or method="variance", only the column requested is returned. (Previously, the other was also given, but with all 0's.) Fixed a bug in sim.cross for type="4way"; it would stop with an error if QTLs were to be simulated. Fixed a bug in fill.geno for the case that a chromosome has just one marker. Removed the function convert.cross(), which converted cross data from the format used in R/qtl version < 0.65 to the current format. This shouldn't be needed anymore. Fixed a bug in plot.pxg; the plot was messed up one requested an autosomal marker and an X chromosome marker, but with the X chromosome marker listed first. Version 1.03, 7/20/2006: Major changes: Fixed subset.cross() so that the attribitutes in the results of calc.genoprob, calc.errorlod, sim.geno, and argmax.geno, don't get lost on subsetting. This was important to ensure that the package will conform to a change that will occur in the next release of R. Added a function checkAlleles() for identifying loci that might have their alleles switched (in an intercross, if AA and BB are switched, or in a backcross if AA and AB are switched). The X chromosome is ignored. An internal function, checkrf() was removed; this was previously called by est.rf() but wasn't well written. Added an argument, alleles, for read.cross(), which takes two single-character allele labels that are included as an attribute in the cross and will be used as labels throughout the program (for example, in geno.table, effectplot and plot.pxg). This required numerous small changes throughout the package. The maps used for the results from argmax.geno(), calc.genoprob() and sim.geno() are now saved as an attribute on the data they create (within the cross object) so that create.map() doesn't have to be called repeatedly. Moved the code for simulating genotype data in sim.cross() into C, to increase speed, and modified it so that one may simulate under Frank Stahl's interference model (which includes the chi-square model as a special case). If one includes a phenotype named "id" or "ID", this will be used in top.errorlod(), plot.errorlod(), and plot.geno() as identifiers for the individual. Minor changes: Added a warning in summary.cross() if there are multiple X chromosomes; the summary now includes the names of the autosomes and X chromosome (for diagnostic purposes). Revised plot.rf() so that, if the results of est.rf aren't available, that function is run. Made a slight modification to the bayesint() function for getting Bayes credible intervals from scanone() results. Added a "chr" argument to pull.geno() and pull.map(), so that you can pull out the genotype data or map for a selected set of chromosomes. Fixed a slight bug in locatemarker(), used by makeqtl(). Added a function print.map() so that when you print a map, all of the class stuff doesn't get in the way. Included an additional argument [stepwidth = c("fixed", "variable")] in the internal create.map function, for Brian Yandell and the R/bmqtl package. This argument was also added to calc.genoprob(), sim.geno() and argmax.geno(). We also added a "stepwidth" attribute to the bits that these functions produce. Added an argument, "lodcolumn", to the function max.scantwo, for picking out a single phenotype in the case that the results concern multiple phenotypes. As with summary.scantwo() and plot.scantwo(), this function will only give the results for a single phenotype. Made a slight change regarding the sizes of the labels in plot.pxg() and added an argument concerning colors. Fixed a bug in scanqtl() regarding the X chromosome. Revised plot.map() slightly so that one can use "main" as an argument, to create a custom title (such as ""). Fixed a bug in sim.map() regarding sex.sp=TRUE Version 1.02, 6/2/2006: Major changes: Modified scanone() and scantwo() so that they may analyze multiple phenotypes simultaneously. This can greatly speed up the analyses with Haley-Knott regression (method="hk") and imputation (method="imp"). We can use this trick to speed up permutation tests: create multiple permuted phenotypes and then analyze them all at once. scanone() results no longer include parameter estimates (such as the phenotypic averages for each genotype group and the residual SD). The bookkeeping for this became too painful as we moved to allow multiple phenotypes to be analyzed simultaneously. To get such estimates, use fitqtl(). fitqtl() currently works only via the imputation method, but we expect to soon implement multiple interval mapping (MIM) and also Haley-Knott regression. scantwo() with method="em" now works for the X chromosome, including with model="binary". Modified the "map10" dataset: a genetic map modeled after the mouse, with markers having an approximately 10 cM spacing. We've revised the chromosome lengths to match those in the Mouse Genome Database. Dropped support for intercrosses with sex-specific maps (class "f2ss"). Changed the meaning of the argument "lodcolumn" in scanone(). It now should be an index starting at 1 rather than 3. I think this will be more clear for the case of LOD scores from multiple phenotypes, though perhaps it will be less clear. summary.scanone() now takes an argument "lodcolumn"; if a single scanone output is given as input, it picks off peaks using those LOD scores. (As with plot.scanone, this is indexed starting at 1.) Similarly, added a "lodcolumn" argument to plot.scantwo() and summary.scantwo(), for the case that the scantwo results are for multiple phenotypes; only results for a single phenotype are used by these functions, and "lodcolumn" indicates which one. Minor changes: Modified summary.cross() to give a warning if there are markers at precisely the same position. Added a function jittermap() to assist in fixing this. Numerous functions run into problems if there are markers on top of each other. Revised the hyper dataset so that it doesn't have this problem. Switched the order of the arguments "model" and "method" in scantwo() to match that for scanone(). Fixed a slight bug in max.scanone() that led to an unnecessary warning message. Fixed a slight bug in locate.xo(), used by plot.geno() to identify the locations of crossovers. Made a slight revision to read.map.qtlcart(), so that chromosome names are not required in the map file, and so that more informative messages are displayed if marker or chromosome names are not found. Modified qtlversion(), which prints the installed version of the package, to use library(help=qtl) rather than installed.packages(). This is a lot faster. Added, to summary.cross(), a check on whether multiple phenotypes have the same name. Modified plot.cross() so that it will work if multiple phenotypes have the same name (though that shouldn't happen). Fixed a bug in fitqtl() regarding the names of coefficients for interactions between QTLs and covariates when get.ests=TRUE. Fixed a slight problem in read.cross.csv() and read.cross.csvs() so they will read in 4-way cross data. You need to use genotypes=NULL. Modified c.cross() so that it can combined crosses typed at different numbers of markers. The number of chromosomes must be the same, and the genetic maps must be consistent. Modified plot.scanone() so that you can give it a "ylab" argument to override the y-axis labels. Eliminated the "main" argument, as that can be passed via "...". Modified the threshold for est.rf() to print warnings about possibly switched genotype data. Fixed a slight typo in the help file for plot.scantwo(). Fixed a bug in calc.pairprob() (used by scantwo) for RILs. Fixed a slight bug in scanone() for permutations; it dropped covariates in permutation tests with model="binary". Fixed a slight bug in read.cross with format "csvs", for the case that the argument genotypes=NULL. Fixed a slight bug in checkcovar() used by scanone() that arose when there were missing values in the phenotype data. Added a utility function chrlen() for pulling out the lengths of all of the chromosomes. Version 1.01, 10/25/2005: Major changes: Revised read.cross to include three additional data formats: "csvr" The format "csv", but with rows and columns interchanged. I call that a "rotated" version of the CSV format, but it's really a transposed version. "csvs" The format "csv", but with separate files for the genotype and phenotype data. Note that the first column in the phenotype data should specify the individuals' IDs, and that there should be a column in the phenotype data with precisely the same name, and the individuals should be in precisely the same order. "csvsr" The format "csvs", but with the genotype and phenotype files rotated (really transposed). Added example files (of the listeria data) in these formats in the "sampledata" directory. Minor changes: plot.scantwo can now plot the additive LOD scores in the lower triangle (with the argument lower="add") Fixed a bug regarding the X chromosome in scanone() with the use of covariates. Modifying plot.geno() to put X's at inferred crossover locations. Modified plot.rf() so that the lines between chromosomes are white. Added a "chr" argument to plot.map, so that a selected set of chromosomes may be plotted. Changed the default value for the error.prob argument from 0 to 0.0001 in est.map(), calc.genoprob(), sim.geno(), and other functions. Version 1.00, 9/10/2005: Major changes: Revised fitqtl() so that it can provide estimated QTL effects (using the imputation method), though this is working completely only for autosomes in backcrosses and intercrosses at this point. Revised fitqtl() so that it treats the X chromosome appropriately. Minor changes: Fixed a bug in read.cross for the "qtlcart" format. There was a problem in the reading of map files if the inter-marker distances were at all out of alignment (which occurred when two markers were 100 cM apart). est.rf() was revised to treat the X chromosome properly in an intercross. Fixed a bug in scanqtl() in the case that covar=NULL was specified; this should be treated just like if it were missing. Modified plot.scantwo() so that the default is lower="joint"; I've become suspicious of lower="cond-int" and "cond-add". Added another argument to plot.scantwo(): point.at.max, for plotting an X at the maximum LOD. Added an argument, "verbose", to scanqtl(), to give feedback about progress. Revised scanqtl() so that makeqtl() doesn't get called repeatedly, but rather the imputations get copied over. This sped the thing up immensely. Replaced calls to print.matrix() in print.summary.ripple(), as the function will be dropped from R ver 2.2. Fixed a bug in plot.pxg(): it halted with an error in the case of multiple markers on the same chromosome. Fixed a bug in fitqtl() that made it die with only one QTL. plot.scantwo() now gives cM locations on the axes in the case of just one chromosome. Fixed a bug in find.marker. Fixed a slight bug in plot.scanone() Added a function qtlversion() to print the version number of the currently installed version of the package. Fixed a bug in plot.scantwo() regarding the X chromosome in the case that markers were included in scan but are not to be plotted. Fixed a bug in scanone() and scantwo() regarding the use of sex and/or pgm as covariates on the X chromosome; this affected only results with method="imp" and only for the X chromosome. Revised some of the help files so that they conform to the rules for the latest version of R. Revised scantwo() by imputation so that the interaction LOD score is obtained by combining across imputations for each of the full and additive model and then subtracting, rather than subtracting and then combining. Fixed some typos in the help files. Version 0.99, 4/26/2005: Major changes: scanone() now allows additive and interactive covariates in the case model="binary" (that is, for a binary trait). This uses a logit link. scantwo() now allows analysis of binary traits (model="binary"), for method="em" only. Added a few new utility functions: bayesint() for calculating Bayesian probability intervals [cf lodint()], comparegeno for comparing individuals' genotypes, and strip.partials() for removing partially informative genotypes. Added +.scanone() and -.scanone() for adding and subtracting the output of scanone() Revised summary.scanone() and print.summary.scanone() so that it can summarize the result of multiple scanone() results together. Added code (the function sim.cc) for simulating the "Collaborative Cross" (8-way RILs) and for calculating QTL genotype probabilities and identifying the most likely genotypes (using Viterbi) with SNP data on such lines. Minor changes: Revised plot.scanone() so that when one chromosome is plotted, it starts whereever the first marker on the chromosome was placed, and not necessarily at 0. Revised plot.map() so that shifting chromosomes so that the first marker is at 0 cM is optional. (Use shift=FALSE to not do such a shift.) Fixed the issue regarding use of sex and/or pgm as covariates for the X chromosome; such covariates are dropped just for the X chromosome. Revised plot.pxg() so that it no longer returns Rsq, fit and so forth. I've gone back to returning just information about the data that are plotted. I added an argument "main" in the case one wishes to use a plot title different from the default. Changed the default for plot.geno from horizontal=FALSE to horizontal=TRUE. switch.order() no longer resets the location of the first marker on the chromosome to 0 cM, but retains the offset from the original map. Changed "trace" as an argument to "verbose", to avoid clashes with the built-in R function trace() Revised summary.cross() to check the class of chromosomes (the components in cross$geno); they should each have class "A" or "X". Revised subset.cross() so that it won't produce duplicate chromosomes, and to treat missing values in the ind argument. Fixed a slight bug in fitqtl() regarding the drop one analysis when there is just one QTL. There was a slight bug in plot.pxg() regarding the X chromosome. Fixed a bug in scanone() regarding the X chromosome for RILs. Fixed a slight, silly bug in est.map() for sex-specific maps, in which the location of the initial marker was randomized. Revised plot.scanone(), plot.scantwo(), and other plotting functions so that they don't show their NULL return values. Fixed a bug in makeqtl() regarding the X chromosome if there are genotype probabilities. Revised the internal checkcovar() function to check that the chosen phenotype for scanone(), scantwo(), etc., is numeric. Previously, an rather uninformative error message was given. Modified the convergence criterion for scantwo() by the EM algorithm; we now just look at the log likelihood, and not at the parameters. Fixed a bug in read.map.qtlcart(). Fixed bugs in scanone and scantwo for method="imp", for the case that one has exactly one imputation. Fixed a memory-overwrite problem in scanone() with method="imp". Fixed a bug in fixXgeno.f2; a slight error that shows up in the case the pgm values are switched. Added code to simulate Collaborative Cross data and to do calc.genoprob() and argmax.geno() on such data, though none of this is documented yet. movemarker() now updates the results of est.rf, calc.genoprob, calc.errorlod, sim.geno, argmax.geno, if they are available. (est.rf is simply re-run; the others are re-run for just the relevant chromosomes). calc.errorlod had neglected to include the map function as an attribute. This is now fixed. Revised scantwo with method="em" so that it prints the verbose warning messages only if the verbose argument is > 1. With verbose=TRUE, only the chromosomes are printed. Version 0.98, 9/11/2004: Major changes: There is no longer an argument "sep" for the function read.cross. Instead, we use "...", which is passed to the function read.table (all this just for the "csv" format). (sep="," is still assumed for that format). This change allows one to use sep=";" and dec="," which many people prefer. The function read.cross now automatically converts X chromosome genotype data into the standard internal format (with all individuals coded like an autosome in a backcross). The functions scanone() and scantwo() were revised so that they treat the X chromosome appropriately. The argument "x.treatment" has been deleted. What had been x.treatment="full" (namely, that hemizygous genotypes are considered different from homozygous genotypes) is now forced. The big change concerns the "null hypothesis" for the X chromosome, which includes sex and/or "pgm" as covariates, in order to avoid spurious linkage on the X due to sex differences in the phenotype. See the help files for these functions for details. Note that the output of the scantwo function has changed somewhat; it would be best to re-run scantwo with this new version of the software. Added a function movemarker() for moving a marker from one chromosome to another. Revised scanone(), scantwo(), discan(), vbscan() and plot.info() so that inter-marker positions are cited as "c*.loc*" rather than "loc*.c*" or "loc*". Added a function convert.scanone() for converting scanone output to the new format. (The new plot.scantwo will interpret, for the old version of scanone output, every inter-marker position as a marker.) Minor changes: Added a function find.pheno() [from Brian Yandell] for finding the phenotype column with a particular name. Added a function find.flanking() [from Brian Yandell], which is similar to find.marker(), but gives not just the closest marker but also the left- and right-flanking markers. Added a function print.cross() which prints a short message and then calls summary.cross(). This was added in order to avoid the essentially always unintentional printing of an entire (generally quite large) cross object. Modified summary.scantwo to allow a type option to get summaries based on peak "joint" or "inter" and to allow negative thresholds relative to max joint, inter, or individual LODs [from Brian Yandell; I'm not really sure what this means]. Modified plot.scantwo to modify the contour option, making 1.5 from the peak the default on each half-image, and to take a numeric vector of drops from the peaks. Added arguments col.scheme and gamma for different color schemes [from Brian Yandell]. Hugely sped up plot.scantwo for the case of lower="cond-int" and lower="cond-add"! (By hugely, I mean by a factor of 50 or more.) Added a function summary.map() for giving summary information about a genetic map. In read.cross, if chromosome names start with "chr" or "chromosome" (ignoring case) these initial strings are removed. est.rf now returns a warning if a marker appears to have its genotypes miscoded (so that it shows a rec. frac. > 0.5 with LOD > 3). Deleted the function pull.chr(), which was deprecated in version 0.89 in Nov, 2001. You can use subset.cross() instead. Fixed a bug in calc.errorlod regarding the X chromosome in an intercross. read.cross with format="qtlcart" printed on screen the number of individuals but called it the number of phenotypes. Brian Yandell provided another revision to this, to fix bugs regarding phenotypes that are factors. Fixed a slight bug in summary.cross() in the case that a cross contains no autosomal data. Fixed a bug in read.cross for the mapmaker format; if a marker or phenotype name were listed without any data, an error resulted. Fixed a bug in summary.scanone which resulted in multiple rows being returned for a single chromosome if multiple positions shared the same maximal LOD score. Fixed bugs in plot.scanone and plot.scantwo for the case that the "chr" argument was used with chromosomes not in the usual order. read.cross with format="csv" now prints a warning if unusual entries are seen in the genotype data. Revised the geno.table() function so that its first column is the chromosome number Modified read.cross.qtx(); "X" or "x" in a phenotype is a missing value. Changed a call to print.coefmat() to a call to printCoefmat(), as the former is being discarded in favor of the latter. Changed the name of the utility function fixXdata() to reviseXdata(). This function deals with the X chromosome genotypes in scanone() and scantwo(). Modified the source code in hmm_bc.c, hmm_f2.c, hmm_main.h so that we don't repeatedly calculate log(0.5), log(2.0) and log(0.25), but rather rely on #define statements Got rid of the WIN32 stuff in addlog and subtractlog in util.c, which were used in version 0.97 as I'd not been able to get log1p to work. plot.missing now gives a more meaningful error if the argument "reorder" is greater than the number of phenotypes. I split up the read.cross.R file into several smaller files, for more easy revisions. Revised the function comparecrosses(), adding an argument "tol" so that the genetic maps and phenotypes do not need to be *exactly* the same, but can be identical to within the specified tolerance, "tol". Also, a warning (rather than an error) is produced if the inter-marker distances are the same but the position of the initial marker is different. Fixed a slight bug in write.cross for the case of phenotypes that are factors. Fixed a problem in read.cross for format "gary"; now I pass the "na.strings" argument for reading the phenotype data. Fixed a problem in read.cross with format "gary" or "csv" for ensuring that phenotypes that appear to be numeric are read as numeric and not as factors. Modified the function getsex(), which finds and interprets the sex and pgm columns in the phenotype data, for the case where sex is read as a factor with just one level (either "F", "f", "M", or "m"). Added more understandable warning/error messages to plot.scanone if a chromosome ID in the "chr" argument doesn't match those in the scanone output. Fixed a problem in the example data "badorder"; chromosomes 2 and 3 were switched. Fixed a bug in read.cross for the QTL Cartographer format for the case that there is just one individual. Modified some of the C calls to FORTRAN subroutines, using the macro F77_CALL(), as this is recommended in the R documentation. (Fortran routines currently used: dqrls, dpoco, dposl) Modified est.map so that it removes individuals with fewer than two typed markers. Their presence is of no value in estimating the map, and can really slow things down. Revised a number of the help files to make the automated tests of the integrity of the software much faster. Revised read.cross.mm() so that (a) the cross type is taken as the 4th word (rather than the last word) on the first line, in case someone includes additional characters, and (b) if the sample map format is used but chromosome assignments are not provided, a meaningful error message is displayed. Made a few very minor revisions to the "rqtltour.pdf" tutorial. Revised getgenonames() and reviseXdata() to remove the x.treatment argument, which is now assumed to be "full". Revised effectplot() and plot.pxg() accordingly. Modified the utility function to do map expansion in RIs for the X chromosome. Need to modify things further to take account of the lack of balance on the X chromosome...it's quite different from a backcross. Revised plot.pxg() so that it can take a vector of marker names [from Brian Yandell]. Revised plot.scantwo to allow different color schemes and to give contours at 1.5 (or other specified values) below the maximum LOD [from Brian Yandell]. Revised summary.scanone and print.summary.scanone so that summary.scanone will never print anything, but will leave it to print.summary.scanone to do so. (Previously, summary.scanone printed a message if there were no peaks above the LOD threshold.) summary.scantwo and print.summary.scantwo were revised similarly. Modified plot.scanone to allow NAs in the LOD scores. Revised scanone and scantwo so that n.perm=0 is treated the same as if it were not provided. Fixed a bug in plot.rf() in the case that chromosomes are provided out of order, in which case the thing plotted garbage. The fix involved revising subset.cross() so that the chr argument is sorted. Replaced the example data sets with compressed versions. Had to modify part of the C code for scanone with model="2part". I'd hard-coded some of the array limits! Changed the default line types and colors in plot.scanone(). Added a warning for scanone() and scantwo() about the number of individuals that are omitted due to missing phenotype or covariate information. Revised write.cross so that X chromosome data is converted back from our internal format to the standard format. Version 0.97, 6/19/2003: Major changes: Added an argument "weights" to the functions scanone and scantwo, to allow differential weighting of individuals in a genome scan; these are only used with model="normal". Modified the function "plot.scantwo" for plotting the results from a two-dimensional, two-QTL genome scan. There are two new arguments: lower controls what LOD scores are plot in the lower triangle (lower="joint" corresponds to the previous version of this function), while nodiag controls whether to plot the scanone results on the diagonal. Using lower="cond-int" (the default) gets rid of the "coattail" effect often seen when plotting the joint LOD scores. Ted Lystig for suggested this modification. Added a new function effectscan() for plotting the estimated allelic affect at all markers on selected chromosomes. Minor changes: Modified write.cross so that it will output data in "gary" format. Added a function lodint, for calculating LOD support intervals based on results from scanone. Added a function nmissing(), which calculates the total number of missing genotypes for each individual in a cross, or for each marker. Added a function pull.geno() for pulling out the set of genotype data for a cross as a single big matrix. Added a function comparecrosses() for verifying that two objects of class "cross" are identical. The results of geno.table() now includes P-values from chi-square tests for Mendelian segregation. Modified the function c.cross for combining crosses. You can now combine backcrosses and intercrosses, provided that they have exactly the same genetic maps. Further, we no longer discard the results of sim.geno and calc.genoprob, provided that the same step, off.end, and error.prob arguments were used. Added an additional argument, cex, to the function plot.geno, for control of the size of the points in the plot. Also changed the orientation of the plot when horiz=FALSE, so that the centromere is at the top of the figure rather than the bottom. Fixed calc.pairprob so that it will work for RI lines ("risib" or "riself"). Thus, scantwo should work with RI lines now. Updated read.cross for format="gary" so that the marker positions file ("mapfile") and phenotype names file ("pnamesfile") are not necessary. Set these arguments to NULL (e.g., mapfile=NULL) if the corresponding files are not available. Added a "chr" argument to max.scanone, so you can get the maximum LOD score for a particular chromosome. Revised the function switch.order() so that, if estimated recombination fractions are present (i.e., est.rf() was used), these are revised appropriately; previously they had been removed. Also added err and map.function arguments, to be passed to est.map() when the map is re-estimated. Revised scanone() and scantwo() slightly; the statement for producing a warning regarding the use of method=="im" (vs "imp" or "em") was slightly wrong. Fixed a slight bug in scanqtl() for the case that a fixed position is provided rather than a range (commented out two lines). summary.cross() now prints a warning if $data objects are data frames. (They should be simple matrices.) summary.cross() now prints a warning message if the genetic maps are not matrices with 2 rows for "f2ss" and "4way" crosses, or are matrices for other crosses. drop.markers() now prints a warning if some markers were not found. Added arguments ylim and add.legend to the function effectplot(). Added arguments xlim and mtick to function plot.scanone(). (mtick allows marker locations to be indicated by triangles rather than line segments.) Fixed a bug in read.cross for the case that phenotypes have values like "1x2". Fixed a slight bug in write.cross for the qtlcart format. Fixed a bug in read.cross for the qtlcart format regarding the determination of whether a chromosome is autosomal or the X. (Previously, looked for an "X" or "x" in the marker names; now look at whether the chromosome names contains an "X" or "x".) Fixed a bug in makeqtl() for the case of a four-way cross. (Hadn't dealt properly with sex-specific maps. fitqtl() now stops with a more meaningful error message if imputed genotypes are not available in the input "qtl" object. Revised the marker names for the X chromosome in the map10 dataset that is included. Revised est.map() for the case of a sex-specific f2 (cross type "f2ss"); the starting map for the EM algorithm is randomized a bit. Revised a bunch of the R code files so that paste() is not included within stop() or warn(). In a couple of utility functions for the hidden Markov model engine, I need access to the log1p() function, but I'm having trouble with that in Windows. Thus, in Windows only, I use log(1+x) in place of the preferred log1p() function. Added tests of input/output that are run when doing a check of the package. Version 0.96, 9/13/2002: Major changes: None. Minor changes: Added Listeria data in QTL Cartographer format to the sampledata directory. Revised read.cross and write.cross for QTL Cartographer format, so that the cross types are converted between those of R/qtl ("f2", "bc", "riself", "risib") and those of QTL Cartographer ("RI0", "RI1", "RI2", "B1", "B2", "SF2", "RF2"). Revised read.cross for the Mapmaker format. The map file can now be in a second format, ".maps", which is created by Mapmaker/exp. The function determines whether it has been presented with the .maps format or the 2- or 3-column tabular format that has been available. Brian Yandell wrote the function to read ".maps" files. Fixed a small bug in write.cross for the Mapmaker format, and modified the ".prep" file that is created, so that marker distances are no longer included, and including lines "framework chr*". Revised read.cross with format="csv", so that it gives more clear error messages in some cases. Updated the R/qtl tutorial, rqtltour.pdf. This is now in a directory "docs" in the R/qtl distribution. Version 0.95, 8/1/2002: Major changes: Modified the functions scanone and scantwo in order to treat the X chromosome appropriately. Each has a new argument, x.treatment, which indicates how to treat the X chromosome (in particular, whether hemizygous males should be treated the same as homozygous females). For analysis to proceed properly, there should be columns "sex" and "pgm" in the phenotype data, indicating the sex of each individual, and the direction of the cross. See the X chromosome section of the help file for read.cross for more information. Added another argument to plot.scanone, "lodcolumn", an integer (or a vector of 3 integers) indicating which columns of the scanone output should be plotted (generally column 3). Added two functions for plotting phenotypes against marker genotypes. plot.pxg() plots the phenotypes against the genotypes at a single marker. effectplot() plots the average phenotypes against genotypes at one or two markers (or covariates). Also added a function find.marker() which returns the name of the marker closest to a specified position. Added facilities for analyzing recombinant inbred lines. We now allow two additional cross types, "riself" (RI lines from selfing) and "risib" (RI lines from sibling matings). Added an internal function expand.rf.ri and made important modifications to calc.genoprob, sim.geno, argmax.geno, and est.map. Also modified summary.cross, print.summary.cross, geno.table, replace.map, discan, ripple, scanone, scantwo, makeqtl, calc.errorlod, est.rf, write.cross.mm, write.cross.csv. Replaced the example fake.f2 with some new data, which includes both males and females and both directions of the intercross, in order to illustrate the proper analysis of the X chromosome. Modified read.cross and write.cross (and added code from Brian Yandell) to read and write data in QTL Cartographer format. Minor changes: Fixed a bug in read.cross; the "genotypes" argument needs to have "C" and "D" reversed. "C" = "not BB" = 5 internally; "D" = "not AA" = 4 internally. Thanks to Martin Grandona for identifying the problem. Fixed a bug in read.cross for format "csv": an error occurred if marker positions were not given and the first individual was missing a phenotype. Thanks to Justin Borevitz and Norman Warthmann for identifying the problem. Fixed a bug in fitqtl. Also added type III sums of squares table and also nominal P-values. Revised the read.cross functions so that if the X chromosome data is coded as A:B, it gets re-coded appropriately. Revised read.cross.mm and read.cross.csv so that if marker positions are included, marker order is taken according to those positions. (Previously, read.cross stopped with an error.) Added some additional example data, bristle3 and bristleX, from Long et al. (1995) Genetics 139:1273-1291. Added an example genetic map, map10, containing 19 autosomes and an X chromosome, with chromosome lengths approximately as in the mouse and markers at approximately 10 cM spacing. Changed web references "biosun01.biostat.jhsph.edu" to "www.biostat.jhsph.edu". Revised summary.cross to print an error if the cross type is not one of "f2", "f2ss", "bc", "4way", "risib", or "riself". Revised plot.map so that, when genetic maps are plotted vertically, the 1st marker is at the top (rather than at the bottom). Version 0.94, 5/30/2002: Major changes: Modified scanone and scantwo to include the methods "mr-imp" and "mr-argmax", for performing "marker regression" by first filling in any missing genotypes by one imputation ("mr-imp") or using the Viterbi algorithm ("mr-argmax"). Edited functions read.cross.*, argmax.geno, sim.cross, and sim.draws, so that the data, argmax and draws portion of a cross object are stored as integers. This can save considerable space. Added functions to perform a general scan by imputation: fitqtl(), makeqtl(), scanqtl(). Minor changes: Edited the read.cross.* functions again; now by default dir = "" rather than ".", and I no longer remove any trailing "/" from dir. Added checks of genotype values in the function summary.cross. Rather than edit the read.cross function to read in X chromosome data appropriately, I instead edited its help file, to explain that X-linked data should be coded as an autosome in a backcross (with genotypes A and H). Fixed slight errors in the functions scanone, calc.genoprob, discan, calc.pairprob, and sim.geno regarding the naming of the genotypes for the 4-way cross. Changed a couple of apostrophes to double-quotes in the function summary.scantwo. Added a Morgan map function (mf.m and imf.m), with revisions to argmax.geno, calc.genoprob, calc.pairprob, calc.errorlod, est.map, ripple, sim.geno, sim.cross, sim.cross.bc, sim.cross.f2, sim.cross.4way, fill.geno. Fixed a slight bug in ripple regarding the estimated chromosome lengths in the case of a 4-way cross; I was picking out the wrong element of the map. Fixed a bug in plot.scantwo regarding chromosome labels. Fixed bugs in max.scantwo and scantwo.perm regarding infinite LOD scores (which comes up especially when running scantwo with method="mr"). Fixed a bug in read.cross.qtx regarding the determination of whether a cross is a backcross or an intercross. Also fixed the case of a backcross coded as H:B rather than A:H. Added a warning in summary.cross regarding duplicate markers. Modified the example cross data (such as hyper and listeria) so that genotypes are stored as integers. Updated the "README.txt" file, to include explanations for installation of R and R/qtl on Mac OS. Changed the default for the na.strings argument in read.cross and read.cross.csv to include "NA". Changed a couple of lines in write.cross.csv and write.cross.mm for the treatment of NA strings. Modified ripple so that orders considered are printed in a way that the left-most marker in the original order is always to the left of the right-most marker in the original order. In various functions, made sure that 0 < error.prob < 1. Edited plot.scanone so that when only one chromosome is plotted, the chromosome number doesn't appear at the top, and when multiple chromosomes are plotted, the chromosome numbers appear at the bottom, rather than cumulative cM position. Changed the addcov and intcov arguments to scanone and scantwo to addcovar and intcovar, respectively. Version 0.93, 4/1/2002: Major changes: Added ability to read data in Mapmanager QTX format. This may be done via the read.cross function by using the argument format="qtx". Added a file in this format to the sampledata directory distributed with R/qtl. Modified function ripple(), which compares marker orders, so that it may evaluate counts of obligate crossovers, which will be extremely quick relative to performing an exact likelihood calculation. This method has been made the default. Minor changes: Added functions max.scanone and max.scantwo for getting information on the location with the highest LOD or joint and interaction LODs Modified summary.scantwo so that if the argument thresholds has length 1, the interaction and conditional thresholds are assumed to be 0 (so that all chromosome pairs for which the maximum joint LOD is greater than the given threshold are printed). Revised the C functions emit_bc(), emit_f2(), emit_f2ss() and emit_4way() so that unexpected observed genotypes are treated as missing. Revised read.cross, read.cross.csv and read.cross.mm slightly, so that estimate.map is TRUE by default, and so that the genetic maps are re-estimated only if both estimate.map is TRUE and the genetic map is missing from the input files. If estimate.map is FALSE and the genetic map is missing from the input files, a dummy genetic map is inserted. Fixed a bug in sim.cross, sorting the "model" matrix in advance of performing the simulation, because the results were erroneous if QTLs were specified out of order. Edited the functions read.cross.* to use the function file.path() to create file names. Version 0.92, 2/12/2002: Minor changes: In read.cross.mm and read.cross.csv, when using the function read.table, we replaced the use of as.is=TRUE with colClasses="character". Apparently as.is=TRUE didn't work in R version 1.4.0. In read.cross, changed the default of the argument "estimate.map" to FALSE. Version 0.91, 12/3/2001: Minor changes: Fixed a problem with chromosome labels in plot.scantwo. Fixed a slight bug in summary.ripple. Previously forgot to implement the use of the "main" arg for plot.scanone. Fixed a slight bug in read.cross.gary related to having just one marker on a chromosome. Fixed a slight bug in plot.cross for the case auto.layout=FALSE. Revised read.cross so that, for the csv format, if the argument "genotypes" is NULL, the genotypes are assumed to be correct. If there are genotypes > 5, it is assumed to be a 4-way cross. For some reason, the wrapper for est_map for 4-way crosses got deleted. I've re-written it. Hopefully it works! Fixed a slight bug in plot.map for plotting two sex-specific maps. (The function works by pulling apart the sex-specific maps and then calling plot.map again twice. After those calls, it should return.) Expanded examples in the help file for fake.4way. Fixed a bug in create.map for sex-specific maps. Revised calc.genoprob, argmax.geno and sim.geno so that, in the case of one marker on a chromosome, off.end is forced to be > 0. Revised plot.scanone so that if there is exactly one LOD score for a chromosome, a small segment is plotted rather than a dot. Fixed a couple of minor bugs in read.cross for the mapmaker format: in dealing with the "symbols" information in the mapmaker file, and in counting the number of lines in the file. Added a utility function checkcovar() to check phenotypes and covariates in scanone and scantwo (thus removing some redundancy). Version 0.90, 11/24/2001: Minor changes: Replaced the example data fake.bc with something that will allow the illustration of the use of covariates. Added print.summary.ripple; I'd forgotten to write it before. Added an updated tutorial on R/qtl, distributed as the file rqtltour.pdf Version 0.89, 11/22/2001: Major changes: Consolidated scanone, vbscan and discan into the single function scanone, with an argument model=c("normal","binary","2part","np"). The non-parametric "method" is now a "model". Buried scanone.perm and scantwo.perm as internal functions. To do permutation tests, one now uses the main functions (such as scantwo) and specifies the n.perm argument. Similarly, read.cross.* and write.cross.* were buried, so that the user is expected to call either read.cross or write.cross rather than calling the format-specific functions directly. This was done anticipating an increase in the number of such format-specific read.cross functions. Got rid of find.errors and plot.errors, as I don't like them. Use calc.errorlod and plot.errorlod instead. Wanted to toss pull.chr, but instead just kept an internal version which calls subset.cross and prints a warning, in case our one official user has code which requires it. Minor changes: Added an "eq.spacing" argument to sim.map for generating maps with equally-spaced markers. This seems more useful than putting them down at random. Re-wrote a great deal of the help documentation (especially the examples and details). Added a new example data set, badorder, with some errors in marker order. (This is to illustrate the functions est.rf, ripple and switch.order.) Fixed a slight error in summary.scantwo. We print pairs of loci only if their joint LOD exceeds its threshold and either (a) the epistasis LOD exceeds its threshold or (b) both conditional LODs exceed their thresholds. Totally re-wrote print.summary.scantwo. It was unnecessarily complicated before. Made a very slight change regarding the zlim in plot.scantwo. Fixed scantwo, summary.scantwo and plot.scantwo to deal with cases of bad LOD scores (NAs, negative numbers and Infs). A warning message will always be printed. Modified scanone_imp.c so that nullRss and altRss don't allocate memory each time. Fixed a very bad bug in dealing with interactive covariates. Fixed a single-character bug in scantwo_mr.c that was causing a core dump. Version 0.88, 11/20/2001: Major changes: Added a scantwo function to do two-dimensional genome scans, calculating LOD scores for a two-QTL model and to test epistasis between each pair, with calculations done by imputation, Haley-Knott regression, marker regression or the EM algorithm. Hao Wu wrote the imputation method. With Hao Wu, wrote plot.scantwo to plot the results of scantwo, summary.scantwo to summarize the results, and scantwo.perm to get genome-wide LOD thresholds for a 2-dimensional genome scan by permutation tests. The summary.scantwo function uses a criterion due to Gary Churchill and Saunak Sen. Added a C function to calculate joint genotype probabilities for pairs of putative QTLs on the same chromosome. Because the resulting set of probabilities can take up a lot of memory, we're not going to make these accessible to the user. The function calc.pairprob was created, but this is not to be called by the user, but rather will be called when needed. Minor changes: Added a "method" argument to vbscan, even though only method="em" is currently available. Revised scanone, scantwo, discan, vbscan, and their corresponding ".perm" functions so that the output has attribute "method" to indicate what method was used and attribute "type" to indicate the type of cross that was analyzed. Changed method="im" to method="em" in scanone and discan; changed method="markreg" again, this time to method="mg". Changed the order of these methods in scanone. calc.genoprob now includes an attribute "map.function" with the probabilities. Changed colors plotted in plot.rf. Modified the C function scanone_mr (marker regression) to avoid repeatedly running the null model regression in the case of complete marker data. Changed a good amount of R code like "1:length(x)" to "seq(along=x)" Added a function fill.geno for imputing missing marker data by simulation (through sim.geno) or by the Viterbi algorithm (through argmax.geno), so that one may perform quick-and-dirty (with an emphasis on dirty) genome scans by marker regression. Fixed a small bug in sim.cross.f2. Fixed some problems related to chromosomes with only one marker: read.cross.csv, create.map, subset.cross. Fixed a bug in the location of chromosome labels in plot.scanone. Added an argument "main" for placing a title on the plot. Revised lots of little pieces of code using "drop=FALSE" when subsetting a matrix or array in order to retain the structure. read.cross.csv can now deal with categorical phenotypes, and plot.cross was revised to deal with such non-numeric phenotypes. Added an argument "auto.layout"; if TRUE, mfrow is set so that the many plots produced will all fit in one figure. par(ask=TRUE) is no longer ever set. Revised sim.cross so that when keep.qtlgeno=TRUE, the QTL genotypes are retained in a component cross$qtlgeno (rather than within the data matrices). Version 0.87, 11/13/2001: Major changes: Hao Wu (hao@jax.org) has implemented the imputation method of Sen and Churchill (2001) for a genome scan, included as method="imp" in the function scanone. Added a non-parametric method to the function scanone, using a modified version of the Kruskal-Wallis test (cf Kruglyak and Lander 1995). scanone now allows the use of covariates for all methods except the non-parametric method. Phenotypes in a cross object are now a data.frame. Modified example data files and the following functions to make this work: sim.cross.*, read.cross.*, summary.cross, write.cross.csv. Minor changes: Changed the name of the "anova" method in scanone to "markreg". Changed the name of the argument "print.rf" in the est.map function to "trace." Modified the default cutoff in top.errorlod; allow cuts and colors in plot.errorlod to be specified by the user. summary.cross() now checks that markers are in increasing order. Made the third row (marker positions) in csv file optional in read.cross.csv. Added a utility function subset.cross() for pulling out specified chromosomes or groups of individuals from a cross object. We should not need pull.chr() any longer. Added a utility function c.cross() for concatenating multiple cross objects. Changed stopping rules for discan, discan.perm, vbscan, vbscan.perm, est.map, est.rf, ripple, scanone, scanone.perm: |x(s+1) - x(s)| < e {|x(s)| + e*100} where by default e = 1e-4 Fixed the utility function create.map() for the case where the genetic map starts at somewhere other than 0. Placed help information for discan.perm, scanone.perm and vbscan.perm within the files for discan, scanone and vbscan, respectively. Version 0.86, 11/4/2001: Fixed a *real* bug in argmax.geno(). Added discan() for doing interval mapping with a dichotomous trait. Added documentation for the print.summary.* and internal functions. Edited documentation files to conform to R guidelines. Reduced the minimum value of the error.prob argument in est.map, calc.genoprob, argmax.geno and sim.geno from 1e-14 to 1e-50. Version 0.85, 10/29/2001: Tried to fix up some of the plot.* and summary.* functions so that I don't get warning messages in "R CMD check". Fixed a few minor problems in the help files. Updated the a.starting.point() help file. Fixed a couple of problems in marker order in the hyper data. Added plot.info() for plotting the proportion of missing information in the genotype data. Fixed bug in plot.scanone() that led to problems in overlaying LOD curves using add=TRUE. Added an argument, gap, to specify the distance between chromosomes. Fixed bug in print.summary.scanone() that resulted in an error when there was just one chromosome with LOD above the specified threshold. Version 0.84, 10/10/2001: Fixed slight error in sim.cross(); marker genotypes were removed rather than qtl genotypes. We now use the function drop.qtlgeno() to do this. Changed anova method in scanone() to use observed genotypes. Individuals with missing or partially missing genotypes are dropped. Added Haley-Knott regression method to scanone(). Added a function ripple() for comparing marker orders for a single chromosome, looking at all permutations of a sliding window of markers. Also added switch.order() to switch the order of markers on a specified chromosome. Removed null markers from listeria data. Fixed bugs in read.cross.mm() and write.cross.mm(). Added csv and mapmaker format files to sample data directory. Allow specification of starting value is scanone and vbscan Added a document "rqtltour.pdf" describing the package and giving a couple of examples. Version 0.83, 09/23/2001: Fixed a very slight bug in summary.scanone(). Changed the argument "which.chr" in plot.scanone() to simply "chr". Added a "chr" argument to plot.missing(). Version 0.82, 09/20/2001: Added write.cross.csv(), for writing data in comma-delimited format. Changed write.mm() to write.cross.mm() and added write.cross() as a wrapper to these two functions. All functions that use map functions now allow use of the Carter-Falconer map function. Changed remove.markers(), remove.nullmarkers(), and remove.qtlgeno() to drop.markers(), drop.nullmarkers() and drop.qtlgeno(). Revised plot.rf() so that missing values appear in gray. Added read.cross.gary(), to read data in Gary's format, and read.cross.csv(), to read data in comma-delimited format. Fixed the bugs in read.cross.mm(); see BUGS.txt. Fixed summary.cross() so that it checks marker names in the data and the map. Added summary.scanone(), giving a summary of the output of scanone(). Added possibility of F2 intercross with sex-specific maps. Use class "f2ss" rather than "f2." This is in the testing stage. The only revised functions, at this point, are est.map() and calc.genoprob(). Added a function convert2ss() to convert a cross object from "f2" to "f2ss" format. Version 0.81, 09/16/2001: plot.scanone can now plot three scanone outputs, and includes an "add" argument for adding additional outputs to a current plot. Replaced 1e-10 with 1e-14 as tolerance value for error probability and minimum map distance. Changed the "min.d" argument in plot.geno() to "min.sep", taken to be a percent of the chromosome length. Added Carter-Falconer map function: mf.cf() and imf.cf(). Note that there is no closed-form version of mf.cf(), and so I use the R function uniroot(). Fixed a slight error in replace.map(). In est.map, calculate the log likelihood at the end; this is saved as an attribute, "loglik" for each chromosome's map. If the "print" argument is used, print the loglik, too. Made error.prob=0 the default for the functions argmax.geno(), calc.genoprob(), est.map(), and sim.geno(). Fixed the file permissions for many of the files, so that they are readable by all users. Version 0.80, 08/07/2001: Eliminated the map component of the results of calc.genoprob, argmax.geno, and sim.geno. Since we are now including attributes "error.prob," "step," and "off.end," we can just use create.map() to recreate the map each time, without having to carry it along. Changed the name of plot.geno() to plot.missing() and plot.chr() to plot.geno(). Added vbscan() and vbscan.perm() to perform the analysis described in V Boyartchuk et al. (2001), for a phenotype where some individuals have some quantitative phenotype, while for others it is undefined. (Examples: the size of a lesion, where some individuals exhibit no lesion; time-to-death after an infection, where some individuals recover from the infection.) Version 0.79, 07/27/2001: Added map functions (and inversion map functions) for Haldane and Kosambi, so that I'm not re-creating them all of the time within functions. Added a function plot.chr() to plot genotypes for a specific chromosome, with likely errors (as determined by calc.errorlod() or find.errors()) highlighted. Added a warning to the help file for argmax.geno. The results greatly depend on the value of the step argument, and may not be terribly trustworthy. Also, if several sequences (of underlying genotypes) are all most likely, our method of randomly choosing among them is not right...recombination events are too far to the right. Version 0.78, 07/24/2001: Fixed a small bug in create.map(), which is used by calc.genoprob(). An error occurred in the case of a genetic map with equally spaced markers, when the argument "step" was set to be exactly the inter-marker distance. Modified calc.genoprob(), calc.argmax(), sim.geno() and calc.errorlod() so that their corresponding components have attributes "error.prob", "step" and "off.end" (only "error.prob" for calc.errorlod()), specifying the corresponding values used in the calculations. Modified calc.errorlod() to re-run calc.genoprob() if the error.prob attribute is different from the corresponding argument. Version 0.77, 06/22/2001: Fixed a small bug in sim.cross(), where dimnames of error component was wrong, when simulating genotyping errors with a QTL present. Version 0.76, 05/17/2001: This is a totally revised version of the package. Most importantly, the data structure for a cross has completely changed. The function convert.cross is included, for converting data from the old structure to the new one. See the help file for read.cross for a description of the new data structure. The main hidden Markov model engine has been rewritten, to make things more flexible and general. We've now implemented the Viterbi algorithm, in the function argmax.geno, to calculate the most likely sequence of underlying genotypes, given the observed marker data, and we've fixed the calculation of the Lincoln and Lander error LOD scores. The analysis of phase-known four-way crosses is now possible. The "singlescan" function (to do a genome scan with a single QTL model) is now called "scanone" (to save a few keystrokes). Note that this function does not yet allow the use of covariates. We'll add that feature in the near future. Saunak Sen and I are now working together on this project, and so things will begin to progress more quickly (we hope). ---------------------------------------------------------------------- End of STATUS.txt qtl/inst/LICENSE.txt0000644000175100001440000000102312135326133013663 0ustar hornikusers The R/qtl package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation. This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details. A copy of the GNU General Public License, version 3, is available at http://www.r-project.org/Licenses/GPL-3 qtl/inst/MQM-TODO.txt0000644000175100001440000000223712135326133014006 0ustar hornikusers "To do" list for MQM part of R/qtl ---------------------------------------------------------------------- This file is intended to contain a list of many of the additions and revisions that are planned for the MQM part of the R/qtl package. If you any additions or revisions to suggest, please send an email to the R/qtl mailing list. ---------------------------------------------------------------------- SHORT TERM: o Tutorial and man pages - fix the bibliography (pjotr) ---------------------------------------------------------------------- MEDIUM-SHORT TERM: o MQM paper - Plot Cis/Trans on geneexpression set from Arabidopsis - Circleplot on the same dataset o Augmentation rewrite to use best N augmentations o Placement of cofactors for automatic backward selection (perhaps use windowsize) ---------------------------------------------------------------------- MEDIUM TERM: o 'Environmental' cofactors (Sex,Block,Age, etc) o Generalized Lineair Models - use R version o Handling of the X-chromosome ---------------------------------------------------------------------- LONG TERM: ---------------------------------------------------------------------- qtl/inst/README.md0000644000175100001440000000466312566656241013352 0ustar hornikusers## R/qtl: A QTL mapping environment [![Build Status](https://travis-ci.org/kbroman/qtl.svg?branch=master)](https://travis-ci.org/kbroman/qtl) **Authors**: Karl W Broman and Hao Wu, with ideas from Gary Churchill and Śaunak Sen and contributions from Danny Arends, Robert Corty, Timothée Flutre, Ritsert Jansen, Pjotr Prins, Lars Rönnegård, Rohan Shah, Laura Shannon, Quoc Tran, Aaron Wolen, and Brian Yandell [R/qtl](http://www.rqtl.org) is an extensible, interactive environment for mapping quantitative trait loci (QTL) in experimental crosses. It is implemented as an add-on package for the freely available and widely used statistical language/software [R](http://www.R-project.org). The development of this software as an add-on to R allows us to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing. A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression. ### License The R/qtl package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation. This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details. A copy of the GNU General Public License, version 3, is available at qtl/inst/INSTALL_ME.txt0000644000175100001440000001305112566656241014312 0ustar hornikusersINSTALL_ME file for the qtl package ---------------------------------------------------------------------- This explains the installation of the qtl package for R. If you have any problems with installation, send an email to Karl Broman . ---------------------------------------------------------------------- OBTAINING R You can download R from the Comprehensive R Archive Network (CRAN). Visit http://cran.r-project.org or a local mirror (for example, http://cran.us.r-project.org). Source code is available for Unix, and binaries are available for Windows, MacOS, and many versions of Linux. OBTAINING R/QTL You can obtain the latest version of R/qtl from http://www.rqtl.org Copies of R/qtl will also be placed on CRAN (cran.r-project.org). Binaries are available for Windows and MacOS; source code is available for Unix. INSTALLATION OF R AND R/QTL (Windows) 1. The Windows version of R is distributed as a single file, with a name something like R-3.2.2-win.exe. Install R by executing this file. We recommend installing R in "c:\R" rather than "c:\Program Files\R". Why didn't Microsoft use "Programs" rather than "Program files"? 2. To install R/qtl, the simplest approach is to start R and type install.packages("qtl") This will download the binary from CRAN and install it. Alternatively, you can download the "qtl_1.37-11.zip" (or the equivalent). Then start R and select (on the menu bar) "Packages" and then "Install package from local zip file...". Find the file "qtl_1.37-11.zip" on your hard drive, and click "Open". INSTALLATION OF R AND R/QTL (MacOS version 10.5 and above) 1. Download the file R-3.2.2.pkg, double-click it, and follow the instructions. 2. To install R/qtl, the simplest approach is to start R and type install.packages("qtl") This will download the binary from CRAN and install it. Alternatively, download the compiled version of R/qtl for Mac OS X, a file like "qtl_1.37-11.tgz". Then start R and select (on the menu bar) "Packages & Data" -> "Package Installer". Select "Local Binary Package" from the drop-down menu at the top of the window that comes up. Click "Install" at the bottom of the window. Find the package on your drive and click "Open". Finally, close the window. INSTALLATION OF R/QTL (Unix) 1. We'll assume that R has already been installed. 2. To install R/qtl, the simplest approach is to start R and type install.packages("qtl") This will download the binary from CRAN and install it. Alternatively, download the R/qtl source code (a file like "qtl_1.37-11.tar.gz"). Go into the directory containing the file and do one of the following: a. To install R/qtl in the standard location (/usr/local/lib/R/library), type R CMD INSTALL qtl_1.37-11.tar.gz You'll probably need to be superuser. b. To install the package locally, type R CMD INSTALL --library=/home/auser/Rlibs qtl_1.37-11.tar.gz (where "/home/auser/Rlibs" should be replaced with the appropriate directory). Create a file ~/.Renviron containing the line R_LIBS=/home/auser/Rlibs so that R will know to search for packages in that directory. GETTING STARTED Once you start R, you'll need to type "library(qtl)" to load the package. You can create a file "~/.Rprofile" (Unix or MacOS) or "c:\.Rprofile" (Windows) containing R code to be run whenever you start R. If you use the R/qtl package regularly, you should place the line "library(qtl)" in such a file. Efficient use of the R/qtl package requires considerable knowledge of the R language. Learning R may require a formidable investment of time, but it will definitely be worth the effort. Numerous free documents on getting started with R are available on CRAN (http://cran.r-project.org). In addition, several books are available. For example, see WN Venables, BD Ripley (2002) Modern Applied Statistics with S, 4th edition. Springer. To get started with R/qtl, you might first peruse the documentation that is bundled with it. Type help.start() to start the html version of the R help pages. Then click "Packages" -> "qtl". In Windows or MacOS, you may gain access to the help documents by clicking "Help" in the menu bar and then "R language (html)". If you include "options(htmlhelp=TRUE)" in your .Rprofile file, use of the html version of the help pages will be automatic. The help file titled "A starting point" gives a brief walk-through of an example analysis, and so is a good place to start. You may also view this help file by typing ?"A starting point" from the command line in R. A tutorial on R/qtl (as a PDF document) is also available. It briefly describes the aims of the R/qtl package, lists the available functions grouped in categories, and provides a few extended examples. The tutorial is bundled with R/qtl, as "rqtltour.pdf" and is also available from the R/qtl website: http://www.rqtl.org CITING R/QTL To cite R/qtl in publications, use Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889-890 QUESTIONS/COMMENTS/CONCERNS If you have any questions, suggestions, problems or complaints regarding R/qtl, please email Karl Broman . ---------------------------------------------------------------------- end of INSTALL_ME.txt qtl/inst/doc/0000755000175100001440000000000012566656320012625 5ustar hornikusersqtl/inst/doc/geneticmaps.pdf0000644000175100001440000614144312422233634015622 0ustar hornikusers%PDF-1.4 %Çì¢ 5 0 obj <> stream xœÍYËn¹Ýë+´€¹m’ÅgV‰‘d 0޳Èd¡—%ÃzY¾’à¿Ïá£o›M¼ ´/Q]¬:uêÁî/ƒ˜ä â_ù~{ô¾¥ÝAŸ¾ÉòC™áý Äü ݤ¤1ÃÉÇ#YÄ¥v“Á–%š¬ôÃÉíÑÆwb$tj¼Œ?È/Âx×ÊH§dµ¿k㤑4~Šk«Œ4žïÈ©É(;Þ&q£ ¹ñôZ”T$LJ(¡£ Æ®´ÁÙñ>é“V 3Ÿ©4_!!C¦>ñ1­•3FŽOÅB!W¸a õpbu99ô·µð†sq®æ:ŠÎ¥ñCÂˉ ï˜Ê/Ñ`Cò`€ ðù&k¤àÍOþŽÈ PRO¤­J¡Ï›agý¤È…áä1ùÇiPy­uöØ…à&´‘ÒSpnü%9o째m³ß§mi%ÖüÉ{¦ñ– @…'ùA ü ¼32šú—“£ŸŽ2q¯ºD“jÀáàLSŒ’“Ð63íç¤_á}9Kvô²ÿÌÖ—lýÈ俲}þìžÉ|;Þ@‹°Ü3‰Ø•jò:£WëhtŸ³õ}Çöž-\fÇöÿÉÖ§l}ÑÁãkkC’¹[öc¬vÆL$ƒ¸…Xý9Kia]…æÛ?mQNû{¶ÛêIkîåþ7ßAü¾ãí¾Ej>gñ¯o9¯å °sü}´KžK¦ðâ ¹‘7Q¹GHÜø·+>v"Ë™~ËÖp¶¤õyÃLéƒU‘!^MXÍAj&‚œtüå~]·4AÜÀ!­o )l.›'=t’챦‚aòjüÝ*Å$S¨ëÌ@2BøaG‡q>û­£Tat¢uâЫD·l}ÆäWÕÊôZg*3—íþlr*¼¹’~wá•híA»Aã?ÙÒâ?tÊÀ3ÛßH¥FþbfõÁ­$"ç—[哺əëø(z¥§7Åeþ°š¾»Qy9i¤E…W¯èÞÖ”0ÐGÚU™ü´]£+§7ªSS ïÝu0]YS6d ©9u›úñ\ó6;BUùè=z·”•ñª¥?¯–qš“ƒ5žìª9ý:vJÂÄö¯ØšïÿÖWä¦ç¾£fæÎÝçæ1d®’¤t¶•ðAáK–îûmü±ÍÆQiýëq ùav{ý– õVN85•“¥’Šº‘êYQñÒkÌY‚"´Ùö2Œ>FAÜäÙ߃+…[Æi%EZRÐËL¼Ø¡íä0œÃ–Ä$:„Zº ‹UÊ›rUÁ” 5€%μFÏQ,ÛM›„M^Ä»ÂN‡.¢bÅ 5X{…Bú%BpÙð¢ðž‰¤31÷›‚„ 2XYE‘W½§|?Ñ¶ÖØÔ‚bb}Œ1“S®ÒÈ=-n8€ËÝàk{•±“Ô¦’Nç\Gdõ ƒ(‚LpÖV®zu]­Ê•›H†4XFw(Á|¥çÙÆ¾6—-,5 d†6{VìÆºŠÊ§ãH4¼„ž‰ÅéqÂÕÔ”s<†ºîË‘pJ¾LEt|0åp[‡r¸w2w³tv—Bp’«G8þà†xòæùÈ# 2›J¨ïõç±£pûÌT—É ›ôúÒpSÓ~hBk笅N-cjÛUŒáäÛ\á· ]ðnŽ‚*¾a·qL«aÅßt¦—r‹{i¹bû]J[2ëƒ0’—óéBå(¬ÏÔ¨W ¦¼¤bçÅw">ä›bƒÀ‡¯‹ÊT–6…,S™Ȭ[Õ1&h­«ÈÎ6y"=úfïLãË줴Mpàƒ®£ÍmÝçTSh|jδӈ‹ä}>uqj‹£‰è7 _¹ÈY:M.7Lº8¢ŒJÝzp¨ñß‹mOM)÷Ž'ø6Ë–)î†o'·1*ÎÓC±õ-ÆÅMNºj»“ý|Íœg!ÊÉš­sõ:7¾`êD„é„ÁV‚ig¥d×´¨ãa‚P±y”D€=Ë7¾.šôr ¤×u ˜M™_”m }¶[øJõõªËl+üÿè˳›|ûc¶Q}KšoÌ+Í¡éÍ*³~e^9t!îôqtÂÅßÞjùƒ9[eˆo=WqËŽƒ­ 7Îqž²æ|UX¥9 ²nZ žY­Êù–qHD^?r~*/tÔB•\×]x›O ²¼‘KcçÖ»~NÈZ‘KFÎÆSc¾Á³$ÓƒzÕKIëIÝmee«r”H:_§|¬˜²1Œ¯ZlòÕ¹2m¸UjñFž%¼©ãv±Õ`"Ýã«j$µÑ>U LVS Ð›ýîS«°¸1<³ÖÅ­¼X:ARg0©õ O1vV—;OrIPw¼ìÈå_2þÖ©&ç(†Ë*yVLƒM2½}íxžS×$s˜QW™GðåëŒ7ÛÚ^Rp¬Ý<-‹}¯$p÷8ð4ûE¶z®gêy ýºí²RÖùTžBaXQµ½Ìð%U3¨uªòÜˑ¼ž µÂ}Ó¡í.œ©¯$±~‰X૱—áœIË6û£ãK«¿ùÔË¿;ð·ó«oO€Þî‡ý-¼%ª´ ;²C¹Pæðrü§£ÿCEa¢endstream endobj 6 0 obj 2399 endobj 23 0 obj <> stream xœÅ=IÞFvw!ÈoÐñë@M³ö*çd‚98p0GH39HjIíŒÔjk±¢ÉŸÏ[Šä«{q6`š]_-¯Þ¾ñ—§ó¤žÎøOþï«÷O¾û)<}ûé ½}ªžþåÉ/OTþŸ9yú»ç0LÙ§JMÉ9õôù›'*þõÓlãÓçïŸüéôÃÅå<Ù£âéæÖqÆuúŒ¯µŸM0§øzNÁ»Ó‡‹KÃggOWÌÒº77ǸíB^^\Âàf'ËÃ6{c˜É!ÕÞÖuø(:Îåö¯x!«Âé+áVÔ¦ªÜì5ŽV“OöžWI³ü|(fà¬Z9 „çW€ü¯pj@X¥Oï/.PEò‰«Ø•?Ýæ­ZÕ ‹g\PÉ«>Šç/Û½ÚG;|»<¥xzÛòqÒ@‡r·Ÿx·æó’hë“àAx\.¹TiÃE¢’3ðB^‡a/¯J¢±ó’ΩM,‡’ÙIÃÜnS➸¡Š.­uSœ}AŠyBçÇ!p úÑÍi´d…×ÖúÉYD„õÚ$† HyoR—į",(÷wµÁA®ø)oÛÆ ÙåUY€Sj¹øõ…†wì›XP"ÞˆiÉËyǃ›¨ÙÌnM…íò—%M8ÑÚ¬§•Òè¸fr©D$Á¯ø¸a•ŒÒ’t®ºxÌÜn)†â—oxBtÓøõýDÒâŠiaÄ-äfÿBLi¨Ã ±^ÚÄËL,Ó%¯¤Ê'`¹äüP-K)x¾!LÑ‚ÙÀÜ]@ºï Ò¨ÖÅpžP/ƒ%¡}lß/ÈÞ]¨ 0$j[- ¯A@ÁY4^b *I:)ç§ó S`òù² (±s¡µÖË÷*i”–ÌŽ°¨lŠÉs|ÌW­‰·“`ê´û3بӟOD–0ádÂB—Ä¡Ñ:@ìÓóMÿH 6 Þþù"¯çF÷"‰î¼ØÁ3â/m0CB—ô*¯úf›ñç` BH"_?ÃE’ƒT™˜{;øûW¼j­ª­fEÊ6Ú׺Óg+{ ²ÍÀ-éV²Æ7ccôzíÆ¹Pñ¤Kmü¤J<ÖÔT*}Ç:¸¦¿käÓBÊ’ò´A–P*­·¬FG¼ñŠ—IGÔ‘$b1ÚI– ¿܈ºÒb“е’°ŒV®Ý S{y`¤ö9¸áÔ|y:˜Tß1«ÑðÀ¤ĨdK°É“É Å>0ͦ€*èB´h·›Š}Ýç~¯p zMhá©àˆ-e®‚œ±å/ÈÈøùÿ¨^_p´N*0Ñfè2Õ:Ã@îܪv¤´õNòb{|<8h2ËâÉoy„Öá Ô„‘I7Þ™°älj³LÞ(ÂÐ-Â>’·6– óeO£Ï¯×–Ì©3¼–Òïpu5áB¿‰.‹Ç˜ I`“Ó¡äètx0Ë*𬠚 á @ýöd^úÝ(zùe{\æDYÄê(=¶x=Ò[¤ñ1Æ‚ *6§ÉB-_wt*_’Pj9Ó8[:@¶5‘Mò¯t¬<]Ÿ²!Ðì“5Ò!ñq€òšå›ÁržM%Öº‚@0-Ò¨¯³Cm¸Yt„ÝQ4Í}žÙ!Ö#AIYEÒ _Hªlö7|Ð$æ»Y”,f Œ†tĨÈ0 0&þ¯,¤Ðè¥ëUÊk•“K­S`û;ñúÛEÚà ƒn¨]œR(¼•w©‡šÏ&½”UW)wÌ.rˆ.‹–¡TåžܘþŽNÒÒ§0t¶ðµ¸ÙÇÑ$óÖ í1ÿˆ2Ó¤m‡Ç.2Tb nŒ›u³’ $b“ÑÆÅ-Á—ûzY\È7zëèzŽúi’W-†4¤y»mü˜pƒ ¸ÉëEÑ43Œ3v¡j$ß”&›ì3‚#$À°ò`Ý{ºÞ<˜Y<£7Aœý㘣“‡ Pº V Dûn#ðèåöʯÂQBôC5ò˶»Áu4&B—D –Và•×$e/z=ŒB,¶Ä^8ä<±”Úèñ¨uü ïا)~íkrøWžyN±–x„9ª”²^M8ÇÐéJ¸c+çˆp>eðUý@ü·2î*£QPT„½>‚hN…>ú_»àîD.#=MÈJÂzíºô®ìfÁkA±‰xi Â~àCÍqlæÈç<¡u…?ûvsÔ|Î{ò¥KDá‘¢"ð{Bn„]Ò¨\¯íEð˜{j¯e>ËÅb]A"ük&6çQ¿Yˆí+Ãæ]÷¥ƒœZ¾Ùäf–\©üI¥Qš¿7›zò6C:fþÃJ«ÜjIaH¢\‡i#ŸåÎ"éˆÚØ*ÍŠ;™è`áÖŽŽ/€‰Å4®Iîˆ#ááˆ2rˆœ±E%ª—‰V­^ÚL‚U"<³x}§ïÄndB®„ˆoÐi¿½ÿýÖªÙò©¼è½RÎèÈZG¢qÏ϶眰‹„„X¶ ívÊ›!c‚òoïfF#B¾¾¡gÎ yÃïá/J§C;kôLÊ=`†S@ÉClX” )oMFµ rHaŽpc0Ä3§^ÚITðÇUÆ*A±Q<¿ ¥8’Äóÿ3‹g ðϰÒƒòÙˆç\7Æ”PSÒåW©2 ¥Ö ÖE—ßüÐ×™N](cŒòùVàé÷âýwâýwâý×ö™ÆÈ÷“x/™Ò/­ £÷ïÄû©”æë³¤§·ÛsA”"i´@d³£@³`;’(?²‹Ò«ønÍSŒä5¤ø[^Së¿™—¨V¼Ð‰Šn'ãÒé?/Vú½î;*ÃÃ?°#åÜýÌæZ«Ì^¹ÄI!¼ÌNô"¨¸;³¢Àª|ãë9TÒ€½ÄŽ¿’U :™Òz~WR¶utÇT¹‚¶c€* Sį7_oe›Àq ìK£ë-NÀ+¬¼C8®,Ü~æ»ÄiÂV‘t¶Ò¹îW4ÆÙêJ"ý#|}ƧKF¾ðgJ[£ UáÄÁQ ].•jO7åÖ`1ܪAÊM¤Þ½ìmܬA‹:Ö‚W¹sg‚ Ôk?Ô„Õ^ȈlFnUºâDáåÊä\ú,³`q’ÊåÕË•®6Š1ˆOyÑTñ§*Ý©Y¦³<ÓŒàzÈ#Vrõ’,³UÈR¨#•±D¾×3n2}ØM†¡Ì]ÓåZßbaࢄ-$tö›D?F7[¸+HJ_Ö±<£eHˆ2üÆšð(Q«Š&®IB~urÞ("ì& —¡§A­ÉìY Ðd|Ï$4›”úi L+©)9Ä4V8'{½“@x»…5FaεD[ Ÿâôx«RÍé@ûîaabÇ·×ÄG)6£4¥%k?ÙÚ9‹b¾õè—µ ŽBÍÛhn=ŽåtT¨ÆíZÒ/shq5¼ ŸÁm_q;ËPkå•ö"Ç‚ˆ³Ô*&¯kr·Æ‚BSDfä/7÷Þæ–ií’Ã<è£<òä=¹PJêçÁ÷ûi•åQXU¾G‹“h`7ì:"ÿÔ[ÝТ5À”4`%©<»¥6`F´v¦p·œà#§þcfC[Ò÷ñû²/Þ:'HU´â®©µ"ôSå„IÝÕ‡>è¥ [ç!‘æÉ)¹¾mL]%å ÈJ™+wék&Ah9PdA÷¨2?ðMÛÈÿ³ÐÈÿ×¾®’©“¡7ôc·Y5ôúåöXqä…@ÿˆÞŒrŠn&Ø8:!ŽO<·Ó¦¶Ê­ºóLIðh’QÒMÿîßñ{ ^û ñGÉ%}£! 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0001590004 00000 n 0001598159 00000 n 0001601153 00000 n 0001608790 00000 n 0001567464 00000 n 0001567990 00000 n 0001568320 00000 n 0001568802 00000 n 0001569893 00000 n 0001570543 00000 n 0001571096 00000 n 0001615530 00000 n trailer << /Size 273 /Root 1 0 R /Info 2 0 R /ID [] >> startxref 1617201 %%EOF qtl/inst/doc/Sources/0000755000175100001440000000000012422233634014236 5ustar hornikusersqtl/inst/doc/Sources/new_multiqtl.Rnw0000644000175100001440000007167612422233634017473 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Karl W. Broman % The new multiple-qtl mapping functions % % This is an "Sweave" document; see the corresponding PDF. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass[12pt]{article} \usepackage{times} \usepackage{amsmath} \usepackage{color} % revise margins \setlength{\headheight}{0.0in} \setlength{\topmargin}{-0.25in} \setlength{\headsep}{0.0in} \setlength{\textheight}{9.00in} \setlength{\footskip}{0.5in} \setlength{\oddsidemargin}{0in} \setlength{\evensidemargin}{0in} \setlength{\textwidth}{6.5in} \setlength{\parskip}{6pt} \setlength{\parindent}{0pt} \newcommand{\code}{\texttt} \newcommand{\lod}{\text{LOD}} % make "Figure" within figure captions a small font \renewcommand{\figurename}{\small Figure} \begin{document} \SweaveOpts{prefix.string=Figs/scantwo,eps=TRUE} \setkeys{Gin}{width=5in} %% <- change width of figures % Try to get the R code from running into the margin <>= options(width=87, digits=3, scipen=4) @ % function (to be used later) to round numbers in a nicer way. <>= source("myround.R") @ % Change S input/output font size \DefineVerbatimEnvironment{Sinput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0, 0, 0.56}}} \DefineVerbatimEnvironment{Soutput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0.56, 0, 0}}} \textbf{\large New functions for exploring multiple-QTL models} \\ Karl W Broman, 7 February 2008 \\ (minor revisions 28 May 2008; further revised 18 July 2008 to discuss \code{stepwiseqtl}; added color 26 Oct 2010) \bigskip R/qtl version 1.08 included a number of new functions to simplify the exploration of multiple-QTL models. In R/qtl version 1.09, the function \code{stepwiseqtl} was added to perform a stepwise selection to identify the multiple-QTL model optimizing a penalized LOD score. The ``Brief tour of R/qtl'' document contains a brief description of these functions (see Example 5 in that document); here, we provide a more thorough explanation. \bigskip \textbf{\large Introduction} \nopagebreak Let us begin with a brief overview of the changes. The basic functions remain \code{makeqtl} (for creating a QTL object), \code{fitqtl} (for fitting a defined multiple-QTL model) and \code{scanqtl} (for multi-dimensional scans with a multiple-QTL model). Previously, \code{fitqtl} and \code{scanqtl} used multiple imputation exclusively. We have now implemented Haley-Knott regression as well. To use Haley-Knott regression, use the argument \code{what="prob"} in a call to \code{makeqtl} and then \code{method="hk"} in calls to \code{fitqtl} and \code{scanqtl} (and the other functions, to be described shortly). The \code{scanqtl} function, while completely flexible and so suitable for most purposes, is rather cumbersome to use. Our most important additions are the functions \code{addqtl}, to scan for a single QTL to be added to a multiple-QTL model, and \code{addpair}, to scan for a pair of QTL to be added to a multiple-QTL model. The output of these functions is of the forms produced by \code{scanone} and \code{scantwo}, respectively, and so one may use the corresponding plot and summary functions, making the results easier to deal with. For most purposes, these functions will be sufficient and direct calls to \code{scanqtl} will no longer be needed. Thus, we will not discuss the use of \code{scanqtl} further here. The next important addition is \code{refineqtl}, which uses an iterative algorithm to refine the locations of QTL in a multiple-QTL model, with the aim of obtaining the maximum likelihood estimates of the QTL positions. If the function is called with \code{keeplodprofile=TRUE} (which is the default), one may use another new function, \code{plotLodProfile}, to plot the LOD profiles for each QTL, in the context of the multiple-QTL model, as is commonly used in multiple interval mapping. The function \code{addint} may be used to test all possible pairwise interactions among the QTL in a multiple-QTL model. We added several functions for manipulating a QTL object (created by \code{makeqtl}). The function \code{addtoqtl} is used to add additional QTL to an object, \code{dropfromqtl} is used to remove QTL from an object, \code{replaceqtl} is used to move a QTL to a new position, and \code{reorderqtl} is used to change the order the loci within the QTL object. Finally, the function \code{stepwiseqtl} may be used to perform a stepwise search for the multiple-QTL model optimizing a penalized LOD score criterion. The function \code{calc.penalties} can be used to calculate the penalties for \code{stepwise}, using the output of a permutation test with \code{scantwo}. \bigskip \textbf{\large \code{makeqtl} and \code{fitqtl}} \nopagebreak We'll look at the \code{hyper} data as an example. These data are from Sugiyama et al. (Genomics 71:70--71, 2001), and concern blood pressure in 250 male backcross mice. We'll use multiple imputation (the default), as Haley-Knott regression performs poorly in the case of selectively genotyping, which was used for the \code{hyper} data. First we need to load the package and the data. <>= library(qtl) data(hyper) @ We will use the multiple imputation approach, and so we first run \code{sim.geno} to perform the imputations. We'll use 128 imputations; this is insufficient for the current data, which has extensive missing genotype information, but suffices to illustrate the methods. In practice, it is a good idea to repeat the analysis with independent imputations. If the results are much changed, increase the number of imputations. <>= hyper <- sim.geno(hyper, step=2, n.draws=128, err=0.001) @ <>= file <- "Rcache/simgeno.RData" if(file.exists(file)) { load(file) } else { set.seed(94743379) <> save(hyper, file=file) } @ Results of \code{scanone} and \code{scantwo}, which we won't revisit, indicated QTL on chromosomes 1, 4, 6 and 15, with an interaction between the QTL on chr 6 and 15, and the possibility of a second QTL on chr 1. We will begin by fitting this 4-QTL model. The function \code{makeqtl} is used to create a QTL object; it pulls out the imputated genotypes at the selected locations. <>= qtl <- makeqtl(hyper, chr=c(1, 4, 6, 15), pos=c(67.3, 30, 60, 17.5)) @ Note that if you type the name of the QTL object, you get a brief summary. <>= qtl @ Also, there is a plot function for displaying the locations of the QTL on the genetic map. See Fig.~\ref{fig:plotqtl}. <>= plot(qtl) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,4.1,0.1), cex.axis=0.8, cex.main=1, cex=0.7) <> @ \caption{\small Locations of the QTL object \code{qtl} on the genetic map for the \code{hyper} data.\label{fig:plotqtl}} \end{figure} We may now use \code{fitqtl} to fit the model (with QTL in fixed positions). (In previous versions of R/qtl, \code{fitqtl} required a column of phenotypes, rather than a cross object, but this has been revised to make calls to \code{fitqtl} more like those to \code{scanone}, \code{scantwo}, and \code{scanqtl}.) Note that we use \code{Q3*Q4} to indicate that QTL 3 and 4 should interact. We could also have written the formula as \verb|y~Q1+Q2+Q3+Q4+Q3:Q4|. See the help file for \code{formula} for more information. <>= out.fq <- fitqtl(hyper, qtl=qtl, formula=y~Q1+Q2+Q3*Q4) summary(out.fq) @ The initial table indicates the overall fit of the model; the LOD score of 21.8 is relative to the null model (with no QTL). In the second table, each locus is dropped from the model, one at a time, and a comparison is made between the full model and the model with the term omitted. If a QTL is dropped, any interactions it is involved in are also dropped, and so the loci on chr 6 and 15 are associated with 2 degrees of freedom, as the 6$\times$15 interaction is dropped when either of these QTL is dropped. The results indicate strong evidence for all of these loci as well as the interaction. \bigskip \textbf{\large \code{refineqtl} and \code{plotLodProfile}} \nopagebreak Let us explore the use of \code{refineqtl}, which allows us to get improved estimates of the locations of the QTL. We use \code{verbose=FALSE} to suppress the display of tracing information. <>= rqtl <- refineqtl(hyper, qtl=qtl, formula=y~Q1+Q2+Q3*Q4, verbose=FALSE) @ <>= file <- "Rcache/refineqtl.RData" if(file.exists(file)) { load(file) } else { <> save(rqtl, file=file) } @ The output is a modified QTL object, with loci in new positions. We can type the name of the new QTL object to see the new locations. <>= rqtl @ The loci on chr 1 and 4 changed position very slightly. Let us use \code{fitqtl} to assess the improvement in fit; we'll skip the drop-one analysis. <>= out.fq2 <- fitqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4, dropone=FALSE) summary(out.fq2) @ The LOD score comparing the full model to the null model has increased by \Sexpr{myround(out.fq2[[1]][1,4] - out.fq[[1]][1,4],1)}, from \Sexpr{myround(out.fq[[1]][1,4],1)} to \Sexpr{myround(out.fq2[[1]][1,4],1)}. If \code{refineqtl} is run with the argument \code{keeplodprofile=TRUE} (which is the default), the LOD traces at that last iteration are saved, which can then be plotted with \code{plotLodProfile}, as follows. See Fig.~\ref{fig:lodprofile}. <>= plotLodProfile(rqtl) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,4.1,0.1), cex=0.8) plotLodProfile(rqtl) @ \caption{\small LOD profiles for a 4-QTL model with the \code{hyper} data.\label{fig:lodprofile}} \end{figure} The LOD profiles in Fig.~\ref{fig:lodprofile} are similar to the usual LOD curves, but instead of comparing a model with a single QTL at a particular position to the null model, we compare, at each position for a given QTL, the model with the QTL of interest at that particular position (and with the positions of all other QTL fixed at their maximum likelihood estimates) to the model with the QTL of interest omitted (and, again, with the positions of all other QTL fixed at their maximum likelihood estimates). For the loci on chromosomes 6 and 15, the 6$\times$15 interaction is omitted when either of the two loci is omitted. Note that maximum LOD for each of the LOD profiles should be the value observed in the drop-one analysis from \code{fitqtl}. These profile LOD curves are useful for the assessment of both the evidence for the individual QTL and the precision of localization of each QTL, but note that they fail to take account of the uncertainty in the location of the other QTL in the model. \bigskip \textbf{\large \code{addint}} \nopagebreak The function \code{addint} is used to test, one at a time, all possible pairwise interactions between QTL that are not already included in a model. For our model with loci on chr 1, 4, 6 and 15, and with a 6$\times$15 interaction, we will consider each of the 5 other possible pairwise interactions, and will compare the base model (with the four QTL and just the 6$\times$15 interaction) to the model with the additional interaction included. The syntax of the function is similar to that of \code{fitqtl}. The output is a table of results similar to that provided by the drop-one analysis of \code{fitqtl}. <>= addint(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4) @ None of the interactions is particularly interesting. Note the difference in the results if we use as the formula \code{y~Q1+Q2+Q3+Q4} (that is, omitting the 6$\times$15 interaction). <>= addint(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3+Q4) @ The 6$\times$15 interaction is also tested, and the LOD scores for the other interactions are somewhat different, as they concern comparisons between the 4-locus additive model and the model with that one interaction added. \bigskip \textbf{\large \code{addqtl}} \nopagebreak We may use the \code{addqtl} function to scan for an additional QTL, to be added to the model. By default, the new QTL is strictly additive. <>= out.aq <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4) @ <>= file <- "Rcache/addqtl.RData" if(file.exists(file)) { load(file) } else { <> save(out.aq, file=file) } @ The output of \code{addqtl} has the same form as that from \code{scanone}, and so we may use the same summary and plot functions. For example, we can identify the genome-wide maximum LOD score with \code{max.scanone}. <>= max(out.aq) @ And we may plot the results with \code{plot.scanone}; see Fig.~\ref{fig:addqtl}. <>= plot(out.aq) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,4.1,0.1)) <> @ \caption{\small LOD curves for adding one QTL to the 4-QTL model, with the \code{hyper} data.\label{fig:addqtl}} \end{figure} We may also use \code{addqtl} to scan for an additional QTL, interacting with one of the current loci. This is done by including the additional QTL in the model formula, with the relevant interaction term. For example, let's scan for an additional QTL interacting with the chr 15 locus. <>= out.aqi <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4+Q4*Q5) @ <>= file <- "Rcache/addqtlint.RData" if(file.exists(file)) { load(file) } else { <> save(out.aqi, file=file) } @ We plot the results as follows; see Fig.~\ref{fig:addqtlint}. <>= plot(out.aqi) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,4.1,0.1)) <> @ \caption{\small LOD curves for adding one QTL, interacting with the chromosome 15 locus, to the 4-QTL model, with the \code{hyper} data.\label{fig:addqtlint}} \end{figure} Also of interest are the LOD scores for the interaction between the chr 15 locus and the new locus being scanned, which are the differences between the LOD scores in \code{out.aqi} and \code{out.aq}. See Fig.~\ref{fig:addqtlint2}. <>= plot(out.aqi - out.aq) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,4.1,0.1)) <> @ \caption{\small Interaction LOD curves in the scan for an additional QTL, interacting with the chromosome 15 locus, to the 4-QTL model, with the \code{hyper} data.\label{fig:addqtlint2}} \end{figure} There is nothing particularly exciting in either of these plots. \bigskip \textbf{\large \code{addpair}} \nopagebreak The function \code{addpair} is similar to \code{addqtl}, but performs a two-dimensional scan to seek a pair of QTL to add. By default, \code{addpair} performs a two-dimensional scan analogous to that of \code{scantwo}: for each pair of positions for the two putative QTL, we fit both an additive model and a model including an interaction between the two QTL. Recall that in the single-QTL analysis with the \code{hyper} data, there were two peaks in the LOD curve on chromosome 1, which indicates that there may be two QTL on that chromosome. In the context of our multiple-QTL model, the LOD profile on chromosome 1 (see Fig.~\ref{fig:lodprofile}) still shows two peaks, though the distal one is more prominent. We may use \code{addpair} to investigate the possibility of a second QTL on chromosome 1. To do so, we omit the chr 1 locus from our formula, and perform a two-dimensional scan just on chromosome 1. <>= out.ap <- addpair(hyper, qtl=rqtl, chr=1, formula=y~Q2+Q3*Q4, verbose=FALSE) @ <>= file <- "Rcache/addpair.RData" if(file.exists(file)) { load(file) } else { <> save(out.ap, file=file) } @ The output is of the same form as that produced by \code{scantwo}, and so we may use the same summary and plot functions. <>= summary(out.ap) @ There is little evidence for an interaction, and the LOD score comparing the model with two additive QTL on chr 1 to that with a single QTL on chr 1 is \Sexpr{myround(summary(out.ap)[[11]],2)}, indicating relatively weak evidence for a second QTL on chr 1. Let us also plot the results. We'll focus on the evidence for a second QTL on the chromosome, displaying $\lod_{fv1}$ (evidence for a second QTL, allowing for an interaction) and $\lod_{av1}$ (evidence for a second QTL, assuming to two are additive). See Fig~\ref{fig:addpair}. <>= plot(out.ap, lower="cond-int", upper="cond-add") @ \begin{figure} \centering <>= plot(out.ap, lower="cond-int", upper="cond-add", layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) @ \caption{\small Results of a two-dimensional, two-QTL scan on chromosome 1, in the context of a model with additional QTL on chr 4, 6 and 15, and a 6$\times$15 interaction, with the \code{hyper} data. $\lod_{fv1}$ is in the lower-right triangle, and $\lod_{av1}$ is in the upper-left triangle. \label{fig:addpair}} \end{figure} There is a good deal of flexibility in the way that \code{addpair} may be used. As in \code{addqtl}, where one can scan for loci that interact with a particular locus in the model, we can use \code{addpair} to scan for an additional pair, with any prespecified set of interactions. For example, we may retain the loci on chromosomes 1, 4 and 6, and scan for an additional pair of interacting loci, one of which also interacts with chromosome 6. This would be useful for assessing evidence for an additional QTL interacting with the chromosome 15 locus, but allowing the location of the locus on chromosome 15 to vary. We use the formula \verb|y~Q1+Q2+Q3+Q5*Q6+Q3:Q5|, as we will omit the chr 15 locus (\code{Q4}), scan for an additional interacting pair (\code{Q5*Q6}), and allow the first QTL in the additional pair to interact with the chr 6 locus (\code{Q3}). Note that the positions of the chr 1, 4 and 6 loci are assumed known. A three-dimensional scan could be performed with the \code{scanqtl} function, but we won't try that here. To save time, we will focus just on chromosomes 7 and 15. <>= out.ap2 <- addpair(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3+Q5*Q6+Q3:Q5, chr=c(7,15), verbose=FALSE) @ <>= file <- "Rcache/addpair2.RData" if(file.exists(file)) { load(file) } else { <> save(out.ap2, file=file) } @ Because we are using a special formula here, with one of the new QTL interacting with the chr 6 locus, the results are similar to, but not quite the same as, those from \code{scantwo}. Rather than fitting an additive and an interactive model at each pair of positions, we fit just the single model specified in the formula. And note that as the formula is not symmetric in \code{Q5} and \code{Q6}, we must do a full 2-dimensional scan, and not just on the triangle. (That is, we need \code{Q5} and \code{Q6} assigned to chromosomes (7,15) as well as (15,7).) The summary of the results are somewhat different here. For each pair of chromosomes, a set of three LOD scores are presented. \code{lod.2v0} compares the full model to the model with neither of the two new QTL included, \code{lod.2v1b} compares the full model to the model with the first of the two new QTL omitted, and \code{lod.2v1a} compares the full model to the model with the second QTL omitted. When a QTL is omitted, any interactions involving that QTL is also omitted. <>= summary(out.ap2) @ Note that, because of the lack of symmetry in the formula we used in \code{addpair}, separate results are provided for the two cases \code{c7:c15} (in which the chr 7 locus interacts with the chr 6 locus) and \code{c15:c7} (in which the chr 15 locus interacts with the chr 6 locus). The \code{c15:c7} row is most interesting, but \code{lod.2v1a} is \Sexpr{myround(summary(out.ap2)[3,7],2)}, indicating little evidence for a chr 7 locus. (Note that \code{lod.2v1a} here concerns both the chr 7 locus and the 7$\times$15 interaction.) With this sort of \code{addpair} output, the \code{thresholds} argument should have length just 1 or 2 (which is different from the usual case for \code{summary.scantwo}). Rows will be retained if \code{lod.2v0} is greater than \code{thresholds[1]} and either of \code{lod.2v1a} or \code{lod.2v1b} is greater than \code{thresholds[2]}. (If a single thresholds is given, we assume that \code{thresholds[2]==0}.) Note that, of the other arguments to \code{summary.scantwo}, all but \code{allpairs} is ignored. The plot of the output from \code{addpair}, in the case of a special formula, is also different from the usual \code{scantwo} plot. <>= plot(out.ap2) @ \begin{figure} \centering <>= plot(out.ap2, layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) @ \caption{\small Results of a two-dimensional, two-QTL scan on chr 7 and 15, in the context of a model with additional QTL on chr 1, 4, and 6, with the \code{hyper} data. The two QTL being scanned were allowed to interact, and the first of them interacts with the chr 6 locus. The LOD scores displayed are for the 5-QTL model relative to the 3-QTL model. The x-axis corresponds to the first of the new QTL (which interacts with the chr 6 locus); the y-axis corresponds to the second of the new QTL.\label{fig:addpair2}} \end{figure} The plot, shown in Fig.~\ref{fig:addpair2}, contains LOD scores comparing the full 5-QTL model to the 3-QTL model (having loci on chr 1, 4 and 6). The x-axis corresponds to the first of the new QTL (\code{Q5}), which is the one that interacts with the chr 6 locus. The y-axis corresponds to the second of the new QTL (\code{Q6}). Clearly, the QTL interacting with the chr 6 locus wants to be on chr 15. Note that the \code{lower} and \code{upper} arguments to \code{plot.scantwo} are ignored in this case. \bigskip \textbf{\large \code{addtoqtl}, \code{dropfromqtl}, and \code{replaceqtl}} \nopagebreak Our analysis of the \code{hyper} data, above, did not indicate much evidence for any further QTL. If we had seen evidence for additional loci, we would want to add them to the QTL object and repeat our explorations with \code{fitqtl}, \code{addint}, \code{addqtl}, and \code{addpair}. The functions \code{addtoqtl}, \code{dropfromqtl} and \code{replaceqtl} can be used to facilitate such analyses. Rather than re-creating the QTL object from scratch with \code{makeqtl}, one can use \code{addtoqtl} to add an additional locus to a QTL object that was previously created. For example, if we were satisfied with the evidence for an additional QTL on chr 1, it could be added to the QTL object \code{rqtl} as follows. <>= rqtl <- addtoqtl(hyper, rqtl, 1, 43.3) rqtl @ The syntax of \code{addtoqtl} is much like that of \code{makeqtl}, though one also provides the QTL object to which additional QTL are to be added. If we want to move the first QTL on chromosome 1 to a different position (say to 73.3 cM rather than 67.8 cM), we may use \code{replaceqtl}, as follows. <>= rqtl <- replaceqtl(hyper, rqtl, 1, 1, 73.3) rqtl @ If we wish to reorder the QTL (e.g., according to their map positions), we may use \code{reorderqtl}, as follows. The argument \code{neworder} is to indicate the new order for the QTL. If missing, the QTL will be ordered by chromosome and position within a chromosome. <>= rqtl <- reorderqtl(rqtl, c(5,1:4)) rqtl @ Finally, \code{dropfromqtl} is used to drop a locus from a QTL object. <>= rqtl <- dropfromqtl(rqtl, 2) rqtl @ \bigskip \textbf{\large \code{stepwiseqtl}} \nopagebreak With the function \code{stepwiseqtl}, one may perform a forward/backward stepwise search algorithm find the multiple-QTL model optimizing a penalized LOD score criterion. The penalized LOD score for a model is the LOD score comparing the model to the null model (with no QTL), with a penalty subtracted for each main effect and separate penalties subtracted for each pairwise interactions among QTL. We consider models with possible pairwise interactions among QTL but no higher-order interactions allowed. A hierarchy is enforced in which the inclusion of an interaction requires the inclusion of each of the corresponding main effects. Such a model may be represented by a graph in which vertices (dots) represent QTL and edges (line segments between the dots) represent interactions between QTL. In the penalized LOD score considered by \code{stepwiseqtl}, we allow two penalties on interactions, a light penalty and a heavy penalty. Each disconnected component of a model is allowed one light interaction penalty; all other interactions are assigned the heavy penalty. The three penalties may be calculed from permutation results with \code{scantwo}, using the function \code{calc.penalties}. We will use default penalties derived by computer simulation: (2.69, 2.62, 1.19) for a mouse backcross, or (3.52, 4.28, 2.69) for a mouse intercross. (The penalties are in the order (main, heavy interaction, light interaction).) First, let us apply \code{stepwiseqtl}, considering only additive QTL models (with \code{additive.only=TRUE}. The algorithm performs forward selection up to a model with a given number of QTL (specified by the argument \code{max.qtl}; we'll use 6), followed by backward elimination. <>= stepout1 <- stepwiseqtl(hyper, additive.only=TRUE, max.qtl=6, verbose=FALSE) @ <>= file <- "Rcache/stepqtl1.RData" if(file.exists(file)) { load(file) } else { <> save(stepout1, file=file) } @ The output is a QTL object; type the name to view the chosen model. <>= stepout1 @ We obtain a model with two QTL, with one on each of chr 1 and 4. Now let's re-run the analysis, allowing for the possibility of interactions among the QTL. If \code{stepwiseqtl} is called with \code{keeptrace=TRUE}, the sequence of models from the stepwise selection is retained as an attribute. <>= stepout2 <- stepwiseqtl(hyper, max.qtl=6, keeptrace=TRUE, verbose=FALSE) @ <>= file <- "Rcache/stepqtl2.RData" if(file.exists(file)) { load(file) } else { <> save(stepout2, file=file) } @ The chosen model contains QTL on chr 1, 4, 6 and 15, with the QTL on 6 and 15 interacting. <>= stepout2 @ Since we called \code{stepwiseqtl} with the argument \code{keeptrace=TRUE}, the sequence of models visited by \code{stepwiseqtl} are retained as an \emph{attribute} (called \code{"trace"}) of the output, \code{stepout2}. Attributes are a way of hiding additional information within an object. The entire set of attributes for an object may be obtained with the \code{attributes} function. It is often useful to just look at the names of the attributes. <>= names(attributes(stepout2)) @ Individual attributes may be obtained with the \code{attr} function. So we can pull out the trace of models with the following. This is a long list, with each component being a compact representation of a QTL model, and so we will print just the first of them. <>= thetrace <- attr(stepout2, "trace") thetrace[[1]] @ It is nicer to look at a sequence of pictures rather than a long list of models. The function \code{plotModel} may be used to plot a graphical representation of a model, with nodes (i.e., dots) representing QTL and edges (i.e., line segments connecting two nodes) representing pairwise interactions among QTL. The argument \code{chronly} is used to print just the chromosome ID for each QTL (rather than chromosome and position). The penalized LOD score for each model is saved as an attribute, \code{"pLOD"}; we include them in the title of each subplot, but this requires another call to \code{attr}. <>= par(mfrow=c(4,3)) for(i in seq(along=thetrace)) plotModel(thetrace[[i]], chronly=TRUE, main=paste(i, ": pLOD =", round(attr(thetrace[[i]], "pLOD"), 2))) @ \begin{figure} \centering <>= par(mar=c(0.6,0.1,2.1,0.6)) <> @ \caption{The sequence of models visited by the forward/backward search of \code{stepwiseqtl}, with the \code{hyper} data.\label{stepwiseqtltrace}} \end{figure} As seen in Fig.~\ref{stepwiseqtltrace}, our chosen model is identified immediately (at step 4). Note that the model at step 3 (with additive QTL on chr 1, 4 and 6) has a lower penalized LOD score than the model at step 2 (with just the chr 1 and 4 QTL), but then the inclusion of the chr 15 QTL and the 6 $\times$ 15 interaction gave the largest penalized LOD score, among all models visited. With the addition of a QTL on chr 5 (at step 5), the pLOD decreased somewhat; the LOD score for the model increased, but not as much as the main effect penalty. \end{document} qtl/inst/doc/Sources/rqtltour.tex0000644000175100001440000022102712422233634016660 0ustar hornikusers\documentclass[10pt,letterpaper]{article} \usepackage{times} % times font \usepackage{color} % getting colored text \usepackage{amsmath} \usepackage{hyperref} % revise margins \setlength{\headheight}{0.0in} \setlength{\topmargin}{-0.25in} \setlength{\headsep}{0.0in} \setlength{\textheight}{9.5in} \setlength{\footskip}{0.35in} \setlength{\oddsidemargin}{-0.25in} \setlength{\evensidemargin}{-0.25in} \setlength{\textwidth}{7.0in} %\setlength{\parindent}{0pt} %\setlength{\parsep}{12pt} % font colors \newcommand{\usercolor}{\color [named]{BlueViolet}} \newcommand{\othercolor}{\color [named]{Mahogany}} \definecolor{hrefcolor}{RGB}{0,65,164} \newcommand{\lod}{\text{LOD}} \hypersetup{pdfpagemode=UseNone} % don't show bookmarks on initial view \hypersetup{colorlinks, urlcolor={hrefcolor}} \begin{document} \begin{center} \rule{7.0in}{1mm} \vspace{0mm} {\Large \textbf{A brief tour of R/qtl}} \vspace{4mm} {\large Karl W Broman} \vspace{2mm} Department of Biostatistics and Medical Informatics\\ University of Wisconsin -- Madison \vspace{2mm} \href{http://www.rqtl.org}{http://www.rqtl.org} \vspace{2mm} 21 March 2012 % the date \rule{7.0in}{1mm} \end{center} \noindent \textbf{Overview of R/qtl} \vspace{6pt} R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTL) in experimental crosses. It is implemented as an add-on package for the freely available and widely used statistical language/software R (see \href{http://www.r-project.org}{www.r-project.org}). The development of this software as an add-on to R allows us to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing. A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression. R/qtl is distributed as source code for Unix or compiled code for Windows or Mac OS X. R/qtl is released under the GNU General Public License, version 3. To download the software, you must agree to the terms in that license. \vspace{12pt} \noindent \textbf{Overview of R} \vspace{6pt} R is an open-source implementation of the S language. As described on the R-project homepage (\href{http://www.r-project.org}{www.r-project.org}): \begin{quote} R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files. The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency. The R distribution contains functionality for a large number of statistical procedures. Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations. Additional modules are available for a variety of specific purposes. \end{quote} R is freely available for Windows, Unix and Mac OS X, and may be downloaded from the Comprehensive R Archive Network (CRAN; cran.r-project.org). Learning R may require a formidable investment of time, but it will definitely be worth the effort. Numerous free documents on getting started with R are available on CRAN. In additional, several books are available. The most important book on R is Venables and Ripley (2002) \emph{Modern Applied Statistics with S}, 4th edition. Dalgaard (2002) \emph{Introductory Statistics with R} provides a more gentle introduction. \vspace{12pt} \noindent \textbf{Citation for R/qtl} \vspace{6pt} To cite R/qtl in publications, use \begin{quote} Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889-890 \end{quote} \newpage \noindent \textbf{Selected R/qtl functions} %\renewcommand{\arraystretch}{0.9} \noindent \begin{tabular}{lll} \hspace*{35mm} & \hspace*{25mm} & \hspace*{103mm} \\ \hline \textbf{Sample data} & badorder & An intercross with misplaced markers \\ & bristle3 & Data on bristle number for Drosophila chromosome 3 \\ & bristleX & Data on bristle number for Drosophila X chromosome \\ & fake.4way & Simulated data for a 4-way cross \\ & fake.bc & Simulated data for a backcross \\ & fake.f2 & Simulated data for an F$_2$ intercross \\ & hyper & Backcross data on salt-induced hypertension \\ & listeria & Intercross data on Listeria monocytogenes susceptibility \\ & map10 & A genetic map modeled after the mouse genome (10 cM spacing)\\ \hline \textbf{Input/output} & read.cross & Read data for a QTL experiment \\ & write.cross & Write data for a QTL experiment to a file \\ \hline \textbf{Simulation} & sim.cross & Simulate a QTL experiment \\ & sim.map & Generate a genetic map \\ \hline \textbf{Summaries} & qtlversion & Gives the version number of installed R/qtl package \\ & plot.cross & Plot various features of a cross object \\ & plotMissing & Plot grid of missing genotypes \\ & geno.image & Plot grid with colored pixels representing different genotypes \\ & plotPheno & Histogram or bar plot of a phenotype \\ & plotInfo & Plot the proportion of missing genotype data \\ & summary.cross & Print summary of QTL experiment \\ & summaryMap & Print summary of a genetic map \\ & \multicolumn{2}{l}{nchr, nind, nmar, nphe, totmar, nmissing, ntyped} \\ & find.pheno & Find the column number for a particular phenotype \\ & find.marker & Find the marker closest to a specified position \\ & find.flanking & Find the markers flanking a particular position \\ & find.pseudomarker & Find the name of the grid position closest to a particular position \\ & find.markerpos & Find the map positions of a marker \\ \hline \textbf{Data manipulation} & clean.cross & Remove intermediate calculations from a cross \\ & drop.markers & Remove a list of markers \\ & drop.nullmarkers & Remove markers without data \\ & fill.geno & Fill in holes in genotype data by imputation or Viterbi \\ & strip.partials & Replace partially informative genotypes with missing values \\ & pull.map & Pull out the genetic map from a cross \\ & pull.geno & Pull out the genotype data as a matrix \\ & pull.pheno & Pull out a phenotype \\ & replace.map & Replace the genetic map of a cross \\ & jittermap & Jitter marker positions slightly so that no two coincide \\ & subset.cross & Select a subset of chromosomes and/or individuals from a cross \\ & c.cross & Combine two crosses into one object \\ & switch.order & Switch the order of markers on a chromosome \\ & movemarker & Move a marker from one chromosome to another \\ \hline \textbf{HMM engine} & argmax.geno & Reconstruct underlying genotypes by the Viterbi algorithm \\ & calc.genoprob & Calculate conditional genotype probabilities \\ & sim.geno & Simulate genotypes given observed marker data \\ \hline \textbf{Diagnostics} & geno.table & Create table of genotype distributions \\ & geno.crosstab & Create cross-tabulation of genotypes at two markers \\ & checkAlleles & Identify markers with potentially switched alleles \\ & calc.errorlod & Calculate Lincoln \& Lander (1992) error LOD scores \\ & top.errorlod & List genotypes with highest error LOD values \\ & plotGeno & Plot observed genotypes, flagging likely errors \\ & comparecrosses & Compare two cross objects, to see if they are the same \\ & comparegeno & Calculate proportion of matching genotypes for each pair of individuals \\ \hline \end{tabular} \newpage \noindent \textbf{Selected R/qtl functions (continued)} %\renewcommand{\arraystretch}{0.9} \noindent \begin{tabular}{lll} \hspace*{35mm} & \hspace*{25mm} & \hspace*{103mm} \\ \hline \textbf{Genetic mapping} & est.rf & Estimate pairwise recombination fractions \\ & plotRF & Plot recombination fractions \\ & est.map & Estimate genetic map \\ & plotMap & Plot genetic map(s) \\ & summaryMap & Print summary of a genetic map \\ & ripple & Assess marker order by permuting groups of adjacent markers \\ & summary.ripple & Print summary of ripple output \\ & compareorder & Compare two orderings of markers on a chromosome \\ & tryallpositions & Test all possible positions for a marker \\ \hline \textbf{QTL mapping} & scanone & Genome scan with a single QTL model \\ & scantwo & Two-dimensional genome scan with a two-QTL model \\ & lodint & Calculate a LOD support interval \\ & bayesint & Calculate an approximate Bayes credible interval \\ & scanoneboot & Non-parametric bootstrap to obtain a confidence interval for QTL location \\ & plot.scanone & Plot output for a one-dimensional genome scan \\ & add.threshold & Add a horizontal line at a LOD threshold to a genome scan plot \\ & plot.scantwo & Plot output for a two-dimensional genome scan \\ & summary.scanone & Print summary of scanone output \\ & summary.scantwo & Print summary of scantwo output \\ & max.scanone & Maximum peak in scanone output \\ & max.scantwo & Maximum peak in scantwo output \\ & effectplot & Plot phenotype means of genotype groups defined by 1 or 2 markers \\ & effectscan & Plot estimated QTL effects across the whole genome \\ & plotPXG & Like effectplot, but as a dot plot of the phenotypes \\ \hline \textbf{Multiple QTL models} & makeqtl & Make a qtl object for use by fitqtl \\ & fitqtl & Fit a multiple QTL model \\ & summary.fitqtl & Get summary of the result of fitqtl \\ & scanqtl & Perform a multi-dimensional genome scan \\ & refineqtl & Refine the QTL locations in a multiple QTL model \\ & plotLodProfile & Plot 1-dimensional LOD profiles for a multiple QTL model \\ & addqtl & Scan for an additional QTL, in a multiple-QTL model \\ & addpair & Scan for an additional pair of QTL, in a multiple-QTL model \\ & addint & Add pairwise interactions, one at a time, in a multiple-QTL model \\ & summary.qtl & Print a summary of a QTL object \\ & plot.qtl & Plot the QTL locations on the genetic map \\ & addtoqtl & Add to a QTL object \\ & dropfromqtl & Drop a QTL from a QTL object \\ & replaceqtl & Replace a QTL location in a QTL object with a different position \\ & reorderqtl & Reorder the QTL in a QTL object \\ & cim & A (relatively crude) implementation of Composite Interval Mapping \\ & stepwiseqtl & Stepwise selection for multiple QTL \\ & calc.penalties & Calculate penalties for use with stepwiseqtl \\ & plotModel & Plot a graphical representation of a multiple-QTL model \\ \hline \end{tabular} \newpage \noindent \textbf{Preliminaries} \vspace{6pt} \noindent Use of the R/qtl package requires considerable knowledge of the R language/environment. We hope that the examples presented here will be understandable with little prior knowledge of R, especially because we neglect to explain the syntax of R. Several books, as well as some free documents, are available to assist the user in learning R; see the R project website cited above. We assume here that the user is running either Windows or Mac OS X. \begin{enumerate} \item To start R, double-click its icon. \item To exit, type: \usercolor \verb-q()- \normalcolor Click yes or no to save or discard your work. \item R keeps all of your work in RAM. If R should crash, all will be lost, and you will have to start from the beginning. The function \verb-save.image- can be used to save your work to disk as you go along, so that, should R crash, you won't have to start from scratch. You would type: \usercolor \verb|save.image()| \normalcolor \item Load the R/qtl package: \usercolor \verb|library(qtl)| \normalcolor \item View the objects in your workspace: \usercolor \verb|ls()| \normalcolor \item The best way to get help on the functions and data sets in R (and in R/qtl) is via the html version of the help files. One way to get access to this is to type \usercolor \verb-help.start()- \normalcolor This should open a browser with the main help menu. If you then click on \othercolor Packages \normalcolor $\rightarrow$ \othercolor qtl\normalcolor , you can see all of the available functions and datasets in R/qtl. For example, look at the help file for the function \verb-read.cross-. An alternative method to view this help file is to type one of the following: \usercolor \verb|help(read.cross)| \\ \verb|?read.cross| \normalcolor The html version of the help files are somewhat easier to read, and allow use of hotlinks between different functions. %You can create a file \othercolor \verb-"c:\.Rprofile"- \normalcolor %(\othercolor \verb-~/.Rprofile- \normalcolor in Mac OS X) containing any %R code to be executed whenever R is started. The command \usercolor %\verb-library(qtl)- \normalcolor %is a good candidate for %placement in such a file. \item All of the code in this tutorial is available as a file from which you may copy and paste into R, if you prefer that to typing. Type the following within R to get access to the file: \usercolor \verb-url.show("http://www.rqtl.org/rqtltour.R")- \normalcolor \end{enumerate} %\vspace{12pt} \noindent \textbf{Data import} \vspace{6pt} \noindent A difficult first step in the use of most data analysis software is the import of data. With R/qtl, one may import data in several different formats by use of the function \verb-read.cross-. (Example data files are available at \href{http://www.rqtl.org/sampledata}{www.rqtl.org/sampledata}.) The internal data structure used by R/qtl is rather complicated, and is described in the help file for \verb-read.cross-. (Also see Example 6 on page~\pageref{example6}.) We won't discuss data import any further here, except to say that the comma-delimited format (\verb-"csv"-) is recommended. If you have trouble importing data, send an email to Karl Broman (\verb-kbroman@biostat.wisc.edu-), attaching examples of your data files. (Such data will be kept confidential.) \vspace{12pt} \noindent \textbf{Example 1: Hypertension} \vspace{6pt} \nopagebreak \noindent As a first example, we consider data from an experiment on hypertension in the mouse (Sugiyama et al., Genomics 71:70-77, 2001), kindly provided by Bev Paigen and Gary Churchill. \begin{enumerate} \item First, get access to the data, see that it is in your workspace, and view its help file. These data are included with the R/qtl package, and so you can get access to the data with the function \verb-data()-. (Remember that you first need to load the R/qtl package via \verb-library(qtl)-.) \usercolor \verb|data(hyper)| \\ \verb|ls()| \\ \verb|?hyper| \normalcolor \item We will postpone discussion of the internal data structure used by R/qtl until later. For now we'll just say that the data \verb-hyper- has ``class'' \verb-"cross"-. The function \verb-summary.cross- prints summary information on such data. We can call that function directly, or we may simply use \verb-summary- and the data is sent to the appropriate function according to its class. \usercolor \verb|summary(hyper)| \normalcolor Several other utility functions are available for getting summary information on the data. Hopefully these are self-explanatory. \usercolor \verb|nind(hyper)| \\ \verb|nphe(hyper)| \\ \verb|nchr(hyper)| \\ \verb|totmar(hyper)| \\ \verb|nmar(hyper)| \normalcolor \item Plot a summary of these data. \usercolor \verb|plot(hyper)| \normalcolor In the upper left, black pixels indicate missing genotype data. Note that one marker has no genotype data. In the upper right, the genetic map of the markers is shown. In the lower left, a histogram of the phenotype is shown. The Windows version of R has a slick method for recording graphs, so that one may page up and down through a series of plots. To initiate this, click (on the menu bar) \othercolor History \normalcolor $\rightarrow$ \othercolor Recording\normalcolor . We may plot the individual components of the above multi-plot figure as follows. \usercolor \verb|plotMissing(hyper)| \\ \verb|plotMap(hyper)| \\ \verb|plotPheno(hyper, pheno.col=1)| %$ \normalcolor We can plot the genetic map with marker names, but they can be rather difficult to read. The following code plots the map with marker names for chr 1, 4, 6, 7 and 15. \usercolor \verb|plotMap(hyper, chr=c(1, 4, 6, 7, 15), show.marker.names=TRUE)| \normalcolor \item Note the odd pattern of missing data; we may make this missing data plot with the individuals ordered according to the value of their phenotype. \usercolor \verb|plotMissing(hyper, reorder=TRUE)| \normalcolor We see that, for most markers, only individuals with extreme phenotypes were genotyped. At many markers (in regions of interest), markers were typed only on recombinant individuals. \item The function \verb-drop.nullmarkers- may be used to remove markers that have no genotype data (such as the marker on chr 14). A call to \verb-totmar- will show that there are now 173 markers (rather than 174, as there were initially). \usercolor \verb|hyper <- drop.nullmarkers(hyper)| \\ \verb|totmar(hyper)| \normalcolor \item Estimate recombination fractions between all pairs of markers, and plot them. This also calculates LOD scores for the test of H$_0{:} \; r=1/2$. The plot of the recombination fractions can be either with recombination fractions in the upper part and LOD scores below, or with just recombination fractions or just LOD scores. Note that red corresponds to a small recombination fraction or a big LOD score, while blue is the reverse. Gray indicates missing values. \usercolor \verb|hyper <- est.rf(hyper)| \\ \verb|plotRF(hyper)| \\ \verb|plotRF(hyper, chr=c(1,4))| \normalcolor There are some very strange patterns in the recombination fractions, but this is due to the fact that some markers were typed largely on recombinant individuals. For example, on chr 6, the tenth marker shows a high recombination fraction with all other markers on the chromosome, but a plot of the missing data shows that this marker was typed only on a selected number of individuals (largely those showing recombination events across the interval). \usercolor \verb|plotRF(hyper, chr=6)| \\ \verb|plotMissing(hyper, chr=6)| \normalcolor \item Re-estimate the genetic map (keeping the order of markers fixed), and plot the original map against the newly estimated one. \usercolor \verb|newmap <- est.map(hyper, error.prob=0.01)| \\ \verb|plotMap(hyper, newmap)| \normalcolor We see some map expansion, especially on chr 6, 13 and 18. It is questionable whether we should replace the map or not. Keep in mind that the previous map locations are based on a limited number of meioses. If one wished to replace the genetic map with the estimated one, it could be done as follows: \usercolor \verb|hyper <- replace.map(hyper, newmap)| \normalcolor This replaces the map in the \verb-hyper- data with \verb-newmap-. \item We now turn to the identification of genotyping errors. In the following, we calculate the error LOD scores of Lincoln and Lander (1992). A LOD score is calculated for each individual at each marker; large scores indicate likely genotyping errors. \usercolor \verb|hyper <- calc.errorlod(hyper, error.prob=0.01)| \normalcolor This calculates the genotype error LOD scores and inserts them into the \verb-hyper- object. The function \verb-top.errorlod- gives a list of genotypes that may be in error. Error LOD scores $<$ 4 can probably be ignored. \usercolor \verb|top.errorlod(hyper)| \normalcolor Note that the results will be different, depending on whether you used \verb-replace.map- above. If you did, you will get an indication of potential errors on chr 16 (and a few on chr 13). If you didn't, you will get a very long list of potential errors on chr 1, 11, 15, 16 and 17. \item The function \verb-plotGeno- may be used to inspect the observed genotypes for a chromosome, with likely genotyping errors flagged. Of course, it's difficult to look at too many individuals at once. Note that white = AA and black = AB (for a backcross). \usercolor \verb|plotGeno(hyper, chr=16, ind=c(24:34, 71:81))| \normalcolor We don't have any utilities for fixing any apparent errors; it would be best to go back to the raw data. (Of course, you should edit a copy of the file; never discard the primary data.) \item The function \verb-plotInfo- plots a measure of the proportion of missing genotype information in the genotype data. The missing information is calculated in two ways: as entropy, or via the variance of the conditional genotypes, given the observed marker data. (See the help file, using \verb-?plotInfo-.) \usercolor \verb|plotInfo(hyper)| \\ \verb|plotInfo(hyper, chr=c(1,4,15))| \\ \verb|plotInfo(hyper, chr=c(1,4,15), method="entropy")| \\ \verb|plotInfo(hyper, chr=c(1,4,15), method="variance")| \normalcolor \item We now, finally, get to QTL mapping. The core of R/qtl is a set of functions which make use of the hidden Markov model (HMM) technology to calculate QTL genotype probabilities, to simulate from the joint genotype distribution and to calculate the most likely sequence of underlying genotypes (all conditional on the observed marker data). This is done in a quite general way, with possible allowance for the presence of genotyping errors. Of course, for convenience we assume no crossover interference. The function \verb-calc.genoprob- calculates QTL genotype probabilities, conditional on the available marker data. These are needed for most of the QTL mapping functions. The argument \verb-step- indicates the step size (in cM) at which the probabilities are calculated, and determines the step size at which later LOD scores are calculated. \usercolor \verb|hyper <- calc.genoprob(hyper, step=1, error.prob=0.01)| \normalcolor We may now use the function \verb-scanone- to perform a single-QTL genome scan with a normal model. We may use maximum likelihood via the EM algorithm (Lander and Botstein 1989) or use Haley-Knott regression (Haley and Knott 1992). \usercolor \verb|out.em <- scanone(hyper)| \\ \verb|out.hk <- scanone(hyper, method="hk")| \normalcolor We may also use the multiple imputation method of Sen and Churchill (2001). This requires that we first use \verb-sim.geno- to simulate from the joint genotype distribution, given the observed marker data. Again, the argument \verb-step- indicates the step size at which the imputations are performed and determines the step size at which LOD scores will be calculated. The \verb-n.draws- indicates the number of imputations to perform. Larger values give more precise results but require considerably more computer memory and computation time. \usercolor \verb|hyper <- sim.geno(hyper, step=2, n.draws=16, error.prob=0.01)| \\ \verb|out.imp <- scanone(hyper, method="imp")| \normalcolor \item The output of scanone has class \verb-"scanone"-; the function \verb-summary.scanone- displays the maximum LOD score on each chromosome for which the LOD exceeds a specified threshold. \usercolor \verb|summary(out.em)| \\ \verb|summary(out.em, threshold=3)| \\ \verb|summary(out.hk, threshold=3)| \\ \verb|summary(out.imp, threshold=3)| \normalcolor \item The function \verb-max.scanone- returns just the highest peak from output of \verb-scanone-. \usercolor \verb|max(out.em)| \\ \verb|max(out.hk)| \\ \verb|max(out.imp)| \normalcolor \item We may also plot the results. \verb-plot.scanone- can plot up to three genome scans at once, provided that they conform appropriately. Alternatively, one may use the argument \verb-add-. \usercolor \verb|plot(out.em, chr=c(1,4,15))| \\ \verb|plot(out.em, out.hk, out.imp, chr=c(1,4,15))| \\ \verb|plot(out.em, chr=c(1,4,15))| \\ \verb|plot(out.hk, chr=c(1,4,15), col="blue", add=TRUE)| \\ \verb|plot(out.imp, chr=c(1,4,15), col="red", add=TRUE)| \normalcolor \item The function \verb-scanone- may also be used to perform a permutation test to get a genome-wide LOD significance threshold. For Haley-Knott regression, this can be quite fast. \usercolor \verb|operm.hk <- scanone(hyper, method="hk", n.perm=1000)| \normalcolor The permutation output has class \verb-"scanoneperm"-. The function \verb-summary.scanoneperm- can be used to get significance thresholds. \usercolor \verb|summary(operm.hk, alpha=0.05)| \normalcolor In addition, if the permutations results are included in a call to \verb-summary.scanone-, you can estimated genome-scan-adjusted p-values for inferred QTL, and can get a report of all chromosomes meeting a certain significance level, with the corresponding LOD threshold calculated automatically. \usercolor \verb|summary(out.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE)| \normalcolor %\item Recall that the pattern of missing genotypes in the % \verb-hyper- data. At most markers, only the phenotypi extremes % were genotyped. We may use the function \verb-nmissing- to obtain % the number of missing genotypes for each individual, and use % \verb-hist- to plot a histogram. % %\usercolor %\verb|nm <- nmissing(hyper)| \\ %\verb|hist(nm, breaks=50)| \\ %\verb|rug(nm)| %\normalcolor % %The argument \verb-breaks- is used to specify the number of bins in %the histogram. The funtion \verb-rug- is used to add tick marks at %the bottom at the data points. % %In this figure, we see that individuals were missing genotypes at %either $<$ 30 or $>$ 120. % %This calls into question the permutation test we ran above, as the %phenotypes were permuted across all individuals. It would be better %to perform a stratified permutation test, permuting individuals within %the more completely genotyped group and separately permuting those %within the less completely genotyped group. % %This may be done with the \verb-perm.strata- argument to scanone, %which will define strata in which permutations will be performed. The %stratified permutation test may be performed as follows. % %\usercolor %\verb|operm2.hk <- scanone(hyper, method="hk", n.perm=1000,| \\ %\verb| perm.strata=(nm > 100))| %\normalcolor % %We may use \verb-summary- again to get a LOD threshold. This is much %larger than that calculated from the unstratified permutation test. % %\usercolor %\verb|summary(operm2.hk, alpha=0.05)| %\normalcolor \item We should mention at this point that the function \verb-save.image- may be used to save your workspace to disk. If R crashes, you will wish you had used this. \usercolor \verb|save.image()| \normalcolor \item The function \verb-scantwo- performs a two-dimensional genome scan with a two-QTL model. For every pair of positions, it calculates a LOD score for the full model (two QTL plus interaction) and a LOD score for the additive model (two QTL but no interaction). This be quite time consuming, and so you may wish to do the calculations on a coarser grid. \usercolor \verb|hyper <- calc.genoprob(hyper, step=5, error.prob=0.01)| \\ \verb|out2.hk <- scantwo(hyper, method="hk")| \normalcolor One can also use \verb-method="em"- or \verb-method="imp"-, but they are even more time consuming. \item \label{scantwo} The output of \verb-scantwo- has class \verb-"scantwo"-; there are functions for obtaining summaries and plots, of course. The summary function considers each pair of chromosomes, and calculates the maximum LOD score for the full model ($M_f$) and the maximum LOD score for the additive model ($M_a$). These two models are allowed to be maximized at different positions. We futher calculate a LOD score for a test of epistasis, $M_i = M_f - M_a$, and two LOD scores that concern evidence for a second QTL: $M_{fv1}$ is the LOD score comparing the full model to the best single-QTL model and $M_{av1}$ is the LOD score comparing the additive model to the best single-QTL model. In the summary, we must provide five thresholds, for $M_f$, $M_{fv1}$, $M_i$, $M_a$, and $M_{av1}$, respectively. Call these $T_f$, $T_{fv1}$, $T_i$, $T_a$, and $T_{av1}$. We then report those pairs of chromosomes for which at least one of the following holds: \begin{itemize} \item $M_f \ge T_f$ and ($M_{fv1} \ge T_{fv1}$ or $M_i \ge T_i$) \item $M_a \ge T_a$ and $M_{av1} \ge T_{av1}$ \end{itemize} The thresholds can be obtained by a permutation test (see below), but this is extremely time-consuming. For a mouse backcross, we suggest the thresholds (6.0, 4.7, 4.4, 4.7, 2.6) for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores, respectively. For a mouse intercross, we suggest the thresholds (9.1, 7.1, 6.3, 6.3, 3.3) for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores, respectively. These were obtained by 10,000 simulations of crosses with 250 individuals, markers at a 10 cM spacing, and analysis by Haley-Knott regression. \usercolor \verb|summary(out2.hk, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6))| \normalcolor The appropriate decision rule is not yet completely clear. I am inclined to ignore $M_i$ and to choose genome-wide thresholds for the other four based on a permutation, using a common significance level for all four. $M_i$ would be ignored if we gave it a very large threshold, as follows. \usercolor \verb|summary(out2.hk, thresholds=c(6.0, 4.7, Inf, 4.7, 2.6))| \normalcolor \item Plots of \verb-scantwo- results are created via \verb-plot.scantwo-. \usercolor \verb|plot(out2.hk)| \\ \verb|plot(out2.hk, chr=c(1,4,6,15))| \normalcolor By default, the upper-left triangle contains epistasis LOD scores and the lower-right triangle contains the LOD scores for the full model. The color scale on the right indicates separate scales for the epistasis and joint LOD scores (on the left and right, respectively). \item The function \verb-max.scantwo- returns the two-locus positions with the maximum LOD score for the full and additive models. \usercolor \verb|max(out2.hk)| \normalcolor \item One may also use \verb-scantwo- to perform permutation tests in order to obtain genome-wide LOD significance thresholds. These can be extremely time consuming, though with the Haley-Knott regression and multiple imputation methods, there is a trick that may be used in some cases to dramatically speed things up. So we'll try 100 permutations by the Haley-Knott regression method and hope that your computer is sufficiently fast. \usercolor \verb|operm2.hk <- scantwo(hyper, method="hk", n.perm=100)| \normalcolor We can again use \verb-summary- to get LOD thresholds. \usercolor \verb|summary(operm2.hk)| \normalcolor And again these may be used in the summary of the \verb-scantwo- output to calculate thresholds and p-values. If you want to ignore the LOD score for the interaction in the rule about what chromosome pairs to report, give $\alpha=0$, corresponding to a threshold $T=\infty$. \usercolor \verb|summary(out2.hk, perms=operm2.hk, pvalues=TRUE,| \\ \verb| alphas=c(0.05, 0.05, 0, 0.05, 0.05))| \normalcolor You can't really trust these results. Haley-Knott regression performs poorly in the case of selective genotyping (as with the \verb-hyper- data). Standard interval mapping or imputation would be better, but Haley-Knott regression has the advantage of speed, which is the reason we use it here. \item Finally, we consider the fit of multiple-QTL models. Currently, only multiple imputation and Haley-Knott regression has been implemented. We use multiple imputation here, as Haley-Knott regression performs poorly in the case of selective genotyping, which was used for the \verb-hyper- data. We first create a QTL object using the function \verb-makeqtl-, with five QTL at specified, fixed positions. \usercolor \verb|chr <- c(1, 1, 4, 6, 15)| \\ \verb|pos <- c(50, 76, 30, 70, 20)| \\ \verb|qtl <- makeqtl(hyper, chr, pos)| \normalcolor Finally, we use the function \verb-fitqtl- to fit a model with five QTL, and allowing the QTL on chr 6 and 15 to interact. \usercolor \verb|my.formula <- y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q4:Q5| \\ \verb|out.fitqtl <- fitqtl(hyper, qtl=qtl, formula=my.formula)| \\ %$ \verb|summary(out.fitqtl)| \normalcolor See Example 5 (page~\pageref{example5}) for a thorough discussion of the multiple QTL mapping methods in R/qtl. \item You may wish to clean up your workspace before we move on to the next example. \usercolor \verb|ls()| \\ \verb|rm(list=ls())| \normalcolor \end{enumerate} \vspace{12pt} \noindent \textbf{Example 2: Genetic mapping} \vspace{6pt} \nopagebreak \noindent R/qtl includes some utilities for estimating genetics maps and checking marker orders. In this example, we describe the use of these utilities. \begin{enumerate} \item Get access to some sample data. This is simulated data with some errors in marker order. \usercolor \verb|data(badorder)| \\ \verb|summary(badorder)| \\ \verb|plot(badorder)| \normalcolor \item Estimate recombination fractions between all pairs of markers, and plot them. \usercolor \verb|badorder <- est.rf(badorder)| \\ \verb|plotRF(badorder)| \normalcolor It appears that markers on chr 2 and 3 have been switched. Also note that, if we look more closely at the recombination fractions for chr 1, there seem to be some errors in marker order. \usercolor \verb|plotRF(badorder, chr=1)| \normalcolor \item Re-estimate the genetic map. \usercolor \verb|newmap <- est.map(badorder, verbose=TRUE)| \\ \verb|plotMap(badorder, newmap)| \normalcolor This really shows the problems on chr 2 and 3. \item Fix the problems on chr 2 and 3. First, we look more closely at the recombination fractions for these chromosoems \usercolor \verb|plotRF(badorder, chr=2:3)| \normalcolor We need to move the sixth marker on chr 2 to chr 3, and the fifth marker on chr 3 to chr 2. We need to figure out which markers these are. \usercolor \verb|pull.map(badorder, chr=2)| \\ \verb|pull.map(badorder, chr=3)| \normalcolor Now we can use the function \verb-movemarker- to move the markers. It seems like they should be exactly switched. \usercolor \verb|badorder <- movemarker(badorder, "D2M937", 3, 48)| \\ \verb|badorder <- movemarker(badorder, "D3M160", 2, 28.8)| \normalcolor Now look at the recombination fractions again. \usercolor \verb|plotRF(badorder, chr=2:3)| \normalcolor \item We can check the marker order on chr 1. The function \verb-ripple- will consider all permutations of a sliding window of adjacent markers. A quick-and-dirty approach is to count the number of obligate crossovers for each possible order, to find the order with the minimum number of crossovers. A more refined, but also more computationally intensive, approach is to re-estimate the genetic map for each order, calculating LOD scores (log$_{10}$ likelihood ratios) relative to the initial order. (This may be done with allowance for the presence of genotyping errors.) The default approach is the quick-and-dirty method. The following checks the marker order on chr 1, permuting groups of six contiguous markers. \usercolor \verb|rip1 <- ripple(badorder, chr=1, window=6)| \\ \verb|summary(rip1)| \normalcolor In the summary output, markers 9--11 clearly need to be flipped. There also seems to be a problem with the order of markers 4--6. \item The following performs the likelihood analysis, permuting groups of three adjacent markers, assuming a genotyping error rate of 1\%. It's considerably slower, but more trustworthy. \usercolor \verb|rip2 <- ripple(badorder, chr=1, window=3, err=0.01, method="likelihood")| \\ \verb|summary(rip2)| \normalcolor Note that positive LOD scores indicate that the alternate order has a higher likelihood than the original. \item We can switch the order of markers 9--11 with the function \verb-switch.order- (which works only for a single chromosome) and then re-assess the order. Note that the second row of \verb-rip1- corresponds to the improved order. \usercolor \verb|badorder.rev <- switch.order(badorder, 1, rip1[2,])| \\ \verb|rip1r <- ripple(badorder.rev, chr=1, window=6)| \\ \verb|summary(rip1r)| \normalcolor It looks like the marker pairs (5,6) and (1,2) should each be inverted. We use \verb-switch.order- again, and then check marker order using the likelihood method. \usercolor \verb|badorder.rev <- switch.order(badorder.rev, 1, rip1r[2,])| \\ \verb|rip2r <- ripple(badorder.rev, chr=1, window=3, err=0.01)| \\ \verb|summary(rip2r)| \normalcolor It's probably best to start out using the quick-and-dirty method, with a large window size, to find the marker order with the minimum number of obligate crossovers, and then refine that order using the slower, but more trustworthy, likelihood method. \item We can look again at the recombination fractions for this chromosome. \usercolor \verb|badorder.rev <- est.rf(badorder.rev)| \\ \verb|plotRF(badorder.rev, 1)| \normalcolor \end{enumerate} %\newpage \vspace{12pt} \noindent \textbf{Example 3: Listeria susceptibility} \vspace{6pt} \nopagebreak \noindent In order to demonstrate further uses of the function \verb-scanone-, we consider some data on susceptibility to \emph{Listeria monocytogenes\/} in mice (Boyartchuk et al., Nature Genetics 27:259-260, 2001). These data were kindly provided by Victor Boyartchuk and Bill Dietrich. \begin{enumerate} \item Get access to the data and view some summaries. \usercolor \verb|data(listeria)| \\ \verb|summary(listeria)| \\ \verb|plot(listeria)| \\ \verb|plotMissing(listeria)| \normalcolor Note that in the missing data plot, gray pixels are partially missing genotypes (e.g., a genotype may be known to be either AA or AB, but not which). The phenotype here is the survival time of a mouse (in hours) following infection with \emph{Listeria monocytogenes}. Individuals with a survival time of 264 hours are those that recovered from the infection. \item We'll use the log survival time, rather than survival time, so we first need to create a new phenotype, which will end up as the third phenotype (after \verb-sex-). \usercolor \verb|listeria$pheno$logSurv <- log(listeria$pheno[,1])| \\ %$ \verb|plot(listeria)| \normalcolor \item Estimate pairwise recombination fractions. \usercolor \verb|listeria <- est.rf(listeria)| \\ \verb|plotRF(listeria)| \\ \verb|plotRF(listeria, chr=c(5,13))| \normalcolor \item Re-estimate the genetic map. \usercolor \verb|newmap <- est.map(listeria, error.prob=0.01)| \\ \verb|plotMap(listeria, newmap)| \\ \verb|listeria <- replace.map(listeria, newmap)| \normalcolor \item Investigate genotyping errors; nothing gets flagged with a cutoff of 4, but one genotype is indicated with error LOD $\sim$3.8. \usercolor \verb|listeria <- calc.errorlod(listeria, error.prob=0.01)| \\ \verb|top.errorlod(listeria)| \\ \verb|top.errorlod(listeria, cutoff=3.5)| \\ \verb|plotGeno(listeria, chr=13, ind=61:70, cutoff=3.5)| \normalcolor Note that in the plot given by \verb-plotGeno-, for an intercross, white = AA, gray = AB, black = BB, green = AA or AB, and orange = AB or BB. \item Now on to the QTL mapping. Recall that the phenotype distribution shows a clear departure from the standard assumptions for interval mapping; 30\% of the mice survived longer than 264 hours, and were considered recovered from the infection. One approach for these data is to use the two-part model considered by Boyartchuk et al.\ (2001). In this model, a mouse with genotype $g$ has probability $p_g$ of surviving the infection. If it does die, its log survival time is assumed to be distributed normal($\mu_g$,$\sigma^2$). Analysis proceeds by maximum likelihood via an EM algorithm. Three LOD scores are calculated. LOD($p,\mu$) is for the test of the null hypothesis $p_g \equiv p$ and $\mu_g \equiv \mu$. LOD($p$) is for the test of the hypothesis $p_g \equiv p$ but the $\mu$ are allowed to vary. LOD($\mu$) is for the test of the hypothesis $\mu_g \equiv \mu$ but the $p$ are allowed to vary. The function \verb-scanone- will fit the above model when the argument \verb-model="2part"-. One must also specify the argument \verb-upper-, which indicates whether the spike in the phenotype is the maximum phenotype (as it is with this phenotype; take \verb-upper=TRUE-) or the minimum phenotype (take \verb-upper=FALSE-). For this model, only the EM algorithm has been implemented so far. \usercolor \verb|listeria <- calc.genoprob(listeria, step=2)| \\ \verb|out.2p <- scanone(listeria, pheno.col=3, model="2part", upper=TRUE)| \normalcolor Note the use of the argument \verb-pheno.col- to indicate the phenotype column to use for the analysis. We can also refer to the phenotype column by name: \verb-pheno.col="logSurv"-. Because the two-part model has three extra parameters, the appropriate LOD threshold is higher---around 4.5 rather than 3.5. The three different LOD curves are in columns 3--5 of the output. \usercolor \verb|summary(out.2p)| \\ \verb|summary(out.2p, threshold=4.5)| \normalcolor Alternatively, we may use \verb-format="allpeaks"-, in which case it displays the maximum LOD score or each column, with the position at which each was maximized. You may provide either one threshold, which would be applied to all LOD score columns, or a separate threshold for each column. \usercolor \verb|summary(out.2p, format="allpeaks", threshold=3)| \\ \verb|summary(out.2p, format="allpeaks", threshold=c(4.5,3,3))| \normalcolor \item By default, \verb-plot.scanone- will plot the first LOD score column. Alternatively, we may indicate another column to plot with the \verb-lodcolumn- argument. Or we can plot up to three LOD scores at once by giving a vector. \usercolor \verb|plot(out.2p)| \\ \verb|plot(out.2p, lodcolumn=2)| \\ \verb|plot(out.2p, lodcolumn=1:3, chr=c(1,5,13,15))| \normalcolor Note that the locus on chr 1 shows effect mostly on the mean time-to-death, conditional on death; the locus on chr 5 shows effect mostly on the probability of survival; and the loci on chr 13 and 15 shows some effect on each. \item Permutation tests may be performed as before. The output will have three columns, corresponding to the three LOD scores. \usercolor \verb|operm.2p <- scanone(listeria, model="2part", pheno.col=3,| \\ \verb| upper=TRUE, n.perm=25)| \\ \verb|summary(operm.2p, alpha=0.05)| \normalcolor We may again use the permutation results in \verb-summary.scanone- to have thresholds calculated automatically and to obtain genome-scan-adjusted p-values, but of course we would want to have performed more than 25 permutations. \usercolor \verb|summary(out.2p, format="allpeaks", perms=operm.2p,| \\ \verb| alpha=0.05, pvalues=TRUE)| \normalcolor \item Alternatively, one may perform separate analyses of the log survival time, conditional on death, and the binary phenotype survival/death. First we set up these phenotypes. \usercolor \verb|y <- listeria$pheno$logSurv| \\ \verb|my <- max(y, na.rm=TRUE)| \\ \verb|z <- as.numeric(y==my)| \\ \verb|y[y==my] <- NA| \\ \verb|listeria$pheno$logSurv2 <- y| \\ \verb|listeria$pheno$binary <- z| \\ \verb|plot(listeria)| \normalcolor We use standard interval mapping for the log survival time conditional on death; the results are slightly different from LOD($\mu$). \usercolor \verb|out.mu <- scanone(listeria, pheno.col=4)| \\ \verb|plot(out.mu, out.2p, lodcolumn=c(1,3), chr=c(1,5,13,15), col=c("blue","red"))| \normalcolor We can use \verb-scanone- with \verb-model="binary"- to analyze the binary phenotype. Again, the results are only slight different from LOD($p$). \usercolor \verb|out.p <- scanone(listeria, pheno.col=5, model="binary")| \\ \verb|plot(out.p, out.2p, lodcolumn=c(1,2), chr=c(1,5,13,15), col=c("blue","red"))| \normalcolor The argument \verb-pheno.col- in \verb-scanone- can actually take a vector of numeric phenotype values, and not just an indicator to a phenotype column, and so we could have performed the binary trait analysis without first pasting the binary phenotype into the \verb-listeria- object, as follows. \usercolor \verb|out.p.alt <- scanone(listeria, pheno.col=as.numeric(listeria$pheno$T264==264),|\\ \verb| model="binary")| \normalcolor \item A further approach is to use a non-parametric form of interval mapping. R/qtl uses an extension of the Kruskal-Wallis test statistic. Use \verb-scanone- with \verb-model="np"-. In this case, the argument \verb-method- is ignored; the analysis method is much like Haley-Knott regression. If the argument \verb-ties.random=TRUE-, tied phenotypes are ranked at random. If \verb-ties.random=FALSE-, tied phenotypes are given the average rank and a correction is applied to the LOD score. \usercolor \verb|out.np1 <- scanone(listeria, model="np", ties.random=TRUE)| \\ \verb|out.np2 <- scanone(listeria, model="np", ties.random=FALSE)| \verb|plot(out.np1, out.np2, col=c("blue","red"))| \\ \verb|plot(out.2p, out.np1, out.np2, chr=c(1,5,13,15))| \normalcolor Note that the significance threshold for the non-parametric genome scan will be quite a bit smaller than that for the two-part model. The two approaches for dealing with ties give basically the same results. Randomizing ties for the non-parametric approach can give quite variable results in the case of a great number of ties, and so we would recommend the use of \verb-ties.random=FALSE- in this case. \end{enumerate} \vspace{12pt} %\newpage \noindent \textbf{Example 4: Covariates in QTL mapping} \vspace{6pt} \nopagebreak \noindent As a further example, we illustrate the use of covariates in QTL mapping. We consider some simulated backcross data. \begin{enumerate} \item Get access to the data. \usercolor \verb|data(fake.bc)| \\ \verb|summary(fake.bc)| \\ \verb|plot(fake.bc)| \normalcolor \item Perform genome scans for the two phenotypes without covariates. Here we consider two phenotypes, scanned individually. \usercolor \verb|fake.bc <- calc.genoprob(fake.bc, step=2.5)| \\ \verb|out.nocovar <- scanone(fake.bc, pheno.col=1:2)| \normalcolor \item Perform genome scans with sex as an additive covariate. Note that the covariates must be numeric. Factors may have to be converted. \usercolor \verb|sex <- fake.bc$pheno$sex| \\ \verb|out.acovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex)| \normalcolor Here, the average phenotype is allowed to be different in the two sexes, but the effect of the putative QTL is assumed to be the same in the two sexes. \item Note that the use of sex as an additive covariate resulted in an increase in the LOD scores for phenotype 1, but resulted in a decreased LOD score at the chr 5 locus for phenotype 2. \usercolor \verb|summary(out.nocovar, threshold=3, format="allpeaks")| \\ \verb|summary(out.acovar, threshold=3, format="allpeaks")| \verb|plot(out.nocovar, out.acovar, chr=c(2, 5))| \\ \verb|plot(out.nocovar, out.acovar, chr=c(2, 5), lodcolumn=2)| \normalcolor \item Let us now perform genome scans with sex as an interactive covariate, so that the QTL is allowed to be different in the two sexes. \usercolor \verb|out.icovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex, intcovar=sex)| \normalcolor \item The LOD score in the output is for the comparison of the full model with terms for sex, QTL and QTL$\times$sex interaction to the reduced model with just the sex term. Thus, the degrees of freedom associated with the LOD score is 2 rather than 1, and so larger LOD scores will generally be obtained. \usercolor \verb|summary(out.icovar, threshold=3, format="allpeaks")| \normalcolor \usercolor \verb|plot(out.acovar, out.icovar, chr=c(2,5), col=c("blue", "red"))| \\ \verb|plot(out.acovar, out.icovar, chr=c(2,5), lodcolumn=2,| \\ \verb| col=c("blue", "red"))| \normalcolor \item The difference between the LOD score with sex as an interactive covariate and the LOD score with sex as an additive covariate concerns the test of the QTL$\times$sex interaction: does the QTL have the same effect in both sexes? The differences, and a plot of the differences, may be obtained as follows. \usercolor \verb|out.sexint <- out.icovar - out.acovar| \\ \verb|plot(out.sexint, lodcolumn=1:2, chr=c(2,5), col=c("green", "purple"))| \normalcolor The green and purple curves are for the first and second phenotypes, respectively. \item To test for the QTL$\times$sex interaction, we may perform a permutation test. This is not perfect, as the permutation test eliminates the effect of the QTL, and so we must assume that the distribution of the LOD score for the QTL$\times$sex interaction is the same in the presence of a QTL as under the global null hypothesis of no QTL effect. The permutation test requires some care. We must perform separate permutations with sex as an additive covariate and with sex as an interactive covariate, but we must ensure, by setting the ``seed'' for the random number generator, that they use matched permutations of the data. For the sake of speed, we will use Haley-Knott regression, even though the results above were obtained by standard interval mapping. Also, we will perform just 100 permutations, though 1000 would be preferred. \usercolor \verb|seed <- ceiling(runif(1, 0, 10^8))| \\ \verb|set.seed(seed)| \\ \verb|operm.acovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex,| \\ \verb| method="hk", n.perm=100)| \\ \verb|set.seed(seed)| \\ \verb|operm.icovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex,| \\ \verb| intcovar=sex, method="hk", n.perm=100)| \normalcolor Again, the differences concern the QTL$\times$sex interaction. \usercolor \verb|operm.sexint <- operm.icovar - operm.acovar| \normalcolor We can use \verb-summary- to get the genome-wide LOD thresholds. \usercolor \verb|summary(operm.sexint, alpha=c(0.05, 0.20))| \normalcolor We can also use these results to look at evidence for QTL$\times$sex interaction in our initial scans. \usercolor \verb|summary(out.sexint, perms=operm.sexint, alpha=0.1,| \\ \verb| format="allpeaks", pvalues=TRUE)| \normalcolor \end{enumerate} %\vspace{12pt} \newpage \noindent \textbf{Example 5: Multiple QTL mapping} \vspace{6pt} \nopagebreak \label{example5} We return to the \verb-hyper- data to illustrate some of the more advanced methods for exploring multiple QTL models. Note that the multiple QTL mapping features are currently implemented only for multiple imputation and Haley-Knott regression. We use multiple imputation here, as Haley-Knott regression performs poorly in the case of selective genotyping, which was used for the \verb-hyper- data. \begin{enumerate} \item First, let us delete everything in our workspace and then re-load the \verb-hyper- data. \usercolor \verb|rm(list=ls())| \\ \verb|data(hyper)| \normalcolor \item We will be using the multiple imputation method throughout this example, and so we first need to perform the imputations. Recall that more imputations give more precise results, but take more time and memory. To speed things along, we will use only 16 imputations, even though much more would be needed for a definitive analysis. The small number of imputations will make the following results somewhat unpredictable. \usercolor \verb|hyper <- sim.geno(hyper, step=2.5, n.draws=16, err=0.01)| \normalcolor \item We first perform a single-QTL genome scan and inspect the results. \usercolor \verb|out1 <- scanone(hyper, method="imp")| \\ \verb|plot(out1)| \normalcolor As you may recall from the results in Example 1, we have clear evidence for a QTL on chr 4, and strong evidence for a QTL on chr 1. The LOD curve on chr 1 has an interesting double peak, suggestive of possibly two QTL. There is a hint of further loci on chr 6 and 15 and elsewhere. \item In the presence of a large-effect QTL, as seen on chr 4, one may wish to repeat the scan, controlling for that locus. This can make the loci with more modest effect more apparent. A simple (but rough) approach is to pull out the genotypes for a marker near the peak locus, and use that marker as an additive covariate in a single-QTL scan. The peak marker for these data was D4Mit164: \usercolor \verb|max(out1)| \normalcolor If the peak LOD score is not at a marker, we may use \verb-find.marker- to identify the marker closest to the LOD peak. \usercolor \verb|find.marker(hyper, 4, 29.5)| \normalcolor \item The function \verb-pull.geno- may be used to pull out the genotype data for that marker, but we'll see that most individuals were not typed at D4Mit164. \usercolor \verb|g <- pull.geno(hyper)[,"D4Mit164"]| \\ \verb|mean(is.na(g))| \normalcolor We may fill in the genotype data using a single imputation, and then use those imputed genotypes as if they were observed. This is not ideal; we'll do this analysis properly below. \usercolor \verb|g <- pull.geno(fill.geno(hyper))[,"D4Mit164"]| \normalcolor \item Now we perform the genome scan, controlling for the chr 4 locus. (Note that in an intercross, we would have to re-code the genotype data to be a two-column numeric matrix.) \usercolor \verb|out1.c4 <- scanone(hyper, method="imp", addcovar=g)| \normalcolor We can plot the results together with the original genome scan. \usercolor \verb|plot(out1, out1.c4, col=c("blue", "red"))| \normalcolor The LOD curve on chr 1 went up quite a bit. (And, of course, the LOD curve on chr 4 went down to near 0.) To see the effect of controlling for the chr 4 locus more clearly, we can plot the differences between the LOD scores. \usercolor \verb|plot(out1.c4 - out1, ylim=c(-3,3))| \\ \verb|abline(h=0, lty=2, col="gray")| \normalcolor \item We may also look for loci that interact with the chr 4 locus, by including marker D4Mit164 as an interactive covariate. \usercolor \verb|out1.c4i <- scanone(hyper, method="imp", addcovar=g, intcovar=g)| \normalcolor The difference between these LOD scores and those obtained with D4Mit164 as a strictly additive covariate indicates evidence for an interaction with the chr 4 locus. \usercolor \verb|plot(out1.c4i - out1.c4)| \normalcolor There is nothing particularly interesting here. \item Now let us perform a 2d scan. This will take a few minutes, as we're doing the scan at a 2.5~cM step size. \usercolor \verb|out2 <- scantwo(hyper, method="imp")| \normalcolor \item Let us look at some summaries for the \verb-scantwo- results. Recall that we need to provide five thresholds (see Example~1, item~\ref{scantwo} on page~\pageref{scantwo}). We'll ignore the threshold on the epistasis LOD score, $T_i$, and use the thresholds suggested above. \usercolor \verb|summary(out2, thr=c(6.0, 4.7, Inf, 4.7, 2.6))| \normalcolor Your results may be different from mine, since we are using so few imputations, but I see evidence for loci on chr 1 and 4 (which don't appear to interact) and loci on chr 6 and 15 (which do show evidence of epistasis). This didn't pick up evidence for two QTL on chr 1; we can look directly at the chr 1 results as follows. \usercolor \verb|summary( subset(out2, chr=1) )| \normalcolor The LOD score for a second, additive QTL on chr 2 ($\lod_{av1}$) is $\sim$1.6; not strong, but not uninteresting. Evidence for an interaction between loci on chr 7 and 15 had been previously reported. Those results may be inspected as follows. \usercolor \verb|summary( subset(out2, chr=c(7,15)) )| \normalcolor Again, this is interesting but not strong. \item Let us look at some plots of the \verb-scantwo- results. First we make the standard plot with selected chromosomes; the upper triangle contains $\lod_i$ and the lower triangle contains $\lod_f$. \usercolor \verb|plot(out2, chr=c(1,4,6,7,15))| \normalcolor The arguments \verb-lower- and \verb-upper- may be used to change what is plotted in the upper and lower triangles. For example, with \verb:lower="cond-int": , $\lod_{fv1}$ (evidence for a second QTL, allowing for epistasis) is displayed in the lower triangle, while with \verb:lower="cond-add":, $\lod_{av1}$ (evidence for a second QTL, assuming no epistasis) is displayed. \usercolor \verb|plot(out2, chr=1, lower="cond-add")| \\ \verb|plot(out2, chr=c(6,15), lower="cond-int")| \\ \verb|plot(out2, chr=c(7,15), lower="cond-int")| \normalcolor Again, evidence for a second QTL on chr 1 is not strong. Evidence for interacting QTL on chr 6 and 15 is quite strong; the 7$\times$15 interaction is not. \item We can also perform the 2d scan conditional on the chr 4 locus. We'll do this just for chr 1, 6, 7, and 15, to save time. \usercolor \verb|out2.c4 <- scantwo(hyper, method="imp", addcovar=g, chr=c(1,6,7,15))| \normalcolor If we look at the same summaries as before, we see decreased evidence for a second QTL on chr 1 and for the 7$\times$15 interaction, but increased evidence for the 6$\times$15 interaction. \usercolor \verb|summary(out2.c4, thr=c(6.0, 4.7, Inf, 4.7, 2.6))| \\ \verb|summary( subset(out2.c4, chr=1) )| \\ \verb|summary( subset(out2.c4, chr=c(7,15)) )| \normalcolor The sort of plots we made before remain interesting. \usercolor \verb|plot(out2.c4)| \\ \verb|plot(out2.c4, chr=1, lower="cond-int")| \\ \verb|plot(out2.c4, chr=c(6,15), lower="cond-int")| \\ \verb|plot(out2.c4, chr=c(7,15), lower="cond-int")| \normalcolor We can also look at the differences in the LOD scores, to see how much conditioning on D4Mit164 has affected the results. We need to subset our original results, since we only scanned selected chromosomes in the conditional analysis. The \verb-allow.neg- argument is used to allow negative LOD scores in the \verb-scantwo- plot, as they would generally be replaced with 0. \usercolor \verb|out2sub <- subset(out2, chr=c(1,6,7,15))| \\ \verb|plot(out2.c4 - out2sub, allow.neg=TRUE, lower="cond-int")| \normalcolor \item Now let us turn to the fit of multiple-QTL models. The function \verb-fitqtl- is used to fit a specific model. One must first pull out the data on fixed QTL locations using \verb-makeqtl-. We will consider the possibility of two QTL on chr 1, but will ignore the putative QTL on chr 7. \usercolor \verb|qc <- c(1, 1, 4, 6, 15)| \\ \verb|qp <- c(43.3, 78.3, 30.0, 62.5, 18.0)| \\ \verb|qtl <- makeqtl(hyper, chr=qc, pos=qp)| \normalcolor We also create a ``formula'' which indicates which QTL are to be included in the fit and which interact; the colon (:) indicates an interaction. \usercolor \verb|myformula <- y ~ Q1+Q2+Q3+Q4+Q5 + Q4:Q5| \normalcolor We can now fit a model, including the 6$\times$15 interaction, and get a summary of the results. \usercolor \verb|out.fq <- fitqtl(hyper, qtl=qtl, formula = myformula)| \\ \verb|summary(out.fq)| \normalcolor The first part of the summary describes the overall fit; the LOD score of $\sim$23 is the log$_{10}$ likelihood ratio comparing the full model to the null model. The second part of the summary gives results dropping one term at a time from the model. In the presence of an interaction, if a term included in the interaction is omitted, the interaction is also omitted, and so the rows for the loci on chr 6 and 15 indicate 2 degrees of freedom. \item One may also use \verb-fitqtl- to get estimated effects of the QTL in the context of the multiple-QTL model. We can use \verb-drop=FALSE-, so that the ``drop one at a time'' part of the analysis is not performed, and \verb-get.ests=TRUE- to get the estimated effects. \usercolor \verb|out.fq <- fitqtl(hyper, qtl=qtl, formula = myformula, drop=FALSE, get.ests=TRUE)| \\ \verb|summary(out.fq)| \normalcolor The estimated effects are the differences between the heterozygote and homozygote groups. The interaction effect is the difference between the differences. \item The function \verb-refineqtl- can be used to refine the estimated positions of the QTL in the context of the multiple-QTL model. A QTL object may be provided, or one may specify the chromosomes and positions, as in \verb-makeqtl-; we'll use the former approach. \usercolor \verb|revqtl <- refineqtl(hyper, qtl=qtl, formula = myformula)| \normalcolor The output is a QTL object, like \verb-qtl-; typing its name gives a brief summary. \usercolor \verb|revqtl| \normalcolor A couple of the QTL moved, but none by very much. One may use the \verb-plot.qtl- function to plot the locations of the QTL on the genetic map. \usercolor \verb|plot(revqtl)| \normalcolor We can re-run \verb-fitqtl- to get a fit with the new positions; the overall LOD score should have increased slightly. (For me, it increased from 23.0 to 23.7.) \usercolor \verb|out.fq2 <- fitqtl(hyper, qtl=revqtl, formula=myformula)| \\ \verb|summary(out.fq2)| \normalcolor \item The \verb-scanqtl- function is used to perform general genome scans in the context of a multiple QTL model. It is quite flexible, but not simple to use. For most purposes, one may focus on the functions \verb-addqtl- and \verb-addpair-, which scan for an additional QTL or pair of QTL, respectively, to add to a multiple-QTL model. We will first use \verb-addqtl- to perform a more precise version of our genome scan conditional on the chr 4 locus. Previously, we had conditioned on imputed genotypes at a marker near the LOD peak on chr 4. With \verb-addqtl- we can do this properly: take proper account of the missing genotype information at the chr 4 locus, rather than taking genotypes from a single imputation as if they had been observed. The \verb-addqtl- function is much like \verb-fitqtl-, taking a QTL object and formula as arguments. If the formula is omitted, all loci are assumed to be additive. The additional QTL to be scanned may be included in the formula; if there are 5 QTL in the input QTL object, refer to the new QTL as \verb-Q6-. This allows a scan with the new QTL interacting with one or more of the current QTL. If the new QTL is not included in the formula, it is assumed to be strictly additive. The following performs a scan on all chromosomes, controlling solely for the QTL on chromosome 4. (This is the third QTL in the QTL object \verb-revqtl-, and so we may use as the formula either \verb-y~Q3- or \verb-y~Q3+Q6-. The former is allowed, as an additional additive QTL is assumed.) \usercolor \verb|out1.c4r <- addqtl(hyper, qtl=revqtl, formula=y~Q3)| \normalcolor The output is of the same form as produced by the \verb-scanone- function, and so we may use the same plot and summary functions as are used for \verb-scanone- results. (Note that the LOD scores produced by \verb-addqtl- are relative to the model specified in the formula, omitting any terms including the additional QTL being scanned, rather than relative to the null model.). We may now plot these results with those obtained earlier. The results are actually not too different. \usercolor \verb|plot(out1.c4, out1.c4r, col=c("blue", "red"))| \normalcolor It may be more informative to plot the differences \usercolor \verb|plot(out1.c4r - out1.c4, ylim=c(-1.7, 1.7))| \\ \verb|abline(h=0, lty=2, col="gray")| \normalcolor \item The function \verb-addpair- may be used to perform a 2d scan for an additional pair of QTL, conditioning on the locus on chr 4. If the new QTL are not specified in the formula, a scan as in \verb-scantwo- is performed (that is, for each possible pair of positions for the new QTL, we fit a model in which the two new QTL interact and one in which they are additive). \usercolor \verb|out2.c4r <- addpair(hyper, qtl=revqtl, formula=y~Q3, chr=c(1,6,7,15))| \normalcolor The results are of the same form as produced by \verb-scantwo-, and We can plot the difference between these results and our previous results. \usercolor \verb|plot(out2.c4r - out2.c4, lower="cond-int", allow.neg=TRUE)| \normalcolor Again, things have not changed dramatically. \item The most interesting use of \verb-addqtl- and \verb-addpair- is to scan for additional loci, starting with our five-QTL model (with the loci on 6 and 15 interacting). First, we scan for an additional additive QTL. \usercolor \verb|out.1more <- addqtl(hyper, qtl=revqtl, formula=myformula)| \\ \verb|plot(out.1more)| \normalcolor There is not much evidence for an additional QTL. \item We may next scan for an additional QTL that interacts with one of the QTL in our model, such as the QTL on chr 15. This may be done by indicating the interaction in the formula, using \verb-Q6- to specify the new QTL, since there are five QTL in the \verb-revqtl- object. \usercolor \verb|out.iw4 <- addqtl(hyper, qtl=revqtl, formula=y~Q1+Q2+Q3+Q4+Q5+Q4:Q5+Q6+Q5:Q6)| \\ \verb|plot(out.iw4)| \normalcolor The LOD scores are just slightly higher, but there are two degrees of freedom in the test. There's nothing particularly exciting here. \item Now, let us scan for an additional pair. This will take quite a bit of time, so let's focus on a few chromosomes: 2, 5, 7 and 15. \usercolor \verb|out.2more <- addpair(hyper, qtl=revqtl, formula=myformula, chr=c(2,5,7,15))| \normalcolor Again, the results are of the form produced by \verb-scantwo-, and so we may use the same plot and summary functions. \usercolor \verb|plot(out.2more, lower="cond-int")| \normalcolor Again, there's nothing particularly exciting. \item Another function of interest is \verb-addint-, for testing the addition of each possible pairwise interactions, one at a time, to a multiple-QTL model. \usercolor \verb|out.ai <- addint(hyper, qtl=revqtl, formula=myformula)| \\ \verb|out.ai| \normalcolor The results contain one row per interaction, and contain the same sort of information as produced by in the drop-one analysis of \verb-fitqtl-. As the base model (in \verb-myformula-) contains an interaction between the loci on chr 6 and 15, that particular interaction is not tested. \item We should mention the functions for manipulating QTL objects (produced by \verb-makeqtl-): \verb-addtoqtl-, \verb-dropfromqtl-, \verb-replaceqtl-, and \verb-reorderqtl-. If the use of \verb-addqtl- and \verb-addpair- had indicated evidence for additional QTL, one could add them to the QTL object with \verb-addtoqtl-. As input, one provides the cross, the QTL object, and the chromosomes and positions of the QTL to be added. \usercolor \verb|qtl2 <- addtoqtl(hyper, revqtl, 7, 53.6)| \\ \verb|qtl2| \normalcolor A QTL may be removed with \verb-dropfromqtl-. One provides either the numeric index within the object, the QTL name, or the chromosome and position of the QTL to be dropped. \usercolor \verb|qtl3 <- dropfromqtl(qtl2, index=2)| \\ \verb|qtl3| \normalcolor We can use \verb-replaceqtl- to move a particular QTL to a new position. One must provide the index of the QTL to be replaced. \usercolor \verb|qtl4 <- replaceqtl(hyper, qtl3, index=1, chr=1, pos=50)| \\ \verb|qtl4| \normalcolor We use \verb-reorderqtl- to change the order of the loci within a QTL object. \usercolor \verb|qtl5 <- reorderqtl(qtl4, c(1:3,5,4))| \\ \verb|qtl5| \normalcolor \item Finally, we consider an automated model selection procedure with a stepwise search algorithm, using the function \verb-stepwiseqtl-. The function seeks to optimize a penalized LOD score criterion, which is the LOD score for a model (relative to the null model with no QTL) with penalties on each QTL main effect and a separate penalty on interactions. Actually, we include include two penalties on interactions, a light penalty and a heavy penalty. We focus on models with possible pairwise interactions among QTL, and with a hierarchical structure in which the inclusion of an interaction term requires the inclusion of both of the corresponding main effects terms. Such a model may be represented by a graph in which vertices (dots) represent QTL and edges (line segments between the dots) represent interactions between QTL. In the penalized LOD score considered by \verb-stepwiseqtl-, each disconnected component of a model is allowed one light interaction penalty; all other interactions are assigned the heavy penalty. The three penalties may be calculed from permutation results with \verb-scantwo-, using the function \verb-calc.penalties-. We will use default penalties derived by computer simulation: (2.69, 2.62, 1.19) for a mouse backcross, or (3.52, 4.28, 2.69) for a mouse intercross. (The penalties are in the order (main, heavy interaction, light interaction).) First, let us apply \verb-stepwiseqtl-, considering only additive QTL models (with \verb-additive.only=TRUE-. The algorithm performs forward selection up to a model with a given number of QTL (specified by the argument \verb-max.qtl-; we'll use 6), followed by backward elimination. \usercolor \verb|stepout.a <- stepwiseqtl(hyper, additive.only=TRUE, max.qtl=6)| \\ \verb|stepout.a| \normalcolor I obtained a model with two QTL, with one QTL on each of chr 1 and 4. Now let's re-run the analysis, allowing for the possibility of interactions among the QTL. \usercolor \verb|stepout.i <- stepwiseqtl(hyper, max.qtl=6)| \\ \verb|stepout.i| \normalcolor I obtained a model with four QTL, including one on each of chr 1, 4, 6 and 15, and including an interaction between the loci on chr 6 and 15. \item Note that all of the above could be performed using Haley-Knott regression rather than multiple imputation. Just three changes need to be made. First, one needs to run \verb-calc.genoprob- rather than \verb-sim.geno-, to calculate the QTL genotype probabilities rather than perform imputations. Second, in a call to \verb-makeqtl-, use the argument \verb-what="prob"-, so that the genotype probabilities are placed in the object rather than imputations. Third, in calls to \verb-fitqtl-, \verb-addqtl-, \verb-addpair-, etc., use \verb-method="hk"-. \end{enumerate} %\vspace{12pt} \newpage \noindent \textbf{Example 6: Internal data structure} \vspace{6pt} \nopagebreak \label{example6} \noindent Finally, let us briefly describe the rather complicated data structure that R/qtl uses for QTL mapping experiments. This will be rather dull, and will require a good deal of familiarity with the R (or S) language. The choice of data structure required some balance between ease of programming and simplicity for the user interface. The syntax for references to certain pieces of the internal data can become extremely complicated. \begin{enumerate} \item Get access to some sample data. \usercolor \verb|data(fake.bc)| \normalcolor \item First, the object has a ``class,'' which indicates that it corresponds to data for an experimental cross, and gives the cross type. By having class \verb-cross-, the functions \verb-plot- and \verb-summary- know to send the data to \verb-plot.cross- and \verb-summary.cross-. \usercolor \verb|class(fake.bc)| \normalcolor \item Every \verb-cross- object has two components, one containing the genotype data and genetic maps and the other containing the phenotype data. \usercolor \verb|names(fake.bc)| \normalcolor \item The phenotype data is simply a matrix (more strictly a data.frame) with rows corresponding to individuals and columns corresponding to phenotypes. \usercolor \verb|fake.bc$pheno[1:10,]| \normalcolor %$ \item The genotype data is a list with components corresponding to chromosomes. Each chromosome has a name and a class. The class for a chromosome is either \verb-"A"- or \verb-"X"-, according to whether it is an autosome or the X chromosome. \usercolor \verb|names(fake.bc$geno)| \\ %$ \verb|sapply(fake.bc$geno, class)| %$ \normalcolor \item Each component of \verb-geno- contains two components, \verb-data- (containing the marker genotype data) and \verb-map- (containing the positions of the markers, in cM). \usercolor \verb|names(fake.bc$geno[[3]])| \\ %$ \verb|fake.bc$geno[[3]]$data[1:5,]| \\ \verb|fake.bc$geno[[3]]$map| \normalcolor That's it for the raw data. \item When one runs \verb-calc.genoprob-, \verb-sim.geno-, \verb-argmax.geno- or \verb-calc.errorlod-, the output is the input cross object with the derived data attached to each component (the chromosomes) of the \verb-geno- component. \usercolor \verb|names(fake.bc$geno[[3]])| \\ %$ \verb|fake.bc <- calc.genoprob(fake.bc, step=10, err=0.01)| \\ \verb|names(fake.bc$geno[[3]])| \\ %$ \verb|fake.bc <- sim.geno(fake.bc, step=10, n.draws=8, err=0.01)| \\ \verb|names(fake.bc$geno[[3]])| \\ %$ \verb|fake.bc <- argmax.geno(fake.bc, step=10, err=0.01)| \\ \verb|names(fake.bc$geno[[3]])| \\ %$ \verb|fake.bc <- calc.errorlod(fake.bc, err=0.01)| \\ \verb|names(fake.bc$geno[[3]])|%$ \normalcolor \item Finally, when one runs \verb-est.rf-, a matrix containing the pairwise recombination fractions and LOD scores is added to the cross object. \usercolor \verb|names(fake.bc)| \\ \verb|fake.bc <- est.rf(fake.bc)| \\ \verb|names(fake.bc)| \normalcolor \end{enumerate} \end{document} qtl/inst/doc/Sources/rqtltour2.tex0000644000175100001440000006311612422233634016745 0ustar hornikusers\documentclass[10pt,letterpaper]{article} \usepackage{times} % times font \usepackage{color} % getting colored text \usepackage{amsmath} \usepackage{hyperref} \hypersetup{pdfpagemode=UseNone} % don't show bookmarks on initial view % revise margins \setlength{\headheight}{0.0in} \setlength{\topmargin}{-0.25in} \setlength{\headsep}{0.0in} \setlength{\textheight}{9.5in} \setlength{\footskip}{0.35in} \setlength{\oddsidemargin}{-0.25in} \setlength{\evensidemargin}{-0.25in} \setlength{\textwidth}{7.0in} \setlength{\parindent}{0pt} \setlength{\parsep}{12pt} % font colors \newcommand{\usercolor}{\color [named]{BlueViolet}} \newcommand{\othercolor}{\color [named]{Mahogany}} \definecolor{hrefcolor}{RGB}{0,65,164} \newcommand{\lod}{\text{LOD}} \hypersetup{colorlinks, urlcolor={hrefcolor}} % environment for references \newenvironment{hanging} {\begin{list}{} {\setlength{\labelwidth}{0in} \setlength{\leftmargin}{1em} \setlength{\itemindent}{-1em} } } {\end{list}} \begin{document} \begin{center} \rule{7.0in}{1mm} \vspace{0mm} {\Large \textbf{A shorter tour of R/qtl}} \vspace{4mm} {\large Karl W Broman} \vspace{2mm} Department of Biostatistics and Medical Informatics\\ University of Wisconsin -- Madison \vspace{2mm} \href{http://www.rqtl.org}{http://www.rqtl.org} \vspace{2mm} 26 November 2012 % the date \rule{7.0in}{1mm} \end{center} \textbf{Preliminaries} \vspace{6pt} \begin{enumerate} \item To install R/qtl, type (within R) {\usercolor \verb-install.packages("qtl")-} (This needs to be done just once.) \item To load the R/qtl package, type \usercolor \verb|library(qtl)| \normalcolor This needs to be done every time you start R. (There is a way to have the package loaded automatically every time, but we won't discuss that here.) \item To view the objects in your workspace: \usercolor \verb|ls()| \normalcolor \item The best way to get help on the functions and data sets in R (and in R/qtl) is via the html version of the help files. One way to get access to this is to type \usercolor \verb-help.start()- \normalcolor This should open a browser with the main help menu. If you then click on \othercolor Packages \normalcolor $\rightarrow$ \othercolor qtl\normalcolor , you can see all of the available functions and datasets in R/qtl. For example, look at the help file for the function \verb-read.cross-. An alternative method to view this help file is to type one of the following: \usercolor \verb|help(read.cross)| \\ \verb|?read.cross| \normalcolor The html version of the help files are somewhat easier to read, and allow use of hotlinks between different functions. \item All of the code in this tutorial is available as a file from which you may copy and paste into R, if you prefer that to typing. Type the following within R to get access to the file: \usercolor \verb-url.show("http://www.rqtl.org/rqtltour2.R")- \normalcolor \end{enumerate} \vspace{12pt} \textbf{Data import} \vspace{6pt} We will consider data from Sugiyama et al., Physiol Genomics 10:5--12, 2002. The data are from an intercross between BALB/cJ and CBA/CaJ; only male offspring were considered. There are four phenotypes: blood pressure, heart rate, body weight, and heart weight. We will focus on the blood pressure phenotype, will consider just the 163 individuals with genotype data and, for simplicity, will focus on the autosomes. The data are contained in the comma-delimited file \href{http://www.rqtl.org/sug.csv}{http://www.rqtl.org/sug.csv}. \begin{enumerate} \addtocounter{enumi}{5} \item Load the data into R/qtl as follows. \usercolor \verb|sug <- read.cross("csv", "http://www.rqtl.org", "sug.csv",| \\ \verb| genotypes=c("CC", "CB", "BB"), alleles=c("C", "B"))| \normalcolor \end{enumerate} The function \verb-read.cross- is for importing data into R/qtl. \verb-"sug.csv"- is the name of the file, which we import directly from the R/qtl website. \verb-genotypes- indicates the codes used for the genotypes; \verb-alleles- indicates single-character codes to be used in plots and such. \vspace{12pt} \verb-read.cross- loads the data from the file and formats it into a special cross object, which is then assigned to \verb-sug- via the assignment operator (\verb:<-:). \clearpage \textbf{Diagnostics} \vspace{6pt} Generally, at this point, one would spend considerable time studying the genotype and phenotype data, looking for potential errors. In many cases, about half of the analysis time is devoted to such diagnostics. \vspace{12pt} In previous tutorials, we've often gotten bogged down in this part, and so we'll skip it here, assume that the data are okay, and jump right into QTL mapping. See the longer (``brief'') tour of R/qtl at \href{http://www.rqtl.org/tutorials}{http://www.rqtl.org/tutorials}, or Chapter 3 of Broman and Sen (2009). \vspace{12pt} \textbf{Summaries} \vspace{6pt} \nopagebreak The data object \verb-sug- is complex; it contains the genotype data, phenotype data and genetic map. R has a certain amount of ``object oriented'' facilities, so that calls to functions like \verb-summary- and \verb-plot- are interpreted appropriately for the object considered. The object \verb-sug- has ``class'' \verb-"cross"-, and so calls to \verb-summary- and \verb-plot- are actually sent to the functions \verb-summary.cross- and \verb-plot.cross-. \begin{enumerate} \addtocounter{enumi}{6} \item Get a quick summary of the data. (This also performs a variety of checks of the integrity of the data.) \usercolor \verb|summary(sug)| \normalcolor We see that this is an intercross with 163 individuals. There are 6 phenotypes, and genotype data at 93 markers across the 19 autosomes. The genotype data is quite complete. \item There are a number of simple functions for pulling out pieces of summary information. Hopefully these are self-explanatory. \usercolor \verb|nind(sug)| \\ \verb|nchr(sug)| \\ \verb|totmar(sug)| \\ \verb|nmar(sug)| \\ \verb|nphe(sug)| \normalcolor \item Get a summary plot of the data. \usercolor \verb|plot(sug)| \normalcolor The plot in the upper-left shows the pattern of missing genotype data, with black pixels corresponding to missing genotypes. The next plot shows the genetic map of the typed markers. The following plots are histograms or bar plots for the six phenotypes. The last two ``phenotypes'' are sex (with 1 corresponding to males) and mouse ID. \item Individual parts of the above plot may be obtained as follows. \usercolor \verb|plotMissing(sug)| \\ \verb|plotMap(sug)| \\ \verb|plotPheno(sug, pheno.col=1)| \\ \verb|plotPheno(sug, pheno.col=2)| \\ \verb|plotPheno(sug, pheno.col=3)| \\ \verb|plotPheno(sug, pheno.col=4)| \\ \verb|plotPheno(sug, pheno.col=5)| \\ \verb|plotPheno(sug, pheno.col=6)| \normalcolor \end{enumerate} \vspace{12pt} \textbf{Single-QTL analysis} \vspace{6pt} Let's now proceed to QTL mapping via a single-QTL model. \begin{enumerate} \addtocounter{enumi}{10} \item We first calculate the QTL genotype probabilities, given the observed marker data, via the function \verb-calc.genoprob-. This is done at the markers and at a grid along the chromosomes. The argument \verb-step- is the density of the grid (in cM), and defines the density of later QTL analyses. \usercolor \verb|sug <- calc.genoprob(sug, step=1)| \normalcolor The output of \verb-calc.genoprob- is the same cross object as input, with additional information (the QTL genotype probabilities) inserted. We assign this back to the original object (writing over the previous data), though it could have also been assigned to a new object. \item To perform a single-QTL genome scan, we use the function \verb-scanone-. By default, it performs standard interval mapping (that is, maximum likelihood via the EM algorithm). Also, by default, it considers the first phenotype in the input cross object (in this case, blood pressure). \usercolor \verb|out.em <- scanone(sug)| \normalcolor \item The output has ``class'' \verb-"scanone"-. The \verb-summary- function is passed to the function \verb-summary.scanone-, and gives the maximum LOD score on each chromosome. \usercolor \verb|summary(out.em)| \normalcolor \item Alternatively, we can give a threshold, e.g., to only see those chromosomes with LOD $>$ 3. \usercolor \verb|summary(out.em, threshold=3)| \normalcolor \item We can plot the results as follows. \usercolor \verb|plot(out.em)| \normalcolor \item We can do the genome scan via Haley-Knott regression by calling \verb-scanone- with the argument \verb-method="hk"-. \usercolor \verb|out.hk <- scanone(sug, method="hk")| \normalcolor \item We may plot the two sets of LOD curves together in a single call to \verb-plot-. \usercolor \verb|plot(out.em, out.hk, col=c("blue", "red"))| \normalcolor \item Alternatively, we could do the following: \usercolor \verb|plot(out.em, col="blue")| \\ \verb|plot(out.hk, col="red", add=TRUE)| \normalcolor \item It's perhaps more informative to plot the differences: \usercolor \verb|plot(out.hk - out.em, ylim=c(-0.3, 0.3), ylab="LOD(HK)-LOD(EM)")| \normalcolor \item To perform a genome scan by the multiple imputation method, one must first call \verb-sim.geno- to perform the multiple imputations. This is similar to \verb-calc.genoprob-, but with an additional argument, \verb-n.draws-, indicating the number of imputations. We then call \verb-scanone- with \verb-method="imp"-. \usercolor \verb|sug <- sim.geno(sug, step=1, n.draws=64)| \\ \verb|out.imp <- scanone(sug, method="imp")| \normalcolor \item We may plot all three curves together as follows. \usercolor \verb|plot(out.em, out.hk, out.imp, col=c("blue", "red", "green"))| \normalcolor \item We can plot the LOD curves for just chromosomes 7 and 15 as follows. \usercolor \verb|plot(out.em, out.hk, out.imp, col=c("blue", "red", "green"), chr=c(7,15))| \normalcolor \item We can also look at differences. \usercolor \verb|plot(out.imp - out.em, out.hk - out.em, col=c("green", "red"), ylim=c(-1,1))| \normalcolor \end{enumerate} \vspace{12pt} \textbf{Permutation tests} \vspace{6pt} \nopagebreak To perform a permutation test, to get a genome-wide significance threshold or genome-scan-adjusted p-values, we use \verb-scanone- just as before, but with an additional argument, \verb-n.perm-, indicating the number of permutation replicates. It's quickest to use Haley-Knott regression. \begin{enumerate} \addtocounter{enumi}{23} \item In case the time to perform the permutation test is too long, you can skip it (here) and load the results (that I calculated previously) for this plus other time-consuming stuff we'll see shortly as follows.\label{various} \usercolor \verb|load(url("http://www.rqtl.org/various.RData"))| \normalcolor \item The code to do the actual permutation test is the following: \usercolor \verb|operm <- scanone(sug, method="hk", n.perm=1000)| \normalcolor \item A histogram of the results (the 1000 genome-wide maximum LOD scores) is obtained as follows: \usercolor \verb|plot(operm)| \normalcolor \item Significance thresholds may be obtained via the \verb-summary- function: \usercolor \verb|summary(operm)| \\ \verb|summary(operm, alpha=c(0.05, 0.2))| \normalcolor \item Most importantly, the permutation results may be used along with the \verb-scanone- results to have significance thresholds and p-values calculated automatically: \usercolor \verb|summary(out.hk, perms=operm, alpha=0.2, pvalues=TRUE)| \normalcolor \end{enumerate} \vspace{12pt} \textbf{Interval estimates of QTL location} \vspace{6pt} \nopagebreak For the blood pressure phenotype, we've seen good evidence for QTL on chromosomes 7 and 15. Interval estimates of the location of QTL are commonly obtained via 1.5-LOD support intervals, which may be calculated via the function \verb-lodint-. Alternatively, an approximate Bayes credible interval may be obtained with \verb-bayesint-. \begin{enumerate} \addtocounter{enumi}{28} \item To obtain the 1.5-LOD support interval and 95\% Bayes interval for the QTL on chromosome 7, type: \usercolor \verb|lodint(out.hk, chr=7)| \\ \verb|bayesint(out.hk, chr=7)| \normalcolor The first and last rows define the ends of the intervals; the middle row is the estimated QTL location. \item It is sometimes useful to identify the closest flanking markers; use \verb-expandtomarkers=TRUE-: \usercolor \verb|lodint(out.hk, chr=7, expandtomarkers=TRUE)| \\ \verb|bayesint(out.hk, chr=7, expandtomarkers=TRUE)| \normalcolor \item We can calculate the 2-LOD support interval and the 99\% Bayes interval as follows. \usercolor \verb|lodint(out.hk, chr=7, drop=2)| \\ \verb|bayesint(out.hk, chr=7, prob=0.99)| \normalcolor \item The intervals for the chr 15 locus may be calculated as follows. \usercolor \verb|lodint(out.hk, chr=15)| \\ \verb|bayesint(out.hk, chr=15)| \normalcolor \end{enumerate} \vspace{12pt} \textbf{QTL effects} \vspace{6pt} \nopagebreak We may obtain plots indicating the estimated effects of the QTL via \verb-plotPXG-, which creates a dot plot, or \verb-effectplot-, which plots the average phenotype for each genotype group. \begin{enumerate} \addtocounter{enumi}{32} \item For \verb-plotPXG-, we must first identify the marker closest to the QTL peak. Use \verb-find.marker-. \usercolor \verb|max(out.hk)| \\ \verb|mar <- find.marker(sug, chr=7, pos=47.7)| \\ \verb|plotPXG(sug, marker=mar)| \normalcolor Note that red dots correspond to inferred genotypes (based on a single imputation). \item The function \verb-effectplot- uses the multiple imputation results from \verb-sim.geno-. \usercolor \verb|effectplot(sug, mname1=mar)| \normalcolor \item We may use \verb-effectplot- at a position on the ``grid'' between markers, using \verb-"7@47.7"- to indicate the position at 47.7~cM on chr~7. \usercolor \verb|effectplot(sug, mname1="7@47.7")| \normalcolor \item Similar plots may be obtained for the locus on chr 15. \usercolor \verb|max(out.hk, chr=15)| \\ \verb|mar2 <- find.marker(sug, chr=15, pos=12)| \\ \verb|plotPXG(sug, marker=mar2)| \\ \verb|effectplot(sug, mname1="15@12")| \normalcolor \item We may plot the joint effects of the two loci via \verb-plotPXG- as follows: \usercolor \verb|plotPXG(sug, marker=c(mar, mar2))| \\ \verb|plotPXG(sug, marker=c(mar2, mar))| \normalcolor \item The function \verb-effectplot- gives more readable figures in this case; it's often useful to look at it in both ways. \usercolor \verb|effectplot(sug, mname1="7@47.7", mname2="15@12")| \\ \verb|effectplot(sug, mname2="7@47.7", mname1="15@12")| \normalcolor The two loci do not appear to interact. \end{enumerate} \vspace{12pt} \textbf{Other phenotypes} \vspace{6pt} \nopagebreak By default in \verb-scanone-, we consider the first phenotype in the input cross object. Other phenotypes, include the parallel consideration of multiple phenotypes, can be considered via the argument \verb-pheno.col-. \begin{enumerate} \addtocounter{enumi}{38} \item To analyze the second phenotype, refer to it by its numeric index, as follows. \usercolor \verb|out.hr <- scanone(sug, pheno.col=2, method="hk")| \normalcolor \item Alternatively, refer to a phenotype by its name: \usercolor \verb|out.bw <- scanone(sug, pheno.col="bw", method="hk")| \normalcolor \item You can also give a numeric vector of phenotype values. This is useful for considering a transformed version of a phenotype, such as log body weight. \usercolor \verb|out.logbw <- scanone(sug, pheno.col=log(sug$pheno$bw), method="hk")| \normalcolor \item Use of vector of phenotype indices results in an object with multiple LOD score columns, one for each phenotype. \usercolor \verb|out.all <- scanone(sug, pheno.col=1:4, method="hk")| \normalcolor \item For this final case, it's important to note that the \verb-summary- function, by default, focuses solely on the first LOD score column. \usercolor \verb|summary(out.all, threshold=3)| \normalcolor Here, it looks at the first LOD score column and picks off the peaks that are above 3, and then gives the LOD scores at that location for the other three columns. To do the same thing but focusing on another column, use the argument \verb-lodcolumn-. \usercolor \verb|summary(out.all, threshold=3, lodcolumn=4)| \normalcolor \item Alternatively, use \verb-format="allpeaks"-, to get the maximum LOD score for each column, with a chromosome being shown if at least one of the LOD score column exceeds the threshold. \usercolor \verb|summary(out.all, threshold=3, format="allpeaks")| \normalcolor \item A third version of the output is obtained with \verb-format="allpheno"-, which gives one row per LOD peak and gives the LOD scores for all columns at each peak. \usercolor \verb|summary(out.all, threshold=3, format="allpheno")| \normalcolor \item There are two other formats that might be preferred: \verb-format="tabByCol"- and \verb-format="tabByChr"-. These give tables with one significant LOD peak per phenotype, organized either by phenotype (with \verb-"tabByCol"-) or by chromosome (with \verb-"tabByChr"-). The tables include 1.5-LOD support intervals, and so one may wish to use \verb-"tabByCol"- even if there is only one LOD score column. \usercolor \verb|summary(out.all, threshold=3, format="tabByCol")| \\ \verb|summary(out.all, threshold=3, format="tabByChr")| \normalcolor \end{enumerate} \clearpage \textbf{Two-dimensional, two-QTL scans} \vspace{6pt} \nopagebreak Two-dimensional, two-QTL scans offer the opportunity to detect interacting loci or to separate pairs of linked QTL. Analysis is performed with \verb-scantwo-, which is much like \verb-scanone-. \begin{enumerate} \addtocounter{enumi}{46} \item For 2d scans, it's advantageous to run things at a coarser step size, by first re-running \verb-calc.genoprob-. \usercolor \verb|sug <- calc.genoprob(sug, step=2)| \normalcolor \item To perform a 2d scan for the blood pressure phenotype, use the following. If you loaded the file \verb-"various.RData"- in step~\ref{various} on page~\pageref{various}, you can skip this, as you already have the results. \usercolor \verb|out2 <- scantwo(sug, method="hk")| \normalcolor \item We may plot the results as follows. \usercolor \verb|plot(out2)| \normalcolor The upper-triangle contains interaction LOD scores, comparing the full two-locus model to the additive two-locus model. The lower-triangle contains the ``full'' LOD scores, comparing the full two-locus model to the null model. Because of the clear evidence for QTL on chromosomes 7 and 15, we see ``tails'' along those two chromosomes: the two locus model with either chr 7 or chr 15 and anything else is clearly better than the null model. \item It's best to replace the lower-triangle with the LOD score comparing the full model to the best single-QTL model, using either \verb:lower="cond-int": or \verb:lower="fv1": (the two are equivalent). \usercolor \verb|plot(out2, lower="fv1")| \normalcolor \item We can also look at the LOD scores comparing the additive two-QTL model to the best single-QTL model, using either \verb:upper="cond-add": or \verb:upper="av1":. \usercolor \verb|plot(out2, lower="fv1", upper="av1")| \normalcolor \item To assess significance, we need to do a permutation test. This can be extremely time consuming. The results were already loaded in step~\ref{various} on page~\pageref{various}, but here is the code (though here I cite \verb:n.perm=5: rather than \verb:n.perm=1000:, as I'd recommend). \usercolor \verb|operm2 <- scantwo(sug, method="hk", n.perm=5)| \normalcolor \item With the permutation results in hand, we can get a summary with p-values. \usercolor \verb|summary(out2, perms=operm2, alpha=0.2, pvalues=TRUE)| \normalcolor The pair of loci on 7 and 15 are clear. They show no evidence for an interaction. There is some evidence for an additional locus on chr 12, with p=0.17. \end{enumerate} \vspace{12pt} \textbf{Multiple-QTL analyses} \vspace{6pt} \nopagebreak After performing the single- and two-QTL genome scans, it's best to bring the identified loci together into a joint model, which we then refine from which we may explore the possibility of further QTL. In this effort, we work with ``QTL objects'' created by \verb-makeqtl-. We fit multiple-QTL models with \verb-fitqtl-. A number of additional functions will be introduced below. \begin{enumerate} \addtocounter{enumi}{52} \item Let's re-run \verb-calc.genoprob- so that we are working at a step size of 1~cM again. \usercolor \verb|sug <- calc.genoprob(sug, step=1)| \normalcolor \item First, we create a QTL object containing the loci on chr 7 and 15. \usercolor \verb|qtl <- makeqtl(sug, chr=c(7,15), pos=c(47.7, 12), what="prob")| \normalcolor The last argument, \verb-what="prob"-, indicates to pull out the QTL genotype probabilities for use in Haley-Knott regression. \item We fit the two locus additive model as follows. \usercolor \verb|out.fq <- fitqtl(sug, qtl=qtl, method="hk")| \\ \verb|summary(out.fq)| \normalcolor A key part of the output is the ``drop one term at a time'' table, which compares the fit of the two-QTL model to the reduced model in which a single QTL is omitted. \item We may obtain the estimated effects of the QTL via \verb-get.ests=TRUE-. We use \verb-dropone=FALSE- to suppress the drop-one-term analysis. \usercolor \verb|summary(fitqtl(sug, qtl=qtl, method="hk", get.ests=TRUE, dropone=FALSE))| \normalcolor Since this is an intercross, we obtain estimates of the additive effect and dominance deviation for each locus. \item To assess the possibility of an interaction between the two QTL, we may fit the model with the interaction, indicated via a model ``formula''. The QTL are referred to as \verb-Q1- and \verb-Q2- in the formula, and we may indicate the interaction in a couple of different ways. \usercolor \verb|out.fqi <- fitqtl(sug, qtl=qtl, method="hk", formula=y~Q1*Q2)| \\ \verb|out.fqi <- fitqtl(sug, qtl=qtl, method="hk", formula=y~Q1+Q2+Q1:Q2)| \\ \verb|summary(out.fqi)| \normalcolor We don't have time to cover the use of such formulas in any detail here. Note that there is no evidence for an interaction. \item Another way to assess interactions is with the function \verb-addint-, which adds one interaction at a time, in the context of a multiple-QTL model. This is most useful when there are more than two QTL being considered. \usercolor \verb|addint(sug, qtl=qtl, method="hk")| \normalcolor \item The locations of the two QTL are as estimated via the single-QTL scan. We may refine our estimates of QTL location in the context of the multiple-QTL model via \verb-refineqtl-. This function uses a greedy algorithm to iteratively refines the locations of the QTL, one at a time, at each step seeking to improve the overall fit. \usercolor \verb|rqtl <- refineqtl(sug, qtl=qtl, method="hk")| \\ \verb|rqtl| \normalcolor The location of each QTL changed slightly, and the overall LOD score increased by 0.03. \item We can re-run \verb-fitqtl- to get the revised drop-one-term table. \usercolor \verb|summary(out.fqr <- fitqtl(sug, qtl=rqtl, method="hk"))| \normalcolor \item The \verb-plotLodProfile- function plots LOD profiles obtained during the call to \verb-refineqtl-. These give one-dimensional views of the precision of QTL localization, in the context of the multiple-QTL model. \usercolor \verb|plotLodProfile(rqtl)| \normalcolor For each position on the curve for the chr 7 QTL, we compare the two-QTL model with the chr 7 locus in varying position but with the chr 15 locus fixed at its estimated position, to the single-QTL model with just the chr 15 locus. The chr 15 curve is similar. These are actually slightly lower than the curves obtained from the single-QTL analysis with \verb-scanone-. \usercolor \verb|plot(out.hk, chr=c(7,15), col="red", add=TRUE)| \normalcolor \item The function \verb-addqtl- is used to scan for an additional QTL to be added to the model. \usercolor \verb|out.aq <- addqtl(sug, qtl=rqtl, method="hk")| \normalcolor The biggest peaks are on chr 8 and 12, but nothing is particularly exciting. \usercolor \verb|plot(out.aq)| \normalcolor There is also a function \verb-addpair-, for scanning for a pair of QTL to be added. \item Finally, we consider the function \verb-stepwiseqtl-, which is our fully automated stepwise algorithm to optimize the penalized LOD scores of Manichaikul et al. (2009). We first need to calculate the appropriate penalties from the two-dimensional permutation results. \usercolor \verb|print(pen <- calc.penalties(operm2))| \normalcolor We then run \verb-stepwiseqtl-, using \verb-max.qtl=5-. It will perform forward selection to a model with 5 QTL, followed by backward elimination, and will report the model giving the largest penalized LOD score. The output is a QTL object. \usercolor \verb|out.sq <- stepwiseqtl(sug, max.qtl=5, penalties=pen, method="hk", verbose=2)| \\ \verb|out.sq| \normalcolor With \verb-verbose=2-, we get an indication of the location of the QTL at each step. The result is exactly the model that we had after \verb-refineqtl-. \end{enumerate} \vspace{12pt} \textbf{References} \vspace{6pt} \nopagebreak \begin{hanging} \item Sugiyama F, Churchill GA, Li R, Libby LJM, Carver T, Yagami K-I, John SWM, Paigen B (2002) QTL associated with blood pressure, heart rate, and heart weight in CBA/CaJ and BALB/cJ mice. Physiol Genomics 10:5--12 \item Broman KW, Sen \'S (2009) A guide to QTL mapping with R/qtl. Springer \item Manichaikul A, Moon JY, Sen \'S, Yandell BS, Broman KW (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. Genetics 181:1077--1086 \item Dalgaard P (2008) Introductory statistics with R, 2nd edition. Springer \item Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edition. Springer \end{hanging} \end{document} qtl/inst/doc/Sources/new_summary_scantwo.Rnw0000644000175100001440000003341312422233634021036 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Karl W. Broman % The new summary.scantwo and plot.scantwo % % This is an "Sweave" document; see the corresponding PDF. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass[12pt]{article} %\usepackage{times} \usepackage{amsmath} \usepackage{color} \usepackage{times} % revise margins \setlength{\headheight}{0.0in} \setlength{\topmargin}{-0.25in} \setlength{\headsep}{0.0in} \setlength{\textheight}{9.00in} \setlength{\footskip}{0.5in} \setlength{\oddsidemargin}{0in} \setlength{\evensidemargin}{0in} \setlength{\textwidth}{6.5in} \setlength{\parskip}{6pt} \setlength{\parindent}{0pt} \newcommand{\code}{\texttt} \newcommand{\lod}{\text{LOD}} \begin{document} \SweaveOpts{prefix.string=Figs/scantwo,eps=TRUE} \setkeys{Gin}{width=\textwidth} %% <- change width of figures % Try to get the R code from running into the margin <>= options(width=77) @ % Change S input/output font size \DefineVerbatimEnvironment{Sinput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0, 0, 0.56}}} \DefineVerbatimEnvironment{Soutput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0.56, 0, 0}}} \textbf{The new \code{summary.scantwo} and \code{plot.scantwo}} \\ Karl W Broman, 27 Oct 2006 \\ (Added color 26 Oct 2010; slight change 21 Mar 2012: \verb|summary.scantwo.old| is now \verb|summaryScantwoOld|) \bigskip In R/qtl version 1.04, the functions \code{summary.scantwo} and \code{plot.scantwo} have been changed quite substantially. Also, the permutations with \code{scantwo} have been changed to match the new format for \code{summary.scantwo}. In this document, I describe the revisions and how to use the new functions. We'll look at the \code{hyper} data as an example. First we need to load the package and the data. <>= library(qtl) data(hyper) @ <>= load("hyper_results.RData") @ I'm going to use \code{scantwo} with \code{method="em"}. First I run \code{calc.genoprob}, and then \code{scantwo} as before. <>= hyper <- calc.genoprob(hyper, step=2.5) out2 <- scantwo(hyper) @ The output, in this new version of R/qtl, is slightly different. The LOD scores for the full model (two QTL plus interaction) are there as before, but in place of the epistasis LOD scores, I save the LOD scores for the additive QTL model. Also, we now always run the single-QTL analysis with \code{scanone}, since the results are necessary to make sense of the output of \code{scantwo}. The big change is in \code{summary.scantwo}. Consider a pair of positions in the genome, $s$ and $t$. We consider four models. \begin{eqnarray*} \text{Full: } && y = \mu + \beta_1 q_1 + \beta_2 q_2 + \beta_3 (q_1 \times q_2) + \epsilon \\ \text{Add: } && y = \mu + \beta_1 q_1 + \beta_2 q_2 + \epsilon \\ \text{One: } && y = \mu + \beta_1 q_1 + \epsilon \\ \text{Null: } && y = \mu + \epsilon \end{eqnarray*} Let $l_f(s,t)$ be the log$_{10}$ likelihood for the full model with QTL at $s$ and $t$, $l_a(s,t)$ be the log$_{10}$ likelihood for the additive model with QTL at $s$ and $t$, $l_1(s)$ be the log$_{10}$ likelihood for the single-QTL model with the QTL at $s$, and $l_0$ be the log$_{10}$ likelihood under the null (with no QTL). Define the LOD scores as follows. \begin{eqnarray*} \lod_f(s,t) & = & l_f(s,t) - l_0 \\ \lod_a(s,t) & = & l_a(s,t) - l_0 \\ \lod_1(s) & = & l_1(s) - l_0 \end{eqnarray*} Now for the new part. Following a suggestion from Gary Churchill, we consider a pair of chromosomes $j$ and $k$. (We include the case $j=k$.) Let $c(s)$ denote the chromosome for position $s$. We now consider the maximum LOD scores over that pair of chromosomes. \begin{eqnarray*} M_f(j,k) & = & \max_{c(s)=j, c(t)=k} \lod_f(s,t) \\ M_a(j,k) & = & \max_{c(s)=j, c(t)=k} \lod_a(s,t) \\ M_1(j,k) & = & \max_{c(s)=j \text{ or } k} \lod_1(s) \end{eqnarray*} So $M_f(j,k)$ is the log$_{10}$ likelihood ratio comparing the full model with QTL on chromosomes $j$ and $k$ to the null model, and $M_a(j,k)$ is the analogous thing for the additive model. Note that the pair of positions at which the full model is maximized may be different from the pair of positions at which the additive model is maximized. $M_1(j,k)$ is the log$_{10}$ likelihood ratio comparing the model with a single QTL on either chromosomes $j$ or $k$ to the null model. We derive three further LOD scores from the above. \begin{eqnarray*} M_i(j,k) & = & M_f(j,k) - M_a(j,k) \\ M_{fv1}(j,k) & = & M_f(j,k) - M_1(j,k) \\ M_{av1}(j,k) & = & M_a(j,k) - M_1(j,k) \end{eqnarray*} $M_i(j,k)$ is the log$_{10}$ likelihood ratio comparing the full model with QTL on chromosomes $j$ and $k$ to the additive model with QTL on chromosomes $j$ and $k$, and so indicates evidence for an interaction between QTL on chromosomes $j$ and $k$, assuming that there is precisely one QTL on each chromosome (or, for $j=k$, that there are two QTL on the chromosome). $M_{fv1}(j,k)$ is the log$_{10}$ likelihood ratio comparing the full model with QTL on chromosomes $j$ and $k$ to the single-QTL model, with a single QTL on either chromosome $j$ or $k$. Thus, it indicates evidence for a second QTL, allowing for the possibility of epistasis. $M_{av1}(j,k)$ is the log$_{10}$ likelihood ratio comparing the additive model with QTL on chromosomes $j$ and $k$ to the single-QTL model, with a single QTL on either chromosome $j$ or $k$. Thus, it indicates evidence for a second QTL, assuming no epistasis. In \code{summary.scantwo}, we must provide thresholds for each of the five LOD scores, $M_f(j,k)$, $M_{fv1}(j,k)$, $M_i(j,k)$, $M_a(j,k)$ and $M_{av1}(j,k)$. A pair of chromosomes $(j,k)$ is reported as interesting if either of the following holds: \begin{itemize} \item $M_f(j,k) \ge T_f$ and [$M_{fv1}(j,k) \ge T_{fv1}$ or $M_i(j,k) \ge T_i$] \item $M_a(j,k) \ge T_a$ and $M_{av1}(j,k) \ge T_{av1}$ \end{itemize} I'm inclined towards ignoring $M_i(j,k)$ in this rule (i.e. setting $T_i = \infty$), and using a common significance level ($\alpha$ = 5 or 10\%) for the four remaining thresholds. By default, \code{summary.scantwo} now calculates the five LOD scores above, keeping track of the positions at which $M_f(j,k)$ and $M_a(j,k)$ were maximized. It either prints the best results on all pairs of chromosomes, or we must provide five thresholds ($T_f$, $T_{fv1}$, $T_i$, $T_a$ and $T_{av1}$, in that order). The thresholds can be obtained by a permutation test (see below), but this is extremely time-consuming. For a mouse backcross, we suggest the thresholds (6.0, 4.7, 4.4, 4.7, 2.6) for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores, respectively. For a mouse intercross, we suggest the thresholds (9.1, 7.1, 6.3, 6.3, 3.3) for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores, respectively. These were obtained by 10,000 simulations of crosses with 250 individuals, markers at a 10 cM spacing, and analysis by Haley-Knott regression. <>= summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6)) @ $M_f$, $M_{fv1}$ and $M_i$ are \code{lod.full}, \code{lod.fv1} and \code{lod.int}, respectively, and correspond to positions \code{pos1f} and \code{pos2f}. $M_a$ and $M_{av1}$ are \code{lod.add} and \code{lod.av1}, respectively, and correspond to positions \code{pos1a} and \code{pos2a}. The above is the default output, with \code{what="best"}. The argument \code{what} may also be given as \code{"full"}, \code{"add"}, or \code{"int"}, in which case, for each pair of chromosomes, we pull out the pair of positions with maximum full, additive, or interactive LOD score, respectively, and calculate, for example, the interaction LOD score as the difference between the full and additive LOD scores \emph{for that fixed pair of positions}, rather than allow the full and additive models to be maximized at different positions. (This is more like what we did before, and is included just for completeness.) The same set of five thresholds is required. <>= summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), what="full") summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), what="add") summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), what="int") @ One may also restrict the summary to just the case of $j=k$, to look at evidence for linked QTL on each chromosome, by using \code{allpairs=FALSE}. <>= summary(out2, allpairs=FALSE) @ Note also that the degrees of freedom associated with each LOD score may be displayed, via \code{df=TRUE}: <>= summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), df=TRUE) @ The old version of \code{summary.scantwo} is still available, though it is now called \code{summaryScantwoOld}. <>= summaryScantwoOld(out2, thresholds=c(6, 4, 4)) @ The permutation test with \code{scantwo} has also changed. At each permutation replicate, we record the maxima for each of the $M_f(j,k)$, $M_{fv1}(j,k)$, $M_i(j,k)$, $M_a(j,k)$ and $M_{av1}(j,k)$. The output is given class \code{"scantwoperm"}, and there is a \code{summary.scantwoperm} function for getting LOD thresholds. These permutations can take a very long time, and so one would generally use a multi-processor computer or cluster and do multiple shorter runs in parallel. And so we have added a function \code{c.scantwoperm} for combining such runs together. %In addition, we have added an argument \code{perm.strata} for doing %stratified permutation tests. In the \code{hyper} data, the extremes %were genotyped at most markers, and the rest of the individuals were %genotyped only in selected regions. This suggests that one should do %a stratified permutation test, permuting individuals within %the more completely genotyped group and separately permuting those %within the less completely genotyped group. We could perform the \code{scantwo} permutations in five batches, as follows. <>= operm2A <- scantwo(hyper, n.perm=200) operm2B <- scantwo(hyper, n.perm=200) operm2C <- scantwo(hyper, n.perm=200) operm2D <- scantwo(hyper, n.perm=200) operm2E <- scantwo(hyper, n.perm=200) operm2 <- c(operm2A, operm2B, operm2C, operm2D, operm2E) @ The 5 and 20\% thresholds could be calculated as follows. <>= summary(operm2, alpha=c(0.05,0.20)) @ The permutation results may also be used within the \code{summary.scantwo} function, to automatically calculate the thresholds for desired significance levels. In this case, rather than provide \code{thresholds}, one provides \code{alphas}, which again should be a vector of length 5, giving the significance levels for $M_f(j,k)$, $M_{fv1}(j,k)$, $M_i(j,k)$, $M_a(j,k)$ and $M_{av1}(j,k)$, in that order. <>= summary(out2, perms=operm2, alphas=rep(0.05, 5)) @ My version of the decision rule, in which $M_{i}(j,k)$ is ignored, could be obtained as follows: <>= summary(out2, perms=operm2, alphas=c(0.05, 0.05, 0, 0.05, 0.05)) @ In the case that permutation results are provided, genome-scan-adjusted p-values may also be displayed, via \code{pvalues=TRUE}: <>= summary(out2, perms=operm2, alphas=c(0.05, 0.05, 0, 0.05, 0.05), pvalues=TRUE) @ I have also made an important change in \code{plot.scantwo}. The arguments \code{upper} and \code{lower} control what is plotted in the upper-left and lower-right triangles, respectively. The options are \code{"full"}, \code{"add"}, \code{"cond-int"}, \code{"cond-add"}, and \code{"int"}. The case \code{"full"} is what was previously called \code{"joint"}, but this and the case \code{"add"} are not changed; the LOD scores for the full model (two QTLs plus interaction) and the additive model are displayed. The other two cases, \code{"cond-int"}, and \code{"cond-add"}, are quite different. We now plot the following LOD scores: \begin{eqnarray*} \text{\code{"cond-int"}: } && \lod_{fv1}(s,t) = \lod_f(s,t) - M_1[c(s), c(t)] \\ \text{\code{"cond-add"}: } && \lod_{av1}(s,t) = \lod_a(s,t) - M_1[c(s), c(t)] \end{eqnarray*} When these values are negative, they are replaced with 0. Before, we had subtracted off the maximum of the single-QTL LOD scores at the points $s$ and $t$. Now we subtract off the maximum of the single-QTL LOD scores for the chromosomes containing $s$ and $t$. Note that these LOD scores will be maximized at the same positions as $\lod_f$ and $\lod_a$. Indeed, except for the negative values being changed to 0's, they will have the same shape as $\lod_f$ and $\lod_a$. In the following code, we plot $\lod_f$ in the lower triangle and $\lod_i$ in the upper triangle, for chromosomes 1, 4, 6, and 15. The result appears in Figure~\ref{scantwofull}. <>= plot(out2, chr=c(1,4,6,15)) @ \begin{figure} \centering <>= plot(out2, chr=c(1,4,6,15),layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) @ \caption{LOD scores for selected chromosomes for a two-dimensional scan with the \code{hyper} data. Epistasis LOD scores are in the upper triangle and $\lod_f$ is in the lower triangle.\label{scantwofull}} \end{figure} The same plot, but with the $fv1$-type LOD scores in the upper triangle, would be obtained as follows. The result appears in Figure~\ref{scantwocondint}. <>= plot(out2, chr=c(1,4,6,15), upper="cond-int") @ \begin{figure} \centering <>= plot(out2, chr=c(1,4,6,15), upper="cond-int", layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) @ \caption{LOD scores for selected chromosomes for a two-dimensional scan with the \code{hyper} data. $\lod_{fv1}$ is in the upper triangle and $\lod_f$ is in the lower triangle.\label{scantwocondint}} \end{figure} \end{document} qtl/inst/doc/Sources/geneticmaps.Rnw0000644000175100001440000023354212422233634017236 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Karl W. Broman % The new multiple-qtl mapping functions % % This is an "Sweave" document; see the corresponding PDF. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass[12pt]{article} \usepackage{times} \usepackage{amsmath} \usepackage{url} \usepackage{color} % revise margins \setlength{\headheight}{0.0in} \setlength{\topmargin}{-0.25in} \setlength{\headsep}{0.0in} \setlength{\textheight}{9.00in} \setlength{\footskip}{0.5in} \setlength{\oddsidemargin}{0in} \setlength{\evensidemargin}{0in} \setlength{\textwidth}{6.5in} \setlength{\parskip}{6pt} \setlength{\parindent}{0pt} \newcommand{\code}{\texttt} \newcommand{\lod}{\text{LOD}} % make "Figure" within figure captions a small font \renewcommand{\figurename}{\small Figure} \begin{document} \SweaveOpts{prefix.string=Figs/fig, eps=TRUE} \setkeys{Gin}{width=\textwidth} %% <- change width of figures % Try to get the R code from running into the margin <>= options(width=87, digits=3, scipen=4) set.seed(61777369) @ % function (to be used later) to round numbers in a nicer way. <>= source("myround.R") @ % Change S input/output font size \DefineVerbatimEnvironment{Sinput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0, 0, 0.56}}} \DefineVerbatimEnvironment{Soutput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0.56, 0, 0}}} \begin{center} \vspace*{1in} \textbf{\Large Genetic map construction with R/qtl} \bigskip \bigskip \bigskip \bigskip {\large Karl W. Broman} \bigskip \bigskip {\color{blue} \tt \bfseries University of Wisconsin-Madison \\ Department of Biostatistics \& Medical Informatics \\ Technical Report \# 214 \\[24pt] 4 November 2010 \color{red} Revised 21 March 2012: \color{black} some function names have changed \\ (e.g., \verb|plot.geno| is now \verb|plotGeno|) } \end{center} \bigskip \bigskip \textbf{\sffamily Abstract}: Genetic map construction remains an important prerequisite for quantitative trait loci analysis in organisms for which genomic sequence is not available. Surprisingly little has been written about the construction of genetic maps, particularly regarding the many practical issues involved. We provide an overview of the process, including a description of the key facilities in the R/qtl software. The process is illustrated through simulated data on an F$_2$ intercross derived from two inbred strains. \vfill \noindent \emph{email}: \verb|kbroman@biostat.wisc.edu| \newpage \textbf{\sffamily Introduction} Mouse geneticists no longer have to be concerned with genetic map construction, as the available genomic sequence provides the physical locations and order of genetic markers. Scientists interested in mapping quantitative trait loci (QTL) in other organisms, however, often must first spend considerable time identifying polymorphic markers and then constructing a genetic map specifying the chromosomal locations of the markers. Surprisingly little has been written about the construction of genetic maps. Certainly, papers describing seminal genetic maps include some description of the methods used, but students desire a general overview of the process along with a discussion of key issues that arise and how to overcome them. I attempt to provide such an overview here, along with a description of the key facilities in R/qtl (Broman et al., \emph{Bioinformatics} 18:889--890, 2003; see \url{http://www.rqtl.org}) for genetic map construction. While I describe the use of R/qtl for this purpose in considerable detail, I hope that my general comments are useful for, and that the R code is not an obstacle to, readers interested in the process generally or in other software. R/qtl is principally aimed at the analysis of simple crosses (particularly backcrosses and intercrosses) derived from a pair of inbred strains. R/qtl includes the ability to analyze doubled haploids or a panel of recombinant inbred lines or even a phase-known four-way cross, but here I will focus on the simple case of an intercross between two inbred strains. \bigskip \textbf{\sffamily Experimental design issues} \nopagebreak Before proceeding with our discussion of genetic map construction, let us first describe some of the design issues that a scientist should consider before embarking on such a project. One must first ask: what sort of cross should be performed? As I have in mind a QTL mapping application, I would suggest using the same cross (that is, the same material) for both constructing a genetic map and for the subsequent QTL mapping. It is likely the case that one will need a larger cross for the QTL mapping than for map construction, and also one probably should use a larger set of markers for map construction than for QTL mapping. Thus the ideal strategy may be to produce a large cross, for QTL mapping, but genotype only a portion of that cross on the full set of markers. Once the genetic map is constructed, one may then identify a smaller set of markers, which cover the genome as evenly as possible, to genotype on the remainder of the cross. In spite of the fact that map construction often requires fewer individuals than QTL mapping, as a general rule one should use more than the minimal number of individuals for map construction. Issues such as genotyping errors and segregation distortion are more easily overcome with a larger cross population. Similarly, one should aim for a much larger set of genetic markers than simple calculations about genome size might indicate to be sufficient. In organisms with a mature complement of genomic resources, one may choose an ideal set of markers that evenly cover the genome and that are all well behaved and easily genotyped. In the \emph{de novo\/} construction of a genetic map, however, markers will be developed at random, and so some regions will be densely covered with markers and other regions will be quite thin with markers. Variation in recombination rate (as well as in marker density and polymorphism) will exacerbate this issue. Moreover, one often finds that some markers are very difficult to genotype, and rather than spend considerable time optimizing such markers (such as by redesigning primers or changing PCR conditions), it would be most expedient if such poorly behaved markers could simply be omitted. If one has at hand more markers than are really necessary, it is less of a concern to be dropping 10\% of them. In the actual genotyping, one should always include DNA from the parental lines and F$_1$ individuals as controls, preferably on each plate. Ideally, include the \emph{actual\/} parents and F$_1$ individuals; if residual heterozygosity is identified, the genotypes of these progenitors will be extremely useful for making sense of the data. Additional blind duplicates are useful to give some sense of the overall genotyping error rate. \bigskip \textbf{\sffamily Load the data} \nopagebreak I will consider, as an illustration, a set of simulated data. Real data might be preferred, but by considering simulated data, I can be sure that they feature all of the various issues that I wish to illustrate. Moreover, these features can be made quite striking, so that there will be little question here of what to do. In practice, aberrant features in the data will often be less than clear and may require additional genotyping, or at least a reconsideration of the raw genotyping results, before the appropriate action is clear. The data set is called \code{mapthis} and is included in R/qtl version 1.19-10 and above. This is an intercross between two inbred lines, with 300 individuals genotyped at 100 markers. There is just one ``phenotype,'' which contains individual identifiers. The 100 markers were chosen at random from five chromosomes of length 200, 150, 100, 75 and 50~cM, respectively. The chromosomal locations of the markers are to be treated as unknown, though the marker names do contain this information: marker \code{C2M4} is the fourth marker on chromosome~2. (Note that the chromosomes are all autosomes. I'm not considering the X chromosome in this tutorial.) To load the data, in R, one first needs to load the R/qtl package, using the function \code{library()}. The data set is then loaded via \code{data()}. % load the data <>= library(qtl) data(mapthis) @ In practice, one would use the function \code{read.cross()} to load a data set into R. See the help file (type \code{?read.cross}), look at the sample data sets at \url{http://www.rqtl.org/sampledata}, and consider Chapter~2 of Broman and Sen (2009) \emph{A Guide to QTL Mapping with R/qtl} (\url{http://www.rqtl.org/book}). In assembling the sort of comma-delimited file that R/qtl can read, one should assign all markers to the same chromosome (which can be named whatever is convenient, ``\code{1}'' or ``\code{un}'' or whatever). There is no need to assign map positions to markers. A data file for the \code{mapthis} data is at \url{http://www.rqtl.org/tutorials/mapthis.csv}. It can be read into R as follows. (Note the use of \code{estimate.map=FALSE}. Markers will be assigned dummy locations, with no attempt to estimate inter-marker distances.) % read the data (illustration) <>= mapthis <- read.cross("csv", "http://www.rqtl.org/tutorials", "mapthis.csv", estimate.map=FALSE) @ \bigskip \textbf{\sffamily Omit individuals and markers with lots of missing data} \nopagebreak After importing a new data set, the first thing I look at is a summary of the cross. % summary <>= summary(mapthis) @ I look to make sure that the cross type (in this case, an F$_2$ intercross) was determined correctly and that the numbers of individuals and markers is as expected. The function \code{summary.cross()} also performs a variety of basic checks of the integrity of the data, and so I'd pay attention to any warning or error messages. Next, I look at the pattern of missing data, through the function \code{plotMissing()}. % plot missing data pattern <>= plotMissing(mapthis) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,0.6,1.1)) plotMissing(mapthis, main="") @ \caption{Pattern of missing genotype data in the \code{mapthis} dataset. Black pixels indicate missing genotypes.\label{fig:plotmissing}} \end{figure} The result (in Fig.~\ref{fig:plotmissing}) indicates several individuals with a great deal of missing data (horizontal lines), as well as several markers with a great deal of missing data (vertical lines). The function \code{ntyped()} provides the numbers of genotyped markers for each individual (or the number of genotyped individuals for each marker). Let us plot these. (And note that there is a related function, \code{nmissing()}, which provides the number of missing genotypes for each individual or marker.) % plot of number of genotypes <>= par(mfrow=c(1,2), las=1) plot(ntyped(mapthis), ylab="No. typed markers", main="No. genotypes by individual") plot(ntyped(mapthis, "mar"), ylab="No. typed individuals", main="No. genotypes by marker") @ \begin{figure} \centering <>= par(mfrow=c(1,2), las=1, cex=0.8) plot(ntyped(mapthis), ylab="No. typed markers", main="No. genotypes by individual") plot(ntyped(mapthis, "mar"), ylab="No. typed individuals", main="No. genotypes by marker") @ \caption{Plot of number of genotyped markers for each individual (left panel) and number of genotyped individuals for each marker (right panel).\label{fig:plotntyped}} \end{figure} As seen in Fig.~\ref{fig:plotntyped}, there are six individuals missing almost all genotypes, and there are four markers that are missing genotypes at about half of the individuals. Such appreciable missing data often indicates a problem (either bad DNAs or difficult-to-call markers) and can lead to difficulties in the genetic map construction, and so it is best, at this stage, to omit them, though one might consider adding them back in later. To omit the individuals with lots of missing genotype data, we may use the function \code{subset.cross()}, as follows. % drop individuals with missing data <>= mapthis <- subset(mapthis, ind=(ntyped(mapthis)>50)) @ To omit the markers with lots of missing data, we first need to identify the names of the markers. We then use \code{drop.markers()}. % drop markers with missing data <>= nt.bymar <- ntyped(mapthis, "mar") todrop <- names(nt.bymar[nt.bymar < 200]) mapthis <- drop.markers(mapthis, todrop) @ %We now have 294 individuals and 96 markers: %% nind; totmar %<>= %nind(mapthis) %totmar(mapthis) %@ \bigskip \textbf{\sffamily Identify duplicate individuals} \nopagebreak I find it useful to compare the genotypes between all pairs of individuals, in order to reveal pairs with unusually similar genotypes, which may indicate sample duplications or monozygotic twins. In either case, we will want to remove one individual from each pair. Such duplicates are not common, but they are also not rare. We use the function \code{comparegeno} to compare the genotypes for all pairs of individuals. The output is a matrix, whose contents are the proportions of markers at which the pairs have matching genotypes. % compare individuals' genotypes <>= cg <- comparegeno(mapthis) hist(cg[lower.tri(cg)], breaks=seq(0, 1, len=101), xlab="No. matching genotypes") rug(cg[lower.tri(cg)]) @ \begin{figure} \centering <>= cg <- comparegeno(mapthis) par(mar=c(4.1,4.1,0.1,0.6),las=1) hist(cg[lower.tri(cg)], breaks=seq(0, 1, len=101), xlab="No. matching genotypes", main="") rug(cg[lower.tri(cg)]) @ \caption{Histogram of the proportion of markers for which pairs of individuals have matching genotypes.\label{fig:comparegenoplot}} \end{figure} As seen in Fig.~\ref{fig:comparegenoplot}, a pair of F$_2$ siblings typically share genotypes at \Sexpr{round(mean(cg[lower.tri(cg)])*100,-1)}\% of the markers. But there are some pairs with well over 90\% matching genotypes. We may identify these pairs as follows. % identify matching pairs <>= wh <- which(cg > 0.9, arr=TRUE) wh <- wh[wh[,1] < wh[,2],] wh @ We may inspect the genotype matches for these pairs with the following. % matching genotypes <>= g <- pull.geno(mapthis) table(g[144,], g[292,]) table(g[214,], g[216,]) table(g[238,], g[288,]) @ As seen above, in each case, the pairs have matching genotypes at all but 2 or 3 markers. Ideally, one would go back to the records to try to assess the source of these problems (e.g., are the pairs from the same litters?). Here, we will simply omit one individual from each pair. But first we will omit the genotypes that mismatch, as these are indicated to be errors in one or the other individual (or both). The R code is a bit complicated. % zero out the mismatches <>= for(i in 1:nrow(wh)) { tozero <- !is.na(g[wh[i,1],]) & !is.na(g[wh[i,2],]) & g[wh[i,1],] != g[wh[i,2],] mapthis$geno[[1]]$data[wh[i,1],tozero] <- NA } @ Now, we omit one individual from each pair. % omit individuals <>= mapthis <- subset(mapthis, ind=-wh[,2]) @ It's also useful to look for duplicate markers (that is, markers with identical genotypes). This is particularly true for the case of very large sets of markers; multiple markers with identical genotypes will invariably map to the same location, and so one might as well thin out the markers so that there are no such duplicates, as the extra markers simply slow down all of our analyses. The function \code{findDupMarkers()} may be used to identify markers with matching genotypes. (Note that the function \code{drop.dupmarkers()} is for dropping markers with matching \emph{names}, and considers the genotypes only in order to establish consensus genotypes across the multiple markers with the same name). Here, though, there are no markers with matching genotypes. % look for duplicate markers <>= print(dup <- findDupMarkers(mapthis, exact.only=FALSE)) @ \bigskip \textbf{\sffamily Look for markers with distorted segregation patterns} \nopagebreak We next study the segregation patterns of the markers. We expect the genotypes to appear with the frequencies 1:2:1. Moderate departures from these frequencies are not unusual and may indicate the presence of partially lethal alleles. Gross departures from these frequencies often indicate problematic markers that should be omitted, at least initially: monomorphic markers (that is, where the two parental lines actually have the same allele), or markers that are especially difficult to call (e.g., AA are often called as AB). We use the function \code{geno.table} to inspect the segregation patterns. It calculates the genotype frequencies and also a P-value for a test of departure from the 1:2:1 expected ratios. We will focus on those markers that show significant distortion at the 5\% level, after a Bonferroni correction for the multiple tests. % inspect segregation patterns <>= gt <- geno.table(mapthis) gt[gt$P.value < 0.05/totmar(mapthis),] @ % $ The first of these markers, \code{C4M2}, is not terrible. The others appear to be monomorphic with a few errors (\code{C2M9} and \code{C2M15}) or have one genotype that is quite rare, which likely indicates difficulties in genotyping. It would be best to omit the worst of these markers. % drop bad markers <>= todrop <- rownames(gt[gt$P.value < 1e-10,]) mapthis <- drop.markers(mapthis, todrop) @ % $ \bigskip \textbf{\sffamily Study individuals' genotype frequencies} \nopagebreak Just as we expect the markers to segregate 1:2:1, we expect the individuals to have genotype frequencies that are in approximately those proportions. Studying such genotype frequencies may help to identify individuals with high genotyping error rates or some other labeling or breeding mistake. There's no R/qtl function to to pull out the genotype frequencies by individual, but we can write a bit of R code to do so. It is a bit nasty, with calls to \code{apply()}, \code{table()}, \code{factor()}, \code{colSums()} and \code{t()}, but hopefully the reader can figure this out after a bit of exploration and introspection. <>= g <- pull.geno(mapthis) gfreq <- apply(g, 1, function(a) table(factor(a, levels=1:3))) gfreq <- t(t(gfreq) / colSums(gfreq)) par(mfrow=c(1,3), las=1) for(i in 1:3) plot(gfreq[i,], ylab="Genotype frequency", main=c("AA", "AB", "BB")[i], ylim=c(0,1)) @ \begin{figure} \centering <>= g <- pull.geno(mapthis) gfreq <- apply(g, 1, function(a) table(factor(a, levels=1:3))) gfreq <- t(t(gfreq) / colSums(gfreq)) par(mfrow=c(1,3), las=1) for(i in 1:3) plot(gfreq[i,], ylab="Genotype frequency", main=c("AA", "AB", "BB")[i], ylim=c(0,1)) @ \caption{Genotype frequencies by individual.\label{fig:plotgenofreqbyind}} \end{figure} The results in Fig.~\ref{fig:plotgenofreqbyind} do not indicate any particular problems, though the small number of short chromosomes result in considerable variability, including one individual with no BB genotypes, and the frequencies of AB genotypes varies from \Sexpr{round(min(gfreq[2,])*100)}--\Sexpr{round(max(gfreq[2,])*100)}\%. However, if there were an individual with $\sim$ 100\% AA or BB genotypes (like one of the parental strains), we would see it here. A more fancy, and potentially more clear view of these genotype frequencies is obtained by representing them as points in an equilateral triangle (see Fig.~\ref{fig:triangleplot}). For any point within an equilateral triangle, the sum of the distances to the three sides is constant, and so one may represent a trinomial distribution as a point within the triangle. I won't show the code, but note that the red X in the center of the figure corresponds to the expected genotype frequencies (1/4, 1/2, 1/4). Each black dot corresponds to an individual's genotype frequencies. There's one point along the right edge; this corresponds to the individual with no BB genotypes. Again, the small size of the genome results in enormous variation, and so no one individual stands out as unreasonable. \begin{figure} \centering <>= source("holmans_triangle.R") par(mar=rep(0.1,4), pty="s") triplot(labels=c("AA","AB","BB")) tripoints(gfreq, cex=0.8) tripoints(c(0.25, 0.5, 0.25), col="red", lwd=2, cex=1, pch=4) @ \caption{Genotype frequencies by individual, represented on an equilateral triangle. The red X indicates the expected frequencies of 1:2:1.\label{fig:triangleplot}} \end{figure} \bigskip \textbf{\sffamily Study pairwise marker linkages; look for switched alleles} \nopagebreak We are now in a position to begin the genetic map construction. We start by assessing the linkage between all pairs of markers. The function \code{est.rf()} is used to estimate the recombination fraction between each pair and to calculate a LOD score for a test of rf = 1/2. But first note that, in the presence of appreciable segregation distortion (which is not the case for these data), unlinked markers can appear to be linked. For example, consider the following 2$\times$2 table of two-locus genotypes in a backcross. \begin{center} \begin{tabular}{cccc} \hline & \multicolumn{2}{c}{\textbf{Marker 2}} \\ \cline{2-3} \textbf{Marker 1} & AA & AB & overall\\ \hline AA & 243 & 27 & 270 \\ AB & 27 & 3 & 30 \\ \hline overall & 270 & 30 & 300 \\ \hline \end{tabular} \end{center} In this case, the two markers are segregating independently but show severe segregation distortion. (They segregate 9:1 rather than 1:1.) The usual estimate of the recombination fraction is (27+27)/300 = 0.18, and the LOD score for the test of rf = 1/2 is $\sim$28.9. If segregation distortion is rampant in a dataset (and such things do happen), the usual tests of pairwise linkage will give distorted results, and so it is best to instead use a simple chi-square or likelihood ratio test, to assess the association between markers. The function \code{markerlrt()} behaves just like \code{est.rf()}, but uses a general likelihood ratio test in place of the usual test of pairwise linkage. Now back to the data, which are not so badly behaved. % pairwise linkages <>= mapthis <- est.rf(mapthis) @ \begin{Schunk} \begin{Soutput} Warning message: In est.rf(mapthis) : Alleles potentially switched at markers C3M16 C2M16 C1M2 C3M9 C2M14 C1M24 C1M1 C2M12 C1M36 C3M1 C2M25 C1M22 C5M5 C5M7 C1M17 C5M1 C3M5 C1M15 C2M24 C2M17 C1M23 C5M6 C1M16 C3M2 C3M10 C3M6 C2M13 \end{Soutput} \end{Schunk} Note the warning message, which indicates that there are numerous markers with likely switched alleles (A $\leftrightarrow$ B). This is indicated through pairs of markers that are strongly indicated to be associated but have estimated recombination fractions $\gg$ 1/2. The \code{checkAlleles()} function gives more detailed information on this issue. % checkAlleles <>= checkAlleles(mapthis, threshold=5) @ The final column in the output for a marker, \code{diff.in.max.LOD}, is the difference between the maximum LOD score for the cases where the estimated recombination fraction is $>$ 1/2 and the maximum LOD score for the cases where the estimated recombination fraction is $<$ 1/2. There are a large number of markers that are tightly associated with other markers, but with recombination fractions well above 1/2, which indicates that some markers likely have their alleles switched. A plot of the LOD scores against the estimated recombination fractions for all marker pairs will give another good view of this problem. We use the function \code{pull.rf()} to pull out the recombination fractions and LOD scores as matrices. <>= rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") @ \begin{figure} \centering <>= rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") par(mar=c(4.1,4.1,0.6,0.6), las=1, cex=0.8) plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") @ \caption{Plot of LOD scores versus estimated recombination fractions for all marker pairs.\label{fig:lodvrf}} \end{figure} As seen in Fig.~\ref{fig:lodvrf}, there are many marker pairs with large LOD scores but estimated recombination fractions $\gg$ 1/2. One solution to this problem is to form initial linkage groups, ensuring that markers with rf $>$ 1/2 are placed in different groups. If all goes well, each chromosome will come out as a pair of linkage groups: one containing markers with correct alleles and another containing markers with switched alleles. We use the function \code{formLinkageGroups()} to infer linkage groups. It uses the pairwise linkage results from \code{est.rf()} (or, pairwise association information from \code{markerlrt()}). The function has two main arguments: \code{max.rf} and \code{min.lod}. Two markers will be placed in the same linkage groups if they have estimated recombination fraction $\le$ \code{max.rf} and LOD score $\ge$ \code{min.lod}. The linkage groups are closed via the transitive property. That is, if markers a and b are linked and markers b and c are linked, then all three are placed in the same linkage group. I generally start with \code{min.lod} relatively large (say 6 or 8 or even 10). The appropriate value depends on the number of markers and chromosomes (and individuals). The aim is to get as many truly linked markers together, but avoid completely putting unlinked markers in the same linkage group. It is usually easier to combine linkage groups after the fact rather than to have to split linkage groups apart. <>= lg <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6) table(lg[,2]) @ The output of \code{formLinkageGroups()} is a matrix with two columns: the initial linkage group or chromosome for each marker, and then the assigned linkage group, as inferred from the pairwise linkage information. The inferred linkage groups are numbered in decreasing order of size (so that linkage group 1 has the largest number of markers). Here we see that \Sexpr{length(table(lg[,2]))} linkage groups were inferred, with the last \Sexpr{sum(table(lg[,2])==1)} groups having just one marker. One may play with \code{max.rf} and \code{min.lod} until the result is about as expected. Here, I was hoping for about 10 linkage groups (since there are five chromosomes but each will likely be split in two due to the allele switching problem), and so the results seem okay. Since we are happy with the results, we can reorganize the markers into these inferred linkage groups. We do so with the same function, \code{formLinkageGroups()}, via the argument \code{reorgMarkers}. <>= mapthis <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6, reorgMarkers=TRUE) @ A plot of the pairwise recombination fractions and LOD scores may indicate how well this worked. <>= plotRF(mapthis, alternate.chrid=TRUE) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,2.1,2.1), las=1) plotRF(mapthis, main="", alternate.chrid=TRUE) @ \caption{Plot of estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of markers. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction).\label{fig:plotrf}} \end{figure} The result, in Fig.~\ref{fig:plotrf}, looks about as expected. The markers within linkage group 1 are linked to each other. The pattern within the group is a bit random, but that is because we have not yet ordered the markers in any way. Note that linkage groups 4 and 5 are associated with each other, in that they have large LOD scores (the lower-right rectangle for groups 4 and 5 is largely red), but their recombination fractions are not small (the upper-left rectangle for groups 4 and 5 is strongly blue). I would infer from this that these markers belong to the same chromosome, but the alleles in one or the other group are switched. We can't know which is correct, and ideally one would have parental genotype data to help fix these problems, but it is not unreasonable, for now, to assume that the larger group of markers corresponds to the ones with the correct alleles. Similarly, groups 6 and 7 belong together, group 8 belongs to group 2, and groups 9--11 belong to group 1. We can look at this more clearly by picking out a marker from one group and studying the recombination fractions and LOD scores for that marker against all others. Let us pick the third marker in linkage group 4. We create the objects \code{rf} and \code{lod} again, using \code{pull.rf()}. These objects have class \code{"rfmatrix"}, and so \code{plot} below actually goes to the function \code{plot.rfmatrix()}, which plots the values for a single marker in a display similar to a set of LOD curves. <>= rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") mn4 <- markernames(mapthis, chr=4) par(mfrow=c(2,1)) plot(rf, mn4[3], bandcol="gray70", ylim=c(0,1), alternate.chrid=TRUE) abline(h=0.5, lty=2) plot(lod, mn4[3], bandcol="gray70", alternate.chrid=TRUE) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,1.1,0.6), las=1) <> @ \caption{Plot of estimated recombination fractions (top panel) and LOD scores (bottom panel) for the marker \code{\Sexpr{mn4[3]}} against all other markers.\label{fig:plotrfonemarker}} \end{figure} As seen in Fig.~\ref{fig:plotrfonemarker}, the marker \code{\Sexpr{mn4[3]}} is strongly associated with markers in both linkage groups 4 and 5, but the estimated recombination fractions between \code{\Sexpr{mn4[3]}} and the markers on linkage group 4 are all $<$ 1/2, while the estimated recombination fractions between \code{\Sexpr{mn4[3]}} and the markers on linkage group 5 are all $>$ 1/2. We can see the problem even more clearly by inspecting a few tables of two-locus genotypes, produced by \code{geno.crosstab()}. <>= geno.crosstab(mapthis, mn4[3], mn4[1]) mn5 <- markernames(mapthis, chr=5) geno.crosstab(mapthis, mn4[3], mn5[1]) @ It is clear that, if the genotypes for \code{\Sexpr{mn4[3]}} are correct, then the A and B alleles for \code{\Sexpr{mn5[1]}} are switched. In practice, I will look at enormous numbers of these sorts of two-locus genotype tables in order to figure out what is going on. But these data are (by design) quite clean, and so we will simply trust that the alleles need to be switched for all of the markers on linkage groups 5 and 7--11. The function \code{switchAlleles()} is convenient for performing the allele switching. <>= toswitch <- markernames(mapthis, chr=c(5, 7:11)) mapthis <- switchAlleles(mapthis, toswitch) @ Now when we revisit the plot of recombination fractions and LOD scores, we will see a quite different picture. (Note that we need to re-run \code{est.rf()} after having run \code{switchAlleles()}.) <>= mapthis <- est.rf(mapthis) plotRF(mapthis, alternate.chrid=TRUE) @ \begin{figure} \centering <>= mapthis <- est.rf(mapthis) par(mar=c(4.1,4.1,2.1,2.1), las=1) plotRF(mapthis, main="", alternate.chrid=TRUE) @ \caption{Plot of estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of markers, after having switched alleles for many markers. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction).\label{fig:plotrfagain}} \end{figure} As seen in Fig.~\ref{fig:plotrfagain}, the LOD scores between marker pairs are unchanged, but now the recombination fractions are small (indicated in red) for marker pairs with evidence of association (large LOD scores, also in red). It is useful to revisit the plot of LOD scores versus recombination fractions for all pairs. <>= rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") @ \begin{figure} \centering <>= rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") par(mar=c(4.1,4.1,0.6,0.6), las=1, cex=0.8) plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") @ \caption{Plot of LOD scores versus estimated recombination fractions for all marker pairs, after alleles at many markers have been switched.\label{fig:lodvrfagain}} \end{figure} Now we see (in Fig.~\ref{fig:lodvrfagain}) no estimated recombination fractions that are much above 1/2, and certainly no large recombination fractions with large LOD scores. In fact, the largest LOD score for marker pairs with estimated recombination fraction $>$ 1/2 is \Sexpr{round(max(lod[!is.na(rf) & rf>0.5]), 2)}. \bigskip \textbf{\sffamily Form linkage groups} \nopagebreak We now should have the genotype data in good shape and can finally proceed to the actual map construction. First, we again attempt to infer the linkage groups, with the hope that we'll come away with exactly five. <>= lg <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6) table(lg[,2]) @ Right; we've got five groups. Now we reorganize the markers again. <>= mapthis <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6, reorgMarkers=TRUE) @ It is useful to plot the pairwise recombination fractions and LOD scores again. <>= plotRF(mapthis) @ \begin{figure} \centering <>= mapthis <- est.rf(mapthis) par(mar=c(4.1,4.1,1.6,1.6), las=1) plotRF(mapthis, main="") @ \caption{Plot of estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of markers, after markers have been placed in their final linkage groups. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction).\label{fig:plotrfyetagain}} \end{figure} As seen in Fig.~\ref{fig:plotrfyetagain}, we have five clear linkage groups, with markers within a group linked to one another, and markers in distinct groups showing no evidence of linkage. The results couldn't possibly be cleaner (and they wouldn't be, if these were real data). \bigskip \textbf{\sffamily Order markers on chromosome~5} \nopagebreak We now turn to the problem of ordering markers within each linkage group. (We could go ahead and call them chromosomes at this point.) I always start with the chromosome with the fewest markers, as there are fewer possible orders and so the process is quicker. I like to see some progress before I move to the larger chromosomes. The function \code{orderMarkers()} may be used to establish an initial order. It picks an arbitrary pair of markers, and then adds an additional marker (chosen at random), one at a time, in the best supported position. With the argument \code{use.ripple=TRUE}, the function \code{ripple()} is used after the addition of each marker, to consider all possible orders in a sliding window of markers, to attempt to improve marker order. The argument \code{window} defines the length of the window. Larger values will explore more possible orders but will require considerably more computation time. The value \code{window=7} is usually about the largest one would ever consider; use of \code{window=8} will take so long that one would not want to sit and wait. Note that the other arguments to \code{orderMarkers} (e.g., \code{error.prob} and \code{map.function}) are passed to the function \code{est.map()} for estimation of the genetic map with the final order that is chosen. Also note that \code{ripple()}, in this case, chooses among orders in order to minimize the number of obligate crossovers. More on this below. <>= mapthis <- orderMarkers(mapthis, chr=5) @ <>= file <- "Rcache/order5.RData" if(file.exists(file)) { load(file) } else { <> save(mapthis, file=file) } @ We may use \code{pull.map()} to inspect the result. <>= pull.map(mapthis, chr=5) @ Since the marker names were chosen to indicate the true marker order, we can see that we got the order exactly right. (Though note that the second marker, \code{C5M2} was omitted at some point during the course of our analysis.) Of course, we wouldn't know this, and so we should make an attempt to explore alternate orders, in case another order might be seen to be an improvement. We use \code{ripple()} to explore alternate orders. As mentioned above, it considers all possible orders of markers in a sliding window of fixed length, with the argument \code{window} defining the width of the window. If \code{window} is equal to the number of markers on the chromosome, than all possible orders are considered. The quickest approach is to count the number of obligate crossovers for each order. The good orders generally are those that result in the smallest numbers of crossovers. The more refined, but considerably slower approach, is to compare the likelihoods of the different orders. (The likelihood for a given marker order is the probability of the data assuming that order is correct and plugging in estimates of the inter-marker distances.) We do this with \code{method="likelihood"} in \code{ripple()}. We may also indicate a genotyping error probability (through \code{error.prob}). While we could start by comparing orders to minimize the number of obligate crossovers (with \code{method="countxo"}, the default, in \code{ripple()}), this was already done when we called \code{orderMarkers()}, and it is not necessary to run it again. Nevertheless, the results will indicate how close, in terms of number of crossovers, the next-best marker order is to the inferred one. <>= file <- "Rcache/rip5.RData" if(file.exists(file)) { load(file) } else { rip5 <- ripple(mapthis, chr=5, window=7) save(rip5, file=file) } @ <>= rip5 <- ripple(mapthis, chr=5, window=7) @ \begin{Schunk} \begin{Soutput} 13680 total orders \end{Soutput} \end{Schunk} <>= summary(rip5) @ As seen above, 13,680 marker orders were considered. (There are $9!/2$ = 181,440 total marker orders.) On my computer, the code above took about 1.5 seconds. If we'd looked at all possible orders (that is, with \code{window=9}), it would take about 14 seconds, but the results---I checked---are the same.) Switching markers 8 and 9 results in one additional obligate crossover. If, instead, one switches markers 5 and 6, there are two additional obligate crossovers. It is good to also study the likelihood of different orders, though we will want to greatly reduce the \code{window} argument, so that it can be accomplished in a reasonable amount of time. <>= file <- "Rcache/rip5lik.RData" if(file.exists(file)) { load(file) } else { rip5lik <- ripple(mapthis, chr=5, window=4, method="likelihood", error.prob=0.005) save(rip5lik, file=file) } @ <>= rip5lik <- ripple(mapthis, chr=5, window=4, method="likelihood", error.prob=0.005) @ \begin{Schunk} \begin{Soutput} 114 total orders \end{Soutput} \end{Schunk} <>= summary(rip5lik) @ The result (on my computer) took about two minutes. We see that switching markers 8 and 9 has a LOD score (that is, log$_{10}$ likelihood, relative to the initial order) of \Sexpr{round(rip5lik[2,"LOD"], 1)}, which indicates that it is slightly preferred. However, the estimated chromosome length is slightly longer (\Sexpr{round(rip5lik[2,"chrlen"], 1)} versus \Sexpr{round(rip5lik[1,"chrlen"], 1)}~cM). We know, from the marker names, that the initial order was the true order, but in practice we would not have such information, and we would probably want to switch markers 8 and 9. Usually we are looking to have the estimated chromosome length be as short as possible, but if we trust the likelihood calculation, we should go with the alternate order. Of course, the two orders are not really distinguishable; we can't really say, on the basis of these data, whether the correct order is the first or the second. Note that the results here can be sensitive to the assumed genotyping error rate. (I chose \code{error.prob=0.005} above, because the data were simulated with this error rate.) The function \code{compareorder()} can be used to compare an initial order to a fixed alternative order. We can use this to quickly inspect how sensitive the results are to the assumed error rate. <>= compareorder(mapthis, chr=5, c(1:7,9,8), error.prob=0.01) compareorder(mapthis, chr=5, c(1:7,9,8), error.prob=0.001) compareorder(mapthis, chr=5, c(1:7,9,8), error.prob=0) @ These results indicate that with smaller assumed genotyping error rates, the evidence in favor of switching markers 8 and 9 increases somewhat. Note also that the map length increases quite a bit. If we were looking at these data blindly, I would likely go with switching markers 8 and 9, so let's go ahead and do that here. We use \code{switch.order()} to do so. It takes the same sort of arguments as \code{est.map()}, as after the marker order is switched, \code{est.map()} is called to revise the estimated map for the chromosome. <>= mapthis <- switch.order(mapthis, chr=5, c(1:7,9,8), error.prob=0.005) pull.map(mapthis, chr=5) @ Note that the map is slightly smaller than what we had seen above, after running \code{orderMarkers()}, as we had used the default value for \code{error.prob} in that function, and now we are using \code{error.prob=0.005}. Also note that markers \code{C5M10} and \code{C5M9} are quite close together and are separated from the next marker by \Sexpr{round(diff(pull.map(mapthis, chr=5)[[1]][7:8]),1)}~cM. This explains why it is difficult to assess the appropriate order for these two markers. \bigskip \textbf{\sffamily Order markers on chromosome~4} \nopagebreak We now turn to chromosome~4. First, we run \code{orderMarkers()} and print out the estimated map. <>= mapthis <- orderMarkers(mapthis, chr=4) pull.map(mapthis, chr=4) @ <>= file <- "Rcache/order4.RData" if(file.exists(file)) { load(file) } else { <> save(mapthis, file=file) } pull.map(mapthis, chr=4) @ The marker names tell us the true order, and so we see that the inferred order is correct except that markers \code{C4M10} and \code{C4M9} are switched. Let us run \code{ripple()}, to see which orders have similar numbers of obligate crossovers. <>= file <- "Rcache/rip4.RData" if(file.exists(file)) { load(file) } else { rip4 <- ripple(mapthis, chr=4, window=7) save(rip4, file=file) } @ <>= rip4 <- ripple(mapthis, chr=4, window=7) @ \begin{Schunk} \begin{Soutput} 18000 total orders \end{Soutput} \end{Schunk} <>= summary(rip4) @ The order with markers 9 and 10 switched gives the same number of obligate crossovers, and so we can not distinguish between these two marker orders. We turn to the likelihood comparison. <>= file <- "Rcache/rip4lik.RData" if(file.exists(file)) { load(file) } else { rip4lik <- ripple(mapthis, chr=4, window=4, method="likelihood", error.prob=0.005) save(rip4lik, file=file) } @ <>= rip4lik <- ripple(mapthis, chr=4, window=4, method="likelihood", error.prob=0.005) @ \begin{Schunk} \begin{Soutput} 132 total orders \end{Soutput} \end{Schunk} <>= summary(rip4lik) @ There is reasonably good evidence to switch markers 9 and 10 (which is nice, as this results in the true marker order). <>= mapthis <- switch.order(mapthis, chr=4, c(1:8,10,9), error.prob=0.005) pull.map(mapthis, chr=4) @ \bigskip \textbf{\sffamily Order markers on chromosome~3} \nopagebreak Turning to chromosome~3, we again run \code{orderMarkers()}. These calculations are starting to take quite a bit of time. It would all be faster if we reduced the argument \code{window} to a smaller value (say 4 rather than 7), but then not as many alternate orders will be explored and so we may not identify the best order. <>= mapthis <- orderMarkers(mapthis, chr=3) pull.map(mapthis, chr=3) @ <>= file <- "Rcache/order3.RData" if(file.exists(file)) { load(file) } else { <> save(mapthis, file=file) } pull.map(mapthis, chr=3) @ Note, from the marker names, that the marker order is the true order. The whole chromosome is flipped, but we have no information, from the genotype data, to orient the chromosome. Let us again use \code{ripple()} to study alternate orders. <>= file <- "Rcache/rip3.RData" if(file.exists(file)) { load(file) } else { rip3 <- ripple(mapthis, chr=3, window=7) save(rip3, file=file) } @ <>= rip3 <- ripple(mapthis, chr=3, window=7) @ \begin{Schunk} \begin{Soutput} 39600 total orders \end{Soutput} \end{Schunk} <>= summary(rip3) @ The next-best order, with markers 2 and 3 switched, results in an additional \Sexpr{diff(rip3[1:2,"obligXO"])} obligate crossovers. We turn to the likelihood comparison. <>= file <- "Rcache/rip3lik.RData" if(file.exists(file)) { load(file) } else { rip3lik <- ripple(mapthis, chr=3, window=4, method="likelihood", error.prob=0.005) save(rip3lik, file=file) } @ <>= rip3lik <- ripple(mapthis, chr=3, window=4, method="likelihood", error.prob=0.005) @ \begin{Schunk} \begin{Soutput} 222 total orders \end{Soutput} \end{Schunk} <>= summary(rip3lik) @ The next-best order (with markers 2 and 3 switched) is considerably worse than our initial order. \bigskip \textbf{\sffamily Order markers on chromosome~2} \nopagebreak We turn to chromosome~2, beginning with \code{orderMarkers()}. <>= mapthis <- orderMarkers(mapthis, chr=2) pull.map(mapthis, chr=2) @ <>= file <- "Rcache/order2.RData" if(file.exists(file)) { load(file) } else { <> save(mapthis, file=file) } pull.map(mapthis, chr=2) @ Again, as seen from the marker names, the inferred order is the true one. Let us run \code{ripple()} to inspect alternate orders. <>= file <- "Rcache/rip2.RData" if(file.exists(file)) { load(file) } else { rip2 <- ripple(mapthis, chr=2, window=7) save(rip2, file=file) } @ <>= rip2 <- ripple(mapthis, chr=2, window=7) @ \begin{Schunk} \begin{Soutput} 78480 total orders \end{Soutput} \end{Schunk} <>= summary(rip2) @ The next-best order, with markers 3 and 4 switched, has \Sexpr{diff(rip2[1:2,"obligXO"])} additional obligate crossovers. We turn to the likelihood comparison. <>= file <- "Rcache/rip2lik.RData" if(file.exists(file)) { load(file) } else { rip2lik <- ripple(mapthis, chr=2, window=4, method="likelihood", error.prob=0.005) save(rip2lik, file=file) } @ <>= rip2lik <- ripple(mapthis, chr=2, window=4, method="likelihood", error.prob=0.005) @ \begin{Schunk} \begin{Soutput} 384 total orders \end{Soutput} \end{Schunk} <>= summary(rip2lik) @ The next-best order, in terms of likelihood, has markers 21 and 22 switched, and is considerably worse than our initial order. It is interesting to compare the numbers of obligate crossovers for different orders to the LOD scores. We have LOD scores for a much smaller number of orders (\Sexpr{nrow(rip2lik)} versus \Sexpr{nrow(rip2) %/% 1000},\Sexpr{nrow(rip2) %% 1000}), since we had used a smaller value for \code{window}, but we can pull out the obligate crossover counts for those orders that we evaluated in terms of likelihood. It is a bit tricky to line up the orders. <>= pat2 <- apply(rip2[,1:24], 1, paste, collapse=":") pat2lik <- apply(rip2lik[,1:24], 1, paste, collapse=":") rip2 <- rip2[match(pat2lik, pat2),] plot(rip2[,"obligXO"], rip2lik[,"LOD"], xlab="obligate crossover count", ylab="LOD score") @ \begin{figure} \centering <>= par(las=1, mar=c(4.1,4.1,1.1,0.1), cex=0.8) <> @ \caption{Comparison of the number of obligate crossovers to the LOD score (relative to our inferred order), for chromosome~2 marker orders explored via \code{ripple()}.\label{fig:comparexo2lik}} \end{figure} As seen in Fig.~\ref{fig:comparexo2lik}, there is a clear negative relationship between the crossover counts and the likelihoods, though the relationship is not perfect, particularly for the orders that are not well supported. \bigskip \textbf{\sffamily Order markers on chromosome~1} \nopagebreak Finally, we turn to chromosome~1, first running \code{orderMarkers()}. <>= mapthis <- orderMarkers(mapthis, chr=1) pull.map(mapthis, chr=1) @ <>= file <- "Rcache/order1.RData" if(file.exists(file)) { load(file) } else { <> save(mapthis, file=file) } pull.map(mapthis, chr=1) @ Again, the inferred order is precisely the true order. I was quite surprised to see how well \code{orderMarkers()} performed with these data. Of course, the data are quite clean (by design) and comprise quite a large number of individuals. In practice, one can't expect \code{orderMarkers()} to perform so well, and it is worthwhile to study the estimated map and the pairwise linkage information closely. We will do so in a moment, but first let's complete our analysis of chromosome~1 by studying the results of \code{ripple}. <>= file <- "Rcache/rip1.RData" if(file.exists(file)) { load(file) } else { rip1 <- ripple(mapthis, chr=1, window=7) save(rip1, file=file) } @ <>= rip1 <- ripple(mapthis, chr=1, window=7) @ \begin{Schunk} \begin{Soutput} 117360 total orders \end{Soutput} \end{Schunk} <>= summary(rip1) @ Two alternate orders (switching markers 28 and 29, or switching markers 31 and 32) have 2 additional obligate crossovers, compared to our initial order. We turn to the likelihood comparison. <>= file <- "Rcache/rip1lik.RData" if(file.exists(file)) { load(file) } else { rip1lik <- ripple(mapthis, chr=1, window=4, method="likelihood", error.prob=0.005) save(rip1lik, file=file) } @ <>= rip1lik <- ripple(mapthis, chr=1, window=4, method="likelihood", error.prob=0.005) @ \begin{Schunk} \begin{Soutput} 546 total orders \end{Soutput} \end{Schunk} <>= summary(rip1lik) @ The next best order (switching markers 28 and 29) results in a moderately large decrease in likelihood, and so we stick with the initial order produced by \code{orderMarkers()}. Let us return to the question of how to assess the quality of the results of \code{orderMarkers()}, aside from our investigations with \code{ripple()}. The \code{ripple()} analysis is excellent for comparing nearby marker orders. And for chromosomes with a modest number of markers (like chromosome~5, here), one can consider all possible orders exhaustively. But with many markers (as with chromosome~1), we can investigate only a small proportion of the possible orders. For example, with 33 markers (as on chromosome~1), there are $33!/2 \approx 10^{36}$ possible marker orders, and we investigated only 117,360 of them using \code{ripple} with \code{window=7}. There are several things to look it, in assessing whether gross changes in marker order are necessary. (Ideally, R/qtl would include a function providing a more complete investigation of marker order, perhaps via simulated annealing or another randomized optimization algorithm, and we do hope to implement such a feature in the future.) First, we should look at the actual map: are there large gaps between markers, indicating adjacent markers that are only weakly linked? Studying the map locations with \code{pull.map()}, as above, is hard when there are lots of markers. The function \code{summaryMap()} provides a summary of the average inter-marker distance and the largest gap on each chromosome. <>= summaryMap(mapthis) @ <>= firstsummary <- summaryMap(mapthis) @ We see that chromosome~1 has a \Sexpr{round(summaryMap(mapthis)[1,"max.spacing"],1)}~cM gap; the other chromosomes have gaps no larger than 25~cM. The large gap on chromosome~1 is suspicious, but it is not terrible. In addition to this summary, it is good to also plot the map. We use the argument \code{show.marker.names=TRUE} so that marker names are included. Most of them are unreadable, because the markers are densely spaced, but at least we learn the identity of markers that are surrounded by large gaps. <>= plotMap(mapthis, show.marker.names=TRUE) @ \begin{figure} \centering <>= par(las=1, mar=c(4.1,4.1,1.1,0.1), cex=0.8) plotMap(mapthis, main="", show.marker.names=TRUE) @ \caption{Plot of the estimated genetic map, after the markers on each chromosome have been ordered.\label{fig:plotmap}} \end{figure} As seen in Fig.~\ref{fig:plotmap}, there are some large gaps on chromosome~1 and some smaller gaps on the other chromosomes. They deserve further investigation (and we will do so in the next section), but they aren't particularly troubling. With gross mistakes in marker order, one will often see much larger gaps, say $>$ 100~cM. Finally, we inspect the pairwise linkage information again. <>= plotRF(mapthis) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,1.6,1.6), las=1) plotRF(mapthis, main="") @ \caption{Plot of estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of markers, after ordering the markers on each chromosome. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction).\label{fig:plotrfonemoretime}} \end{figure} The pairwise linkage information in Fig.~\ref{fig:plotrfonemoretime} is close to what we want to see: nearby markers show clear association and no distant markers show any association: red along the diagonal, dissipating to blue away from the diagonal. If there were gross problems with marker order, we might see groups of distantly placed markers that are more highly associated than more closely placed markers. Just as an example, suppose we split chromosome~1 into three pieces and moved the latter piece into the middle. Let's look at the pairwise linkage information following such a re-ordering. <>= messedup <- switch.order(mapthis, chr=1, c(1:11,23:33,12:22), error.prob=0.005) plotRF(messedup, chr=1) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,1.6,1.6), las=1, pty="s", cex=0.8) messedup <- switch.order(mapthis, chr=1, c(1:11,23:33,12:22), error.prob=0.005) plotRF(messedup, chr=1, main="") @ \caption{Plot of estimated recombination fractions (upper-left triangle) and LOD scores (lower-right triangle) for all pairs of markers on chromosome~1, after messing up the order of the markers. Red indicates linked (large LOD score or small recombination fraction) and blue indicates not linked (small LOD score or large recombination fraction).\label{fig:plotrfafterreorder}} \end{figure} Fig.~\ref{fig:plotrfafterreorder} (on page~\pageref{fig:plotrfafterreorder}) displays the sort of pattern one should expect with gross problems in marker order: the markers in the first and last segments are more tightly associated to each other than they are to the markers in the middle segment. A plot of the genetic map (see Fig.~\ref{fig:plotmapmessedup}, page~\pageref{fig:plotmapmessedup}) shows a 50~cM gap between the first and middle segments and an 80~cM gap between the middle and last segments. <>= plotMap(messedup, show.marker.names=TRUE) @ \begin{figure} \centering <>= par(las=1, mar=c(4.1,4.1,1.1,0.1), cex=0.8) plotMap(messedup, main="", show.marker.names=TRUE) @ \caption{Plot of the estimated genetic map, after messing up the order of markers on chromosome~1.\label{fig:plotmapmessedup}} \end{figure} The map is not as telling as the pairwise linkage information, but these are the sorts of things to look at, in trying to decide whether there are gross problems that need to be corrected. If features such as those in Fig.~\ref{fig:plotrfafterreorder} and \ref{fig:plotmapmessedup} were seen, one should identify the segments of markers that need to be moved around, and then use \code{switch.order()} to reorganize the markers. One might first use \code{compareorder()} to compare the current order to the reorganized one, to see that the new order gave a clear improvement in likelihood. In addition, one will often need to alternate between \code{ripple()} and \code{switch.order()} until the final marker order is established. \bigskip \textbf{\sffamily Drop one marker at a time} \nopagebreak The large gaps in the genetic map on chromosome~1 remain a concern. While such gaps may indicate problems with the order of markers, they might also indicate a high genotyping error rate at certain markers. If an individual marker is more prone to genotyping errors than others, it would often (in the sort of analyses performed above) be placed at one end of the chromosome or the other, but in some cases (particularly if the genotyping error rate is not high and there are a large number of individuals in the cross) it may be placed at approximately the correct position but result in reasonably large gaps surrounding the marker. One approach for identifying such problematic markers is to drop one marker at a time and investigate the change in chromosome length and the change in log likelihood. This analysis may be accomplished with the function \code{droponemarker()}. <>= dropone <- droponemarker(mapthis, error.prob=0.005) @ <>= file <- "Rcache/dropone.RData" if(file.exists(file)) { load(file) } else { dropone <- droponemarker(mapthis, error.prob=0.005) save(dropone, file=file) } @ The results are of the same form as a genome scan by QTL mapping. (In particular, they have class \code{"scanone"}, like an object produced by the \code{scanone()} function.) We may plot the results as follows. <>= par(mfrow=c(2,1)) plot(dropone, lod=1, ylim=c(-100,0)) plot(dropone, lod=2, ylab="Change in chromosome length") @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,1.6,0.1), mfrow=c(2,1), cex=0.8) plot(dropone, lod=1, ylim=c(-100,0)) plot(dropone, lod=2, ylab="Change in chr length (cM)") @ \caption{Results of dropping one marker at a time. The top panel contains LOD scores; positive values would indicate that dropping a marker improves the likelihood. The bottom panel indicates the decrease in estimated chromosome length (in cM) following dropping a marker.\label{fig:plotdropone}} \end{figure} As seen in top panel of Fig.~\ref{fig:plotdropone}, there is no one marker for which its omission results in an increase in likelihood, but as seen in the bottom panel, there are a number of markers that give an appreciable decrease in chromosome length, of 15--25 cM, when omitted. Markers at the ends of chromosomes will often result in a smaller estimated chromosome length, but that is just because such terminal markers hang off some distance from the rest of the markers, and so these changes can often be discounted. Interior markers that result in a big change, and there appears to be one on each of chromosomes 1, 2 and 3, might indicate error-prone markers that are best omitted. One may identify the marker on each chromosome whose omission results in the largest decreases in chromosome length as follows. <>= summary(dropone, lod.column=2) @ For chromosomes 4 and 5, these are terminal markers. The markers on chromosomes 1, 2 and 3 are all interior markers. One should probably study the pairwise linkage between these markers and surrounding markers before proceeding, but we will go ahead and remove these markers without any further investigations. <>= badmar <- rownames(summary(dropone, lod.column=2))[1:3] mapthis <- drop.markers(mapthis, badmar) @ One should re-estimate the genetic map. We use \code{replace.map()} to insert it into the cross object. <>= newmap <- est.map(mapthis, error.prob=0.005) mapthis <- replace.map(mapthis, newmap) summaryMap(mapthis) @ <>= secondsummary <- summaryMap(mapthis) @ Removing those three markers resulted in a decrease in the overall map length from \Sexpr{round(firstsummary["overall","length"])}~cM to \Sexpr{round(secondsummary["overall","length"])}~cM. \bigskip \textbf{\sffamily Look for problem individuals} \nopagebreak Now that we have the markers in their appropriate order, it is a good idea to return to the question of whether there are particular individuals whose data are problematic. One should ask: if a particular individual showed considerable genotyping errors or did not actually belong to the cross under investigation (e.g., through some sort of labeling or breeding error), what sort of aberrations might be seen in the data? Above, we studied the amount of missing genotype data for each individual as well as the individuals' genotype frequencies. Another feature to investigate is the observed number of crossovers in each individual. These may be counted with the function \code{countXO()}. (A related function \code{locateXO()} will return the estimated locations of all crossovers.) <>= plot(countXO(mapthis), ylab="Number of crossovers") @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,0.6,0.6), cex=0.8) <> thecounts <- countXO(mapthis) worst <- rev(sort(thecounts, decreasing=TRUE)[1:2]) @ \caption{Numbers of observed crossovers in each individual.\label{fig:countxo}} \end{figure} The crossover counts in Fig.~\ref{fig:countxo} clearly indicate two problematic individuals, with \Sexpr{worst[1]} and \Sexpr{worst[2]} crossovers; the other individuals have \Sexpr{min(thecounts)}--\Sexpr{max(thecounts[thecounts < worst[1]])} crossovers. We should remove these individuals. <>= mapthis <- subset(mapthis, ind=(countXO(mapthis) < 50)) @ Ideally, we would now revisit the entire process again; in particular, after removing these problematic individuals, is there evidence that marker order needs to be changed? Let us at least look at chromosome 5. <>= summary(rip <- ripple(mapthis, chr=5, window=7)) summary(rip <- ripple(mapthis, chr=5, window=2, method="likelihood", error.prob=0.005)) @ There is good evidence for switching markers 8 and 9, which actually brings us back to the true order of the markers. <>= mapthis <- switch.order(mapthis, chr=5, c(1:7,9,8), error.prob=0.005) pull.map(mapthis, chr=5) @ I investigated the other four chromosomes similarly, and there was no evidence for further changes in marker order, so I won't present the results here. We should, finally, re-estimate the genetic map. <>= newmap <- est.map(mapthis, error.prob=0.005) mapthis <- replace.map(mapthis, newmap) summaryMap(mapthis) @ <>= thirdsummary <- summaryMap(mapthis) @ The overall map length has decreased further, from \Sexpr{round(secondsummary["overall","length"])}~cM to \Sexpr{round(thirdsummary["overall","length"])}~cM. \bigskip \textbf{\sffamily Estimate genotyping error rate} \nopagebreak Above, I had generally assumed a genotyping error rate of 5/1000 (in estimating map distances and in likelihood calculations comparing different marker orders); but I cheated somewhat in using this value, as the genotype data were simulated with this error rate. One can actually estimate the genotyping error rate from the data, as the function \code{est.map()} not only estimates the inter-marker distances, but also calculates the log likelihood for each chromosome. Thus, if we run \code{est.map()} with different assumed values for the genotyping error rate (specified with the \code{error.prob} argument), one can identify the maximum likelihood estimate of the error rate. <>= loglik <- err <- c(0.001, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02) for(i in seq(along=err)) { cat(i, "of", length(err), "\n") tempmap <- est.map(mapthis, error.prob=err[i]) loglik[i] <- sum(sapply(tempmap, attr, "loglik")) } lod <- (loglik - max(loglik))/log(10) @ <>= file <- "Rcache/errorrate.RData" if(file.exists(file)) { load(file) } else { <> save(err, lod, file=file) } @ The code is a bit tricky, mostly because the log likelihoods (and note that they are on the natural log scale) are included as ``attributes'' to each chromosome component in the output from \code{est.map()}. We use \code{sapply()} to pull those out, and then we add them up. We finally convert them to the log$_{10}$ scale and re-center them so that the maximum is 0. We may plot the log$_{10}$ likelihood as follows. <>= plot(err, lod, xlab="Genotyping error rate", xlim=c(0,0.02), ylab=expression(paste(log[10], " likelihood"))) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,0.6,0.6), las=1) <> @ \caption{The log$_{10}$ likelihood for the genotyping error rate.\label{fig:errorratelik}} \end{figure} The log$_{10}$ likelihood in Fig.~\ref{fig:errorratelik} indicates that the MLE is approximately 0.005. Error rates of 0.0025 and 0.0075 have log$_{10}$ likelihoods that are 3 less than that of the MLE. We might investigate additional assumed error rates, or even use the R function \code{optimize()} to refine our estimate, but we won't pursue that here. \bigskip \textbf{\sffamily Look for genotyping errors} \nopagebreak While the methods for estimating inter-marker distances allow for the presence of genotyping errors at a fixed rate, it is nevertheless worthwhile to look for, and ideally correct, potential genotyping errors in the data. Such errors may be identified through apparent tight double-crossovers, with a single marker being out of phase with its adjacent markers. The most convenient approach for identifying such double-crossovers is to calculate genotyping error LOD scores, first developed by Lincoln and Lander (\emph{Genomics\/} 14:604--610, 1992). The LOD score compares the likelihood for a genotype being in error versus it not being in error. R/qtl uses a modified calculation of such genotyping error LOD scores, with all genotypes except that being considered assumed to be strictly correct. The error LOD scores are calculated with \code{calc.errorlod()}. One must assume a genotyping error rate, but the results are almost identical for a wide range of values. <>= mapthis <- calc.errorlod(mapthis, error.prob=0.005) @ <>= file <- "Rcache/errorlod.RData" if(file.exists(file)) { load(file) } else { <> save(mapthis, file=file) } @ The function \code{top.errorlod()} produces a list of the genotypes with the largest error LOD scores. One may generally focus on those with quite large values, say at least 4--5. Here will we look at just those genotypes with error LOD $\ge$ 6; this is indicated with the argument \code{cutoff}. <>= print(toperr <- top.errorlod(mapthis, cutoff=6)) @ There are \Sexpr{nrow(toperr)} genotypes that meet this criterion. Let us look at the genotypes that were flagged on chromosome 1, using the function \code{plotGeno()}. The argument \code{cutoff} indicates a threshold for flagging genotypes as potential errors, based on their error LOD scores. If we had used the argument \code{include.xo=TRUE} (which is the default), inferred locations of crossovers would be displayed; we suppress that here to get a more clean figure. <>= plotGeno(mapthis, chr=1, ind=toperr$id[toperr$chr==1], cutoff=6, include.xo=FALSE) @ \begin{figure} \centering <>= par(mar=c(4.1,4.1,0.6,0.6), las=1, cex.axis=0.9) plotGeno(mapthis, chr=1, ind=toperr$id[toperr$chr==1], main="", cex=0.8, include.xo=FALSE, cutoff=6) @ \caption{Genotypes on chromosome 1 for individuals with some potential errors flagged by red squares. White, gray and black circles correspond to AA, AB and BB genotypes, respectively.\label{fig:plotgeno}} \end{figure} The results are in Fig.~\ref{fig:plotgeno}. All of the flagged genotypes are cases with an exchange from one homozygote to the other and then back again (thus, two crossovers in each interval flanking a single marker). One might zero out these suspicious genotypes (that is, make them missing). Even better would be to revisit the raw genotyping information, or even re-genotype these instances. But we are talking about just \Sexpr{nrow(toperr)} genotypes out of \Sexpr{sum(ntyped(mapthis)) %/% 1000},\Sexpr{sum(ntyped(mapthis)) %% 1000}, and with our allowance for genotyping errors in the map estimation, they have little influence on the results. If we did wish to delete these genotypes, we could do so as follows. <>= mapthis.clean <- mapthis for(i in 1:nrow(toperr)) { chr <- toperr$chr[i] id <- toperr$id[i] mar <- toperr$marker[i] mapthis.clean$geno[[chr]]$data[mapthis$pheno$id==id, mar] <- NA } @ %$ \bigskip \textbf{\sffamily Revisit segregation distortion} \nopagebreak Finally, let us return to an investigation of segregation distortion in these data. <>= gt <- geno.table(mapthis, scanone.output=TRUE) par(mfrow=c(2,1)) plot(gt, ylab=expression(paste(-log[10], " P-value"))) plot(gt, lod=3:5, ylab="Genotype frequency") abline(h=c(0.25, 0.5), lty=2, col="gray") @ \begin{figure} \centering <>= gt <- geno.table(mapthis, scanone.output=TRUE) par(mar=c(4.1,4.1,0.6,0.6), las=1, mfrow=c(2,1), cex=0.8) plot(gt, ylab=expression(paste(-log[10], " P-value"))) plot(gt, lod=3:5, ylab="Genotype frequency") abline(h=c(0.25, 0.5), lty=2, col="gray") @ \caption{Evidence for segregation distortion: $-$log$_{10}$ P-values from tests of 1:2:1 segregation at each marker (top panel) and the genotype frequencies at each marker (bottom panel, with black, blue and red denoting AA, AB and BB genotypes, respectively).\label{fig:segdis}} \end{figure} The top panel of Fig.~\ref{fig:segdis} contains $-$log$_{10}$ P-values from tests of 1:2:1 segregation at each marker. The bottom panel in Fig.~\ref{fig:segdis} contains the observed genotype frequencies at each marker (with black, blue and red corresponding to AA, AB and BB genotypes, respectively). The greatest departure from 1:2:1 segregation is on chromosome 4, with somewhat more AA genotypes and somewhat fewer BB genotypes. If we apply a Bonferroni correction for the \Sexpr{totmar(mapthis)} tests (\Sexpr{totmar(mapthis)} is the total number of markers we have retained in the data), we would look for P $\ge$ 0.05/\Sexpr{totmar(mapthis)} which corresponds to $-$log$_{10}$ P $\ge$ \Sexpr{round(-log10(0.05/totmar(mapthis)),2)}, and there is one marker on chromosome 4 that exceeds this. There are also some departures from 1:2:1 segregation on chromosomes 2 and 3, but these appear to be within the range of what would be expected by chance; the evidence for a real departure from normal segregation is not strong. The aberrant segregation pattern on chromosome 4 is not too worrisome. Multipoint estimates of genetic map distances are little affected by segregation distortion, and the pattern of distortion on the chromosome indicates rather smooth changes in genotype frequency. More worrisome would be a single distorted marker in the midst of other markers with normal segregation, which would indicate genotyping errors rather than, for example, the presence of partially lethal alleles. And so, finally, we're done. Let us plot the final map. <>= plotMap(mapthis, show.marker.names=TRUE) @ \begin{figure} \centering <>= par(las=1, mar=c(4.6,4.6,0.6,0.6), cex=0.8) plotMap(mapthis, main="", show.marker.names=TRUE) @ \caption{Plot of the final estimated genetic map\label{fig:plotfinalmap}} \end{figure} The map in Fig.~\ref{fig:plotfinalmap} is not pretty, as most of the marker names are obscured. R/qtl does not produce production-quality figures automatically; I always go through quite a few extra contortions within R to produce a figure suitable for a paper. \bigskip \textbf{Discussion} \nopagebreak The process of genetic map construction seems to be at least 90\% data diagnostics. While many might find that frustrating, for me that is a large part of what makes it fun: it is interesting detective work. I have been involved in the construction of genetic maps for humans, mice, dogs, zebrafish, mosquitoes, and sea squirts. Each project was different, with its own special issues that needed to be overcome, and such issues can seldom be anticipated in advance. The general strategy is to think about what sorts of things might be going wrong with data, and then what sorts of features of the data (summary statistics or plots) might indicate the presence of such problems. There are a variety of things to check routinely, and note that the particular order in which these checks are performed is often important. In the procedures described above, and in forming these simulated data, I attempted to cover most of the possible problems that might be expected to arise. With the simple intercross considered here, and particularly as the data were simulated so that the problems would generally be quite clear, the decisions about how to proceed were easy to make. In practice, potential problems in data will often be more murky, and careful judgment calls will need to be made and frequently revisited, ideally with careful consideration of raw genotyping data and other records, and perhaps even with some additional rounds of genotyping or even the redesign of genotyping assays. Moreover, with more complex experiments, such as an outcross or the combined analysis of multiple crosses, additional issues will arise that we have not touched on here. But the overall strategy, laid out above, can be applied quite generally. \newpage {\small \noindent \textbf{\sffamily R/qtl functions useful for genetic map construction} \\ \noindent \begin{tabular}{ll} \hspace*{25mm} & \hspace*{103mm} \\ \hline plotMissing & Plot pattern of missing genotypes \\ ntyped & Count number of typed markers for each individual \\ nmissing & Count number of missing genotypes for each individual \\ subset.cross & Pull out a specified set of chromosomes and/or individuals from a cross \\ drop.markers & Remove a list of markers \\ pull.markers & Drop all but a selected set of markers \\ drop.nullmarkers & Remove markers without data \\ comparegeno & Count proportion of matching genotypes between all pairs of individuals \\ findDupMarkers & Find markers with identical genotype data \\ drop.dupmarkers & Drop duplicate markers \\ geno.table & Create table of genotype distributions \\ est.rf & Estimate pairwise recombination fractions \\ markerlrt & General likelihood ratio test for association between marker pairs \\ checkAlleles & Identify markers with potentially switched alleles \\ pull.rf & Pull out the pairwise recombination fractions or LOD scores as a matrix \\ formLinkageGroups & Partition markers into linkage groups \\ plotRF & Plot recombination fractions \\ markernames & Pull out the marker names from a cross \\ plot.rfmatrix & Plot a slice through the pairwise recombination fractions or LOD scores \\ geno.crosstab & Create cross-tabulation of genotypes at two markers \\ switchAlleles & Switch alleles at selected markers \\ orderMarkers & Find an initial order for markers within chromosomes \\ ripple & Assess marker order by permuting groups of adjacent markers \\ summary.ripple & Print summary of ripple output \\ est.map & Estimate genetic map \\ pull.map & Pull out the genetic map from a cross \\ compareorder & Compare two orderings of markers on a chromosome \\ switch.order & Switch the order of markers on a chromosome \\ summaryMap & Print summary of a genetic map \\ plotMap & Plot genetic map(s) \\ droponemarker & Drop one marker at a time from a genetic map \\ replace.map & Replace the genetic map of a cross \\ countXO & Count number of obligate crossovers for each individual \\ locateXO & Estimate locations of crossovers \\ calc.errorlod & Calculate Lincoln \& Lander (1992) error LOD scores \\ top.errorlod & List genotypes with highest error LOD values \\ plotGeno & Plot genotypes on a particular chromosomes for selected individuals \\ \hline % tryallpositions & Test all possible positions for a marker \\ allchrsplits & Test all possible splits of a chromosome into two pieces \\ movemarker & Move a marker from one chromosome to another \\ convert.map & Change map function for a genetic map \\ shiftmap & Shift starting points in genetic maps \\ rescalemap & Rescale genetic map \\ \hline \end{tabular} } \end{document} qtl/inst/doc/Sources/new_summary_scanone.Rnw0000644000175100001440000001671212422233634021011 0ustar hornikusers%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Karl W. Broman % The new summary.scanone % % This is an "Sweave" document; see the corresponding PDF. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass[12pt]{article} %\usepackage{times} \usepackage{amsmath} \usepackage{color} \usepackage{times} % revise margins \setlength{\headheight}{0.0in} \setlength{\topmargin}{-0.25in} \setlength{\headsep}{0.0in} \setlength{\textheight}{9.00in} \setlength{\footskip}{0.5in} \setlength{\oddsidemargin}{0in} \setlength{\evensidemargin}{0in} \setlength{\textwidth}{6.5in} \setlength{\parskip}{6pt} \setlength{\parindent}{0pt} \newcommand{\code}{\texttt} \newcommand{\lod}{\text{LOD}} \begin{document} \SweaveOpts{prefix.string=Figs/scanone} \setkeys{Gin}{width=\textwidth} %% <- change width of figures % Try to get the R code from running into the margin <>= options(width=77) @ % Change S input/output font size \DefineVerbatimEnvironment{Sinput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0, 0, 0.56}}} \DefineVerbatimEnvironment{Soutput}{Verbatim}{fontsize=\footnotesize, baselinestretch=0.75, formatcom = {\color[rgb]{0.56, 0, 0}}} \textbf{The new \code{summary.scanone}} \\ Karl W Broman, 27 Oct 2006 \\ (Added color 26 Oct 2010) \bigskip In R/qtl version 1.04, the function \code{summary.scanone} has been changed quite substantially. Also, the permutations with \code{scanone} have changed to allow the calculation of autosome- and X-chromosome-specific LOD thresholds, and to enable stratified permutation tests. In this document, I describe the revisions and how to use the new functions. We'll first look at the \code{fake.f2} data as an example. First we need to load the package and the data. <>= library(qtl) data(fake.f2) @ <>= load("fakef2_results.RData") @ I'm going to use \code{scanone} with \code{method="hk"}. First I run \code{calc.genoprob}, and then \code{scanone} as before. <>= fake.f2 <- calc.genoprob(fake.f2, step=2.5) out.f2 <- scanone(fake.f2, method="hk") @ In \code{summary.scanone}, we can now get an indication of the number of degrees of freedom associated with the LOD scores. We use \code{df=TRUE}, as follows. <>= summary(out.f2, threshold=3, df=TRUE) @ There are a couple of improvements in the permutations performed by \code{scanone}. First, we can calculate autosome- and X-chromosome-specific LOD thresholds; this is important in this case, as the number of degrees of freedom is different for the X chromosome. Separate autosome and X chromosome permutations may be performed in \code{scanone} via \code{perm.Xsp=TRUE}. The X-chromosome-specific thresholds requires many more permutation replicates to get a threshold of equivalent precision. An increased number of permutations is chosen automatically. Permutations can take a very long time, and so one might want to use a multi-processor computer or cluster and do multiple shorter runs in parallel. And so we have added a function \code{c.scanoneperm} for combining such runs together. <>= operm1.f2 <- scanone(fake.f2, method="hk", n.perm=500, perm.Xsp=TRUE) operm2.f2 <- scanone(fake.f2, method="hk", n.perm=500, perm.Xsp=TRUE) operm.f2 <- c(operm1.f2, operm2.f2) @ Getting the autosome- and X-chromosome-specific thresholds is a bit tricky, and so another improvement is the addition of the function \code{summary.scanoneperm} for calculating such thresholds. The argument \code{alpha} indicates the significance levels. <>= summary(operm.f2, alpha=c(0.05, 0.20)) @ Further, we may include the permutation results in the call to \code{summary.scanone} to automatically calculate thresholds and to have genome-scan-adjusted p-values displayed. <>= summary(out.f2, perms=operm.f2, alpha=0.05, pvalues=TRUE) @ Finally, one may prefer to do a stratified permutation test, permuting the phenotypes separately within each of the groups defined by sex and cross direction. This may be done in \code{scanone} with the argument \code{perm.strata}, which should be a numeric vector whose unique values define the separate strata. We set of the strata as follows. <>= sex <- fake.f2$pheno$sex pgm <- fake.f2$pheno$pgm strata <- sex + 2*pgm table(strata) @ We then perform the permutation test in four pieces, and combine the results together, as follows. <>= operm1.f2strat <- scanone(fake.f2, method="hk", n.perm=250, perm.Xsp=TRUE, perm.strata=strata) operm2.f2strat <- scanone(fake.f2, method="hk", n.perm=250, perm.Xsp=TRUE, perm.strata=strata) operm3.f2strat <- scanone(fake.f2, method="hk", n.perm=250, perm.Xsp=TRUE, perm.strata=strata) operm4.f2strat <- scanone(fake.f2, method="hk", n.perm=250, perm.Xsp=TRUE, perm.strata=strata) operm.f2strat <- c(operm1.f2strat, operm2.f2strat, operm3.f2strat, operm4.f2strat) @ The new thresholds are as follows. <>= summary(operm.f2strat, alpha=c(0.05, 0.20)) @ The big changes to the \code{summary.scanone} function concern the case of results for multiple phenotypes. To illustrate this, we will look at the \code{fake.bc} data, which has two phenotypes. First we load the data. <>= data(fake.bc) @ <>= load("fakebc_results.RData") @ Now let's run \code{calc.genoprob} and do a genome scan on the two phenotypes. Again, we use \code{method="hk"} for the sake of speed. <>= fake.bc <- calc.genoprob(fake.bc, step=2.5) out.bc <- scanone(fake.bc, pheno.col=1:2, method="hk") @ The results contain LOD scores for each of the phenotypes. By default, \code{summary.scanone} looks at the first of these, though it also shows the LOD score for the second phenotype at the locations of the LOD peaks for the first phenotype. <>= summary(out.bc, threshold=3) @ If we use \code{lodcolumn=2}, we get the analogous results, looking at the second phenotype. <>= summary(out.bc, threshold=3, lodcolumn=2) @ If we use \code{format="allpheno"}, we get separate rows for the peaks of each of the phenotypes. <>= summary(out.bc, threshold=3, format="allpheno") @ Perhaps the most convenient output is obtained with \code{format="allpeaks"}, which gives a single row for each chromosome, with the maximum LOD score and its position for each of the phenotypes. A chromosome is displayed if the LOD score for at least one of the phenotypes exceeds its threshold. The \code{threshold} argument can be a single threshold, applied to all phenotypes, or we can give a vector with separate thresholds for each of the LOD score columns. <>= summary(out.bc, threshold=c(3,2.5), format="allpeaks") @ A permutation test may be performed as before. Since the \code{fake.bc} data has only autosomal data, use of \code{perm.Xsp=TRUE} would be ignored. <>= operm.bc <- scanone(out.bc, pheno.col=1:2, method="hk", n.perm=1000) @ We can again use \code{summary} to get LOD thresholds <>= summary(operm.bc, alpha=0.05) @ And again these can be used in \code{summary.scanone} to calculate thresholds and get genome-scan-adjusted p-values. <>= summary(out.bc, perms=operm.bc, alpha=0.05, format="allpeaks", pvalues=TRUE) @ \end{document} qtl/inst/doc/Sources/MQM/0000755000175100001440000000000012566656321014703 5ustar hornikusersqtl/inst/doc/Sources/MQM/sweaveit.sh0000755000175100001440000000042712422233634017061 0ustar hornikusers#! /bin/sh echo "* Starting SWEAVE to generate MQM-tour (MQM)" rm -vf MQM-tour.tex which R R --version R CMD BATCH SweaveIt.R if [ ! -e MQM-tour.tex ]; then cat SweaveIt.Rout exit 2 fi latex MQM-tour.tex # run twice for references latex MQM-tour.tex dvipdfm MQM-tour.dvi qtl/inst/doc/Sources/MQM/MQM-tour.Rnw0000644000175100001440000014577612424414457017033 0ustar hornikusers\documentclass[11pt]{article} \setlength{\topmargin}{-.5in} \setlength{\textheight}{23.5cm} \setlength{\textwidth}{17.0cm} \setlength{\oddsidemargin}{.025in} \setlength{\evensidemargin}{.025in} \setlength{\textwidth}{6.25in} \usepackage{amsmath} \usepackage{graphicx} \usepackage{verbatim} % useful for program listings \usepackage{color} % use if color is used in text \usepackage{subfigure} % use for side-by-side figures \usepackage{float} \usepackage{Sweave} \usepackage{url} \newcommand{\mqm}{\emph{MQM}} \newcommand{\MQM}{\mqm} \newcommand{\qtl}{QTL} \newcommand{\QTL}{\qtl} \newcommand{\xqtl}{\emph{x}QTL} \newcommand{\mqtl}{\emph{m}QTL} \newcommand{\eqtl}{\emph{e}QTL} \newcommand{\lod}{LOD} \newcommand{\cM}{cM} \newcommand{\rqtl}{\emph{R/qtl}} \newcommand{\cim}{\emph{CIM}} \newcommand{\At}{\emph{Arabidopsis thaliana}} \newcommand{\FIXME}{({\bf FIXME!})} \newcommand{\CHECK}{({\bf CHECK!})} \newcommand{\NOTE}[1]{({\tt NOTE: #1 })} \newcommand{\intro}[1]{\vspace{0.15in}#1:} \newcommand{\code}{\texttt} \newcommand{\etal}{\emph{et al.}} \newcommand{\Atintro}{\At\ RIL mQTL dataset (multitrait) with 24 metabolites as phenotypes \cite{Keurentjes2006}} \newcommand{\Atintrocolors}{\Atintro\ comparing \mqm\ (\code{mqmscan} in green) and single \qtl\ mapping (\code{scanone} in black)} \title { Tutorial - Multiple-QTL Mapping (MQM) Analysis for R/qtl } \author { Danny Arends, Pjotr Prins, Karl W. Broman and Ritsert C. Jansen } \begin {document} \maketitle \clearpage \SweaveOpts{prefix.string=Figs/fig,eps=TRUE} \setkeys{Gin}{width=6.25in} %% <- change width of figures \section{Introduction} \input{mqm/description.txt} \vspace{0.3in} \input{mqm/advantages_latex.txt} \input{mqm/limitations.txt} Despite these limitations, \mqm\footnote{MQM should not be confused with composite interval mapping (CIM) \cite{CIMa,CIMb}. The advantage of MQM over CIM is reduction of type I error (a QTL is indicated at a location where there is no QTL present) and type II error (a QTL is not detected) for QTL detection \cite{jansen94b}.} is a valuable addition to the \qtl\ mapper's toolbox. It is able to deal with QTL in coupling phase and QTL in repulsion phase. \mqm\ handles missing data and has higher power to detect QTL (linked and unlinked) than other methods. R/qtl's \mqm\ is faster than other implementations and scales on multi-CPU systems and computer clusters. In this tutorial we will show you how to use \mqm\ for \qtl\ mapping. \mqm\ is an integral part of the free \rqtl\ package \cite{rqtlbook,broman09,broman03} for the R statistical language\footnote{We assume the reader knows how to load his data into R using the R/qtl \code{read.cross} function; see also the R/qtl tutorials \cite{broman09} and book \cite{rqtlbook}.}. \section{A quick overview of \mqm} These are the typical steps in an \mqm\ \qtl\ analysis: \begin{itemize} \item Load data into R \item Fill in missing data, using either \code{mqmaugmentdata} or \code{fill.geno} \item Unsupervised backward elimination to analyse \emph{cofactors}, using \code{mqmscan} \item Optionally select \emph{cofactors\/} at markers that are thought to influence \qtl\ at, or near, the location \item Permutation or simulation analysis to get estimates of significance, using \code{mqmpermutation} or \code{mqmscanfdr} \end{itemize} Using maximum likelihood (ML), or restricted maximum likelihood (REML), the algorithm employs a backward elimination strategy to identify \qtl\ underlying the trait. The algorithm passes through the following stages: \begin{itemize} \item Likelihood-based estimation of the full model using all cofactors \item Backward elimination of cofactors, followed by a genome scan for \qtl \item If there are no \emph{cofactors\/} defined, the backward elimination of cofactors is skipped and a genome scan for \qtl\ is performed, testing each genetic (interval) location individually. In this case REML and ML will result in the same \qtl\ profile because there is no full model. \end{itemize} The results created during the genome scan and the \qtl\ model are returned as an (extended) R/qtl \code{scanone} object. Several special plotting routines are available for \mqm\ results. %\clearpage \section{Data augmentation} \label{augmentation} In an ideal world all datasets would be complete (with the genotype for every individual at every marker determined), however in the real world datasets are often incomplete. That is, genotype information is missing, or can have multiple plausible values. \mqm\ automatically expands the dataset by adding all potential variants and attaching a probability to each. For example, information is missing (unknown) at a marker location for one individual. Based on the values of the neighbouring markers, and the (estimated) recombination rate, a probability is attached to all possible genotypes. With \mqm\ all possible genotypes with a probability above the parameter \code{minprob} are considered. When encountering a missing marker genotype (possible genotypes {\bf A} and {\bf B} in a RIL), all possible genotypes at the missing location are created. Thus at the missing location two `individuals' are created in the \emph{augmentation} step, one with genotype {\bf A}, and one with genotype {\bf B}. A probability is attached to both \emph{augmented} individuals. The combined probability of all missing marker locations tells whether a genotype is likely, or unlikely, which allows for weighted analysis later. To see an example of missing data with an F$_2$ intercross, we can visualize the genotypes of the individuals using \code{geno.image}. In Figure~\ref{missing data} there are 2\% missing values in white. The other colors are genotypes at a certain position, for a certain individual. Simulate an F$_2$ dataset with 2\% missing genotypes as follows: \intro{Simulate a dataset with missing data} % set seed so that everything comes out exactly the same <>= set.seed(19696527) @ <<>>= library(qtl) data(map10) simcross <- sim.cross(map10, type="f2", n.ind=100, missing.prob=0.02) @ and plot the genotype data using \code{geno.image} (Figure~\ref{missing data}): <>= geno.image(simcross) @ \begin{figure} <>= <> @ \caption{Genotype data for a simulated F$_2$ intercross generated with \code{sim.cross}, with 100 individuals and 2\% missing data. White pixels indicate missing genotypes.\label{missing data}} \end{figure} Before going to the next step (the \qtl\ genome scan), the data has to be completed (i.e. no more missing data). There are two possibilities: use (1) the \mqm\ data augmentation routine \code{mqmaugment} or (2) the imputation routine \code{fill.geno}. Augmentation tries to analyse all possible genotypes of interest by leaving them in the solution space. In contrast, the imputation method \emph{selects} the most likely genotype, and uses that single individual for further analysis. The downside of augmentation is that the addition of many possible genotypes can exceed available computer memory. Currently, augmentation moves an individual to a second augmentation round when it has too many possible genotypes (above the maximum number of augmented individuals \code{maxaugind}). In this second augmentation round the user can specify what needs to be done with these individuals: (1) Only use the most likely genotype, (2) use multiple imputation to create multiple possible genotypes (up to \code{maxaugind}) or (3) remove the original genotype/individual from the analysis. Note that you can opt to use \code{fill.geno}'s imputation method on your dataset, instead of augmentation, when too many individuals are dropped because of missing data. The function \code{mqmaugment} is specific to \mqm\ and the recommended procedure\footnote{Note that after augmentation the resulting object is no longer suitable for the use with other R/qtl mapping functions, like \code{scanone} and \code{cim}, because they can not account for duplicated or dropped individuals.}. In this tutorial we focus on \mqm's augmentation. The function \code{mqmaugment} fills in missing genotypes for us. For each missing genotype data, at a marker, it fills in all possible genotypes and calculates the probability. When the total probability is higher than the \code{minprob} parameter the \emph{augmented} individual is stored in the new cross object, ready for QTL mapping. The important parameters are: \code{cross}, \code{pheno.col}, \code{maxaugind}, \code{minprob} and \code{verbose} (see also the \code{mqmaugment} help page). \code{maxaugind} sets the maximum number of \emph{augmented} genotypes per individual in a dataset. The default of 82 allows six missing markers per individual in a BC, and four in an F$_2$. As a result the user has to increase the \code{maxaugind} parameter when there are more missing markers. The \code{minprob} parameter sets the minimum probability of a genotype for inclusion in the augmented dataset. This genotype probability is calculated for every marker \emph{relative} to the most likely genotype of this individual. Note that setting this value too low may result in moving a lot of individuals to the second augmentation round as the maximum of augmented individuals (the parameter \code{maxaugind}) is quickly reached. Increasing \code{minprob} (towards a value of 1.0) can keep individuals with more missing data inside the first augmentation round; a possible rule of thumb may be to set \code{minprob} to the percentage of data missing. A value of \code{minprob=1.0} makes augmentation behave similar to \code{fill.geno}'s imputation method, though with different resulting genotypes. Use \code{verbose=TRUE} to get more feedback on the augmentation routine and to check how many individuals are moved to the second stage, for imputation or removal\footnote{Augmentation is not always suitable with a lot of missing data, like in the case of selective genotyped datasets (for example the mouse \code{hyper} dataset that comes with R/qtl); these will always be handled with \code{minprob=1.0} (and a warning will be issued).} To start with an example, first run \code{mqmaugment} with \code{minprob=1.0} (Figure~\ref{augment1}): \intro{Plot augmented data using \code{geno.image}} <<>>= # displays warning because MQM ignores the X chromosome in an F2 augmentedcross <- mqmaugment(simcross, minprob=1.0) @ Plot the genotype data as follows: <>= geno.image(augmentedcross) @ \begin{figure} <>= <> @ \caption{Genotypes, as visualized with \code{geno.image}, of 100 filled individuals (\code{mqmaugment} with \code{minprob=1.0}. With missing data only a `most likely' individual is used and no real expansion of the dataset takes place, with similar results as \code{fill.geno}'s imputation method).\label{augment1}} \end{figure} With a lower \code{minprob}, more augmented individuals are kept, and the resulting \emph{augmented} dataset will be larger. Adding (weighted) augmented individuals with all possible genotypes theoretically leads to a more accurate mapping when dealing with missing values \cite{jansen93}\footnote{Note again that the augmented dataset can only be used with pure \mqm\ functions. \mqm\ functions recognise expanded individuals as single entities. Other R/qtl functions, like \code{scanone}, assume the augmented individuals are \emph{real} individuals.}. \intro{Try augmentation with \code{minprob=0.1} (Figure~\ref{augment2})} <<>>= augmentedcross <- mqmaugment(simcross, minprob=0.1) @ \intro{Plot the genotype data} <>= geno.image(augmentedcross) @ \begin{figure} <>= <> @ \caption{Genotypes, as visualized with \code{geno.image} of the \emph{augmented} genotypes of 100 individuals. There are a total of \Sexpr{nind(augmentedcross)} `expanded' individuals in this plot, because \mqm\ fills in missing markers with all likely genotypes (an average expansion of \Sexpr{round(nind(augmentedcross)/nind(simcross),1)} per individual).\label{augment2}} \end{figure} \label{multitrait} An \mqtl\ dataset (\code{multitrait}), which contains 24 metabolite traits from a RIL population of \At, is now distributed with R/qtl (load the data with \code{data(multitrait)}). This is part of the \At\ RIL selfing experiment with Landsberg erecta (Ler) and Cape Verde Islands (Cvi) with 162 individuals scored 117 markers \cite{Koornneef1998}. The experiment concerned empirical untargeted metabolomics using liquid chromatography time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between \At\ accessions \cite{Keurentjes2006}. \intro{Simulate missing data by removing some genotype data (5\%, 10\% and 80\%) from the cross object} <>= data(multitrait) msim5 <- simulatemissingdata(multitrait, 5) msim10 <- simulatemissingdata(multitrait, 10) msim80 <- simulatemissingdata(multitrait, 80) @ Next use augmentation to fill in the missing genotypes; with more missing data increase the \code{minprob} parameter. When the \code{minprob} parameter is set too low it is possible that an individual cannot be augmented, and is moved to the second round of augmentation (see the description above). <>= maug5 <- mqmaugment(msim5) maug10 <- mqmaugment(msim10, minprob=0.25) maug80 <- mqmaugment(msim80, minprob=0.80) @ Taking the 10\% missing set, we can try a lower \code{minprob=0.001}. The output below shows that ten augmented individuals miss too many markers to be augmented. By using the imputation strategy these individuals are kept in the set with a single `most likely' genotype. \intro{Augment with an imputation strategy} <>= maug10minprob <- mqmaugment(msim10, minprob=0.001, verbose=TRUE) maug10minprobImpute <- mqmaugment(msim10, minprob=0.001, strategy="impute", verbose=TRUE) # check how many individuals are expanded: nind(maug10minprob) nind(maug10minprobImpute) @ Next, scan for QTL inside the cross objects with \code{mqmscan} and the single-QTL mapping function \code{scanone} (for reference). The effect of increasing the amount of missing data on QTL mapping, using default values, can be seen in Figure~\ref{augment6}. <>= mqm5 <- mqmscan(maug5) mqm10 <- mqmscan(maug10) mqm80 <- mqmscan(maug80) @ <>= msim5 <- calc.genoprob(msim5) one5 <- scanone(msim5) msim10 <- calc.genoprob(msim10) one10 <- scanone(msim10) msim80 <- calc.genoprob(msim80) one80 <- scanone(msim80) @ <>= op <- par(mfrow = c(2,2)) plot(mqm5, mqm10, mqm80, col=c("green","blue","red"), main="MQM missing data") legend("topleft", c("MQM 5%","MQM 10%","MQM 80%"), col=c("green","blue","red"), lwd=1) plot(one5, mqm5, main="5% missing", col=c("black","green")) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) plot(one10, mqm10, main="10% missing", col=c("black","blue")) legend("topleft", c("scanone","MQM"), col=c("black","blue"), lwd=1) plot(one80, mqm80, main="80% missing", col=c("black","red")) legend("topleft", c("scanone","MQM"), col=c("black","red"), lwd=1) @ \begin{figure} <>= <> @ \caption{\Atintro. Effect of missing data on \code{mqmscan} after augmentation (green=5\%, blue=10\%, red=80\%) and \code{scanone} (black), after \code{fill.geno} imputation. \label{augment6}} \end{figure} \clearpage \section{Multiple-QTL Mapping (MQM)} \label{QTL modelling} The \code{multitrait} dataset, distributed with R/qtl, contains 24 metabolite traits from a RIL population of \At \cite{Keurentjes2006} (see also section \ref{multitrait} and \code{help(multitrait)} in R). Here we analyse the \code{multitrait} dataset using both \code{scanone} (single-\qtl\ analysis) and \code{mqmscan} (Multiple-QTL Mapping). First augment the data using the \code{mqmaugment} function with \code{minprob=1.0}, to compare against \code{scanone} with imputation (see also section \ref{augmentation}). \intro{Scan for \qtl\ with \code{mqmscan}, after filling missing data with \code{mqmaugment minprob=1.0}} <<>>= data(multitrait) maug_min1 <- mqmaugment(multitrait, minprob=1.0) mqm_min1 <- mqmscan(maug_min1) @ We compare \code{mqmscan} with \code{scanone}. For \code{scanone} one first calculates conditional \qtl\ genotype probabilities via \code{calc.genoprob}. <<>>= mgenop <- calc.genoprob(multitrait, step=5) m_one <- scanone(mgenop) @ Figure~\ref{Atminprob} shows that, without augmentation, the results from \mqm\ are similar to \code{scanone}. \intro{\code{mqmscan} after augmentation, without cofactor selection} <<>>= maug <- mqmaugment(multitrait) mqm <- mqmscan(maug) @ \begin{figure} <>= plot(m_one, mqm_min1, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) @ \caption{\Atintrocolors. \mqm\ shows similar results as single \qtl\ mapping, when used without \emph{augmentation} (\code{minprob} is 1.0), and with default parameters.\label{Atminprob}} \end{figure} By default \MQM\ introduces fictional markers, or `pseudo markers', at fixed intervals. A pseudo marker has a name like \code{c7.loc25}, which is the pseudo marker at 25 \cM\ on chromosome 7. (Note that this reflects the standard naming used in R/qtl.) Each chromosome is divided into evenly spaced pseudo markers, \code{step.size} \cM\ apart. A \lod\ score for underlying \qtl\ is calculated at these pseudo markers. A small \code{step.size} allows for smoother profiles compared with a pure marker-based mapping approach. The real markers are listed between the pseudo markers. In the result you can remove the pseudo markers by using the function \code{mqmextractmarkers}, as follows: <<>>= real_markers <- mqmextractmarkers(mqm) @ For model selection in \mqm, first supply the algorithm with an initial model. This initial model can be produced in two ways: by (1) building a model by hand (forward stepwise), or (2) by unsupervised backward elimination on a large number of markers (discussed in Section~\ref{backelim}). % @@ First build this initial model by hand using a forward stepwise approach. (Note that the automated procedure is preferred, both for theoretical and practical reasons.) A model consists of a set of markers we want to account for. We can start building the initial model by adding cofactors at markers with high LOD scores scored by using \code{mqmscan} with default values. Figure~\ref{Atminprob} displayed a large QTL peak on chromosome 5 at 35 \cM. So we account for that by setting a cofactor at the marker nearest to the peak on chromosome 5 and running \code{mqmscan} again. (See Figures~\ref{Cofactor4} and \ref{Cofactor4b}.) \intro{Add marker GH.117C (chromosome 5, at 35 \cM) as a cofactor} <<>>= max(mqm) find.marker(maug, chr=5, pos=35) multitoset <- find.markerindex(maug, "GH.117C") setcofactors <- mqmsetcofactors(maug, cofactors=multitoset) mqm_co1 <- mqmscan(maug, setcofactors) @ The function \code{find.marker} identifies the name of the marker closest to 35 \cM. The function \code{find.markerindex} translates the marker name into a cofactor number. The function \code{mqmsetcofactors} sets up a cofactor list for use with \code{mqmscan}. \intro{Plot the results of the genome scan after adding a single cofactor (Figure~\ref{Cofactor4})} <>= par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_co1)) plot(mqm_co1) @ \begin{figure} <>= # plot after adding first cofactor <> @ \caption{\Atintro. \code{mqmscan} after a cofactor is added at the top scoring marker of chromosome 5. During the analysis it is kept in the model. \label{Cofactor4}} \end{figure} % -------------- \intro{Plot the \code{mqmscan} results with \code{scanone} results as follows (Figure~\ref{Cofactor4b})} <>= plot(m_one, mqm_co1, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) @ \begin{figure} <>= <> @ \caption{\Atintro\ after introducing a cofactor on chromosome 5 (GH.117C). \code{mqmscan} (green, dashed) differs from \code{scanone} (black). \label{Cofactor4b}} \end{figure} % --------------------------- Figures~\ref{Cofactor4} and \ref{Cofactor4b} show the effect of setting a single marker as a cofactor related to the \qtl\ on chromosome 5, followed by an \mqm\ scan. The marker is not dropped and it passes initial thresholding to account for the \code{cofactor.significance} level. LOD scores are expected to change slightly, because of variation already explained by the \qtl\ on chromosome 5 (Figure~\ref{Cofactor4b}). Figure~\ref{Cofactor4b} shows the second peak on chromosome 4 at 10 \cM\ increases. Add a cofactor to the model and check if the model with both cofactors changes the \qtl. Combining \code{find.markerindex} with \code{find.marker}, adds the new cofactor to the cofactor already in \code{multitoset} (see Figure~\ref{twowaycomparison}): <<>>= # summary(mqm_co1) multitoset <- c(multitoset, find.markerindex(maug, find.marker(maug,4,10))) setcofactors <- mqmsetcofactors(maug,cofactors=multitoset) mqm_co2 <- mqmscan(maug, setcofactors) @ % ----------- \intro{Plot after adding second cofactor on chromosome 4 at 10 \cM} <>= par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_co2)) plot(mqm_co1, mqm_co2, col=c("blue","green"), lty=1:2) legend("topleft", c("one cofactor","two cofactors"), col=c("blue","green"), lwd=1) @) \begin{figure} <>= <> @ \caption{\Atintro\ using an added cofactor on chromosome 5 (blue), versus two cofactors, using an additional cofactor on chromosome 4 (green). \label{twowaycomparison}} \end{figure} % --------------------------- \intro{Plot the results with 0, 1 and 2 cofactors as follows} <>= plot(mqm, mqm_co1, mqm_co2, col=c("green","red","blue"), lty=1:3) legend("topleft", c("no cofactors","one cofactor","two cofactors"), col=c("green","red","blue"), lwd=1) @ \begin{figure} <>= # plot closeup of threeway comparison <> @ \caption{\Atintro. Comparison of \mqm\ adding 0 (green), 1 (red) and 2 (blue) cofactor(s) (note that adding more cofactors does not improve the two QTL model).\label{threewaycomparison}} \end{figure} When using the functions \code{mqmsetcofactors}, or the automated \code{mqmautocofactor} (described in the next section), the number of cofactors is compared against the number of individuals inside the cross object. If there is a danger of setting too many cofactors, an error message is shown. \mqm\ also verifies the \code{cofactor.significance} level specified by the user. In the example the marker on chromosome 1 was informative enough, and included into the model. This way a new initial model consisting of cofactors on chromosome 4 and 5 was created. This (forward) selection of cofactors can continue until there are no more informative markers. Manually determining the markers to set a cofactor can be very time consuming in the case of many \qtl\ underlying a trait. It is also prone to overfitting. Furthermore, manual fitting is generally not feasible for a large number of traits. Fortunately \mqm\ provides unsupervised backward elimination, which is described in the next section. \clearpage \section{Unsupervised cofactor selection through backward elimination\label{backelim}} \mqm\ provides unsupervised backward elimination on a large number of markers by selecting cofactors automatically. Normally the number of markers in a dataset is much larger than the number of individuals. \mqm\ allows using any number of cofactors simultaneously. This can be as low as 0 cofactors up to a maximum of the number of individuals minus 12 ($Inds - 12$), as described in the Handbook of Statistical Genetics\cite{jansen07}. The functions: ``\code{mqmsetcofactor}'' and ``\code{mqmautocofactors}'' both create lists of cofactors that can be used for backward elimination. \code{mqmautocofactor} accounts for the underlying marker density and is therefore suitable for datasets with few individuals. See Figure~\ref{ManualAuto} for a comparison on the \code{multitrait} dataset, using the \code{mqmsetcofactors} function to set cofactors every 5th marker and \code{mqmautocofactor} to set 50 cofactors across the genome. After cofactor selection \mqm\ analyses and drops the least informative cofactor from the model. This step is repeated until a limited number of informative cofactors remain. When taking marker density into account, an extra cofactor is introduced on chromosome 1 (see Figure~\ref{ManualAuto}). After unsupervised backward elimination \code{mqmscan} scans each chromosome using the model with the remaining set of cofactors. For example, starting with 50 cofactors using \code{mqmautocofactor} and \code{mqmsetcofactors}, map \qtl\ for the various traits in \code{multitrait}, which contains 24 metabolite traits from a RIL population of \At\, as described in section \ref{multitrait}. The \qtl\ LOD scores differ between \mqm\ and single \qtl\ mapping with \code{scanone} (see Figures~\ref{Backward1} and \ref{Backward2}). \intro{Unsupervised cofactor selection through backward elimination} <>= autocofactors <- mqmautocofactors(maug, 50) mqm_auto <- mqmscan(maug, autocofactors) setcofactors <- mqmsetcofactors(maug, 5) mqm_backw <- mqmscan(maug, setcofactors) @ <>= autocofactors <- mqmautocofactors(maug, 50) mqm_auto <- mqmscan(maug, autocofactors) setcofactors <- mqmsetcofactors(maug, 5) mqm_backw <- mqmscan(maug, setcofactors) @ \intro{Visual inspection of the initial models} <>= par(mfrow = c(2,1)) mqmplot.cofactors(maug, autocofactors, justdots=TRUE) mqmplot.cofactors(maug, setcofactors, justdots=TRUE) @ \begin{figure} <>= # plot result of cofactor selection <> @ \caption{\Atintro. \code{mqmsetcofactor} after introducing cofactors at every fifth marker (top) and \code{mqmautocofactor} automatic marker selection (bottom). Automatic selection takes the underlying marker density into consideration.\label{ManualAutoStart}} \end{figure} \intro{Plot results} <>= par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_backw)) plot(mqmgetmodel(mqm_auto)) @ \begin{figure} <>= # plot result of cofactor backward elimination <> @ \caption{\Atintro. \code{mqmsetcofactor} after introducing cofactors at every fifth marker (top) and \code{mqmautocofactor} automatic marker selection (bottom). \code{mqmautocofactor} places an additional cofactor at chromosome 1 (see also Figure~\ref{ManualAutoStart}). After backward elimination this extra marker remains informative.\label{ManualAuto}} \end{figure} <>= par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_backw)) plot(mqm_backw) @ \begin{figure} <>= # plot result of cofactor backward elimination <> @ \caption{\Atintro. Unsupervised cofactor selection through backward elimination using \code{mqmsetcofactor} after introducing cofactors at every fifth marker. \qtl\ mapped for trait X3.Hydroxypropyl on chromosome 4 and 5.\label{Backward1}} \end{figure} The \code{mqmgetmodel} function returns the final model from the output of \code{mqmscan}. This model can be further investigated using the \code{fitqtl} and \code{fitqtl} routines from R/qtl. \code{mqmgetmodel} can only be used after backward elimination produces a significant model. The resulting model can also be used to obtain the location and name of the significant cofactors. \intro{Plot result of \mqm, using unsupervised backward elimination, against that of \code{scanone}} <>= plot(m_one, mqm_backw, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) @ % ------------------------------ <>= plot(m_one, mqm_backw, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) @ \begin{figure} <>= <> @ \caption{\Atintro. Compare \qtl\ mapping of \mqm\, after introducing cofactors at every fifth marker and unsupervised backward elimination of cofactors (green, dashed), and \code{scanone} (black).\label{Backward2}} \end{figure} \mqm\ \qtl\ mapping may result in many significant (informative) cofactors. Figure~\ref{Backward2} shows at \code{cofactor.significance=0.02} chromosomes 4 and 5 are involved. Lowering the significance level from 0.02 to 0.002 may yield a smaller model. In biology extensive models are sometimes preferred, but in general a simpler model is easier to understand and, perhaps, validated. Depending on the trait, and the sample size, increasing cofactor.significance can reduce the number of significant QTL in the model. In this example we have already have a small model, so we don't really expect to lose the two QTL on chromosome 4 and chromosome 5. When decreasing the \code{cofactor.significance} no additional cofactors are dropped from the model (See Figure~\ref{lowalpha}) \clearpage \intro{Plot with lowered \code{cofactor.significance}} <>= mqm_backw_low <- mqmscan(maug, setcofactors, cofactor.significance=0.002) par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_backw_low)) plot(mqm_backw,mqm_backw_low, col=c("blue","green"), lty=1:2) legend("topleft", c("Significance=0.02","Significance=0.002"), col=c("blue","green"), lwd=1) @ \intro{\qtl\ mapped with different \code{cofactor.significance=0.002}, using the same starting markers as Figure~\ref{Backward1}. As can be seen from the plot the models selected are similar. This means the QTL found significant at 0.02 are still significant at a more restrictive cutoff.\label{lowalpha}} \begin{figure} <>= <> @ \caption{\Atintro. \qtl\ mapped with different \code{cofactor.significance}. With the lower \code{cofactor.significance=0.002} the model does not change.\label{FigLowAlpha}} \end{figure} When comparing the \mqm\ scan in Figure~\ref{FigLowAlpha} with the original \code{scanone} result in Figure~\ref{Backward2} there are some notable differences. Some \qtl\ show higher significance (LOD scores) and some others show lower significance and are, therefore, estimated to be less likely involved in this trait. Figures can be reconstructed from the result of \code{mqmscan} using the \code{mqmplot.singletrait} function (see, for example, Figure~\ref{AutoCofactor}). Here the model and \qtl\ profile are retrieved. These functions can only be used with \code{mqmscan} functions, as they require the additional information about the inferred \qtl\ model. The results also contain the \emph{estimated} information content per marker. % <>= % mqmplot.singletrait(result, extended=TRUE) % @ <>= mqmplot.singletrait(mqm_backw_low, extended=TRUE) @ \begin{figure} <>= <> @ \caption{\Atintro. Plot using \code{mqmplot.singletrait} of the first metabolic trait. The information content per marker (red) and the mapped \qtl\ (black).\label{AutoCofactor}} \end{figure} The information content \code{info} in the result is calculated from the deviation of the `ideal marker distribution'. For example, with a dataset of 100 individuals, when comparing two distinct phenotypes at a marker location, we have most power when both groups are equally divided 50/50. A marker has virtually no power when one group containing 1 individual versus a group of 99. We can multiply the estimated \qtl\ effect by this information content to `clean' the QTL profile by giving less weight to less informative markers. Please note that the sample size already plays a role in calculating \qtl. Meanwhile it allows (informal) further weighting/exploring \emph{information} content (Figure~\ref{AutoCofactor}). \section{MQM effect plots} \label{effectplots} The function \code{mqmplot.directedqtl} is used to plot \lod\ curves with an indication of the sign of the estimated QTL effects. This function because it uses internal R/qtl functions cannot handle augmented cross objects. An error will occur when the object supplied is augmented using \code{mqmaugment}. This requires using \code{mqmscan} with parameter \code{outputmarkers=TRUE} (default). \intro{Create a directed QTL plot (Figure~\ref{QTLeffects})} <>= dirresults <- mqmplot.directedqtl(multitrait, mqm_backw_low) @ \begin{figure} <>= <> @ \caption{\Atintro. Like Figure~\ref{AutoCofactor}, but with LOD scores multiplied by $\pm$1 according to the sign of the estimated \qtl\ effect.\label{QTLeffects}} \end{figure} The results in Figure~\ref{FigLowAlpha} imply that \qtl\ on chromosomes 4 and 5 are associated with the metabolite X3.Hydroxypropyl. If we want to investigate the effects of the QTL, we can use the functions \code{plotPXG} and \code{effectplot}. The following plots show these for markers GH.117C (main effect, Figure~\ref{MainEffectsD1}) and the interaction between GH.117C and GA1 (Figure~\ref{epistatic1}). <>= plotPXG(multitrait, marker="GH.117C") @ \begin{figure} <>= <> @ \caption{\Atintro. Main effect plot, with \code{plotPXG}, of marker GH.117C on trait X3.Hydroxypropyl. For each marker genotype the individual phenotype is plotted, with the mean of genotype AA (red) and BB (blue).\label{MainEffectsD1}} \end{figure} %---------------------------------------------- The initial scans for X3.Hydroxypropyl (Figure~\ref{Backward1}) show two possible \qtl\ on chromosome 4 and 5. We can investigate interactions between these main effect QTL using the \code{effectplot} function. To investigate the possible epistatic interaction, select markers GA1 (significant in Figure~\ref{Backward1} and Figure~\ref{Backward2}) and GH.117C (significant in Figure~\ref{Backward2} and \ref{AutoCofactor}). See Figure~\ref{epistatic1}. <>= effectplot(multitrait, mname1="GH.117C", mname2="GA1") @ \begin{figure} <>= <> @ \caption{\Atintro. Explore epistatic interaction, using \code{effectplot}, between markers GH.117C and GA1. GA1 appears to obscure the effect of GH.117C. An individual that has BB at GA1 has no difference in expression between being AA or BB at GH.117C. However when an individual is AA at GA1 there is clear difference between the two genotype means (~1.500 BB versus 12000 when AA) at GH.117C. \label{epistatic1}} \end{figure} %------------------------------------------------ Likewise, in case we are interested in the interactions between the first small hump on chromosomes 1 (marker: PVV4 not significant) and the main efect on 5 (GH.117C), we could make interaction plots between these two markers with a high LOD score on those chromosomes. See Figure~\ref{epistatic2}. <>= effectplot(multitrait, mname1="PVV4", mname2="GH.117C") @ Meanwhile, Figure~\ref{epistatic2} shows no evidence for an interaction between the two markers GH.117C and PVV4, as the lines are close to parallel. \begin{figure} <>= <> @ \caption{\Atintro. \code{effectplot} shows no epistatic effects between markers GH.117C and PVV4, ths can be seen because the two lines run in parallel, the genotype on one location (PVV4) does not affect the effect of the expression on GH.117C other location. \label{epistatic2}} \end{figure} %------------------------------------------------ \clearpage \section{QTL significance} \label{significance} To estimate the significance of \qtl\, and perhaps further exclude markers from a model, permutation testing is provided by the function \code{mqmpermutation}. This step is computationally expensive\footnote{In the tutorial, for all examples, 25 permutations are used. A real experiment should use over 1000 permutation tests.} as the same test is repeated many times on shuffled data. Each test calculates \lod\ scores for non associated (randomly ordered) data. \mqm\ provides parametric and non-parametric bootstrapping to estimate \qtl\ significance. Select the type with the \code{bootmethod} parameter. If you have are lucky enough to have multiple CPUs on your computer you can use the SNOW package \cite{tierney03,tierney04}, which allows parallel computations on multiple CPU/cores. SNOW is available through the Internet R archive CRAN. For example, with Rgui SNOW can be installed by selecting from the menu: `Packages' and `Install Package(s)` from the drop down menu. Select a CRAN mirror near you and select the SNOW package. Rgui will start downloading the package, and install any dependencies needed. Linux users can download a copy of SNOW from \url{http://cran.r-project.org/web/packages/snow/index.html}. Once the package has finished downloading the tar.gz file can be installed using \code{R CMD INSTALL snow.tar.gz} To summarize results from \code{mqmpermutation}, \code{mqmprocespermutation} makes the output comparable to \code{scanone} when using the \code{n.perm} parameter for permutation. \intro{Calculate significance - using SNOW parallelization parameters} <<>>= require(snow) results <- mqmpermutation(maug, scanfunction=mqmscan, cofactors=setcofactors, n.cluster=2, n.perm=25, batchsize=25) @ \begin{figure} <>= mqmplot.permutations(results) @ \caption{\Atintro. \qtl\ significance calculated through permutation using \code{mqmpermutation}. Estimate by permuting a single trait (X3.Hydroxypropyl) with randomly distributing trait values amongst individuals, which gives an indication of \lod\ scores found by chance. \qtl\ with a \lod\ higher than 2.5 can be considered significant (at \code{cofactor.significance=0.05} (green) or \code{cofactor.significance=0.10} (blue)). Chromosome are marked by the gray vertical grid lines.} \end{figure} <<>>= resultsrqtl <- mqmprocesspermutation(results) summary(resultsrqtl) @ For small datasets, with a limited amount of classical traits, \code{mqmpermutation} is nice. However, for large expression studies (eQTL) using microarrays, use \code{mqmscanfdr} instead, which estimates false discovery rates (FDR) across the entire dataset at \lod\ cutoff, as described by Breitling \etal\cite{breitling08}. To estimate the FDR, \code{mqmscanfdr} permutes whole genome information, taking correlation between traits into account and giving an unbiased estimate of FDR at different (user specified) thresholds. The function scans the traits and counts observed \qtl\ with a LOD above \code{x}, setting a certain threshold. It permutes all the data leaving the correlation structure between traits intact. Below, very high FDR estimates are calculated because of a small amount of permutations and high correlation between traits. We discover many QTL that map to the same location. This can normally only happen with information sparse marker(s), or correlated traits, as seen in microarray experiments. \intro{Calculate FDR} <<>>= data(multitrait) m_imp <- fill.geno(multitrait) mqmscanfdr(m_imp, mqmscanall, cofactors=setcofactors, n.cluster=2) @ In contrast, the function \code{mqmpermutation} does single trait permutations, and does not take correlation between the traits into account. The advantage is that a permutation threshold is determined for each trait. This leads to different significance levels per trait and could lead to certain \qtl\ being significant at their trait cut-off, which are not significant when a single cut-off. The \mqm\ output needs to be converted to the standard R/qtl format using the \code{mqmprocesspermutation} function. The resulting object is of class \code{scanoneperm} and can be used by the standard R/qtl functions for further analysis. To parallelize calculations \code{n.cluster} sets the number of CPU cores to use. A batch consists of a number of traits to analyze on one core. A large(r) \code{batchsize} (default 10) can also be set to improve efficiency. Every time a batch is sent a new instance of R is started, so it pays to have as few batches as possible. \section{Parallelized \xqtl\ analysis} \mqm\ can handle high throughput \xqtl\ data - the name coined for the family of expression QTL, or eQTL \cite{jansen01}, metabolite QTL (mQTL) and pQTL (protein QTL), where measurements like gene expression on microrray probes are treated as phenotypes. \mqm\ analyses traits simultaneously using parallel computing on multiple CPU/cores, and even computer clusters. \xqtl datasets (expression eQTL, metabolite mQTL) usually contain a large amount of phenotypes with known locations on the genome. These locations can be used for detecting cis/trans regulation, for example. For QTL mapping every phenotype requires one or more calls to \code{mqmscan}. In addition special plots are presented for \xqtl studies. Our example, the mQTL dataset \code{multitrait}, an \At\ RIL cross, containing 24 metabolites measured as phenotypes. Of these 24 phenotypes we will only scan the first five phenotypes by setting the \code{pheno.col} parameter. To map back the regulatory locations of these metabolites one can use plain scanning of all metabolites (initially without cofactors). Next, we plot all the profiles in a heatmap (see Figure~\ref{At.heatmap1}). In this heatmap the colors represent the LOD score, on the x-axis the marker number and on the y-axis the metabolite. The traits are numbered in the plot. Plot heatmap without cofactors and then the heatmap with cofactors and backward elimination. Figure~\ref{At.heatmap2} shows improvement over Figure~\ref{At.heatmap1} because of an improved signal to noise ratio. <<>>= data(multitrait) m_imp <- fill.geno(multitrait) mqm_imp5 <- mqmscan(m_imp, pheno.col=1:5, n.cluster=2) @ \begin{figure} <>= mqmplot.multitrait(mqm_imp5, type="image") @ \caption{\Atintro. Heatmap of five metabolite expression traits, with profiles created using \mqm\ without preselected cofactors. The colors represent the LOD score, on the x-axis the marker number and on the y-axis the metabolite.\label{At.heatmap1}} \end{figure} <<>>= cofactorlist <- mqmsetcofactors(m_imp, 3) mqm_imp5 <- mqmscan(m_imp, pheno.col=1:5 , cofactors=cofactorlist, n.cluster=2) @ \begin{figure} <>= mqmplot.multitrait(mqm_imp5, type="image") @ \caption{\Atintro. Heatmap of metabolite expression traits, with profiles created using \mqm\ with cofactors at each third marker. The colors represent the LOD score, on the x-axis the marker number and on the y-axis the metabolite.\label{At.heatmap2}} \end{figure} Use \code{mqmplot.multitrait} for more graphical output. (Unfortunately this does not show in the generated PDF, but in R it shows the trait profiles) <<>>= mqmplot.multitrait(mqm_imp5, type="lines") @ Next is \code{mqmplot.circle}. The circle plot shows a circular representation of the genome. After using automatic backward selection certain marker/cofactors are found to be significant. These are highlighted and colored. The cofactor size can be scaled, based on significance (see Figures~\ref{circle1} and \ref{circle2}). The plot can be tweaked. For example, \code{highlight} a specific trait, and calculate interactions between the significant cofactors. All other traits are grayed out, but remain partly visible, in this way it is possible to see if significant QTL for this trait are also colocated with other traits. Parameter \code{interactstrength}: highlights interactions between significant markers. However they are only drawn (and reported in the output) if the effect change is larger than \code{interactstrength} multiplied by the summed standard deviation. Parameter \code{spacing} sets space between the chromosomes in Cm. \begin{figure} <>= mqmplot.circle(m_imp, mqm_imp5) @ \caption{\Atintro. Circle plot 1 - Multiple metabolic traits without known locations. Four traits are in the centre connected by a colored spline to their QTL. Significant QTL locations are depicted as solid square circles. A lower \lod\ score is closer to the center. A 'hotspot' of \qtl\ is visible on chromosome 5. \label{circle1}} \end{figure} \begin{figure} <>= mqmplot.circle(m_imp, mqm_imp5, highlight=2) @ \caption{\Atintro. Circle plot 2 - Multiple metabolic traits without known locations. Highlight the second trait. The significant \qtl\ locations are depicted as solid red square circles. The splines show epistatic interactions (see also Figure~\ref{epistatic1}). The blue lines are locations which are modulating expression (higher or lower), the green lines show a flip in effect. To explain this: with two markers, having AA at marker one shows trait mean AA > trait mean BB at marker two however when the individual has BB at marker one the effect at marker two is reversed AA < BB. \label{circle2}} \end{figure} Next a cis-trans plot with \code{mqmplot.cistrans}. This plot is only available when genomic locations of the traits are known, e.g. the genomic probe locations in microarray eQTL studies. By default the R/qtl cross object does not store this data. So the user has to add this information to the cross object using the \code{addloctocross} function. After this operation the cis-trans plot can be created for \qtl\ with associated genome locations. The two axis of the cis-trans plot both show the genetic location. The X-axis is, normally, the \qtl\ location and the Y-axis the locations of the trait. \begin{figure} <>= data(locations) multiloc <- addloctocross(m_imp, locations) mqmplot.cistrans(mqm_imp5, multiloc, 5, FALSE, TRUE) @ \caption{\Atintro. \code{mqmplot.cistrans} can be drawn when \qtl\ have associated genome locations. \qtl\ are plotted against the position on the genome they were measured (here mQTL for \At), cutoff is at \lod=5. Normally these plots are created using 10.000 + traits. However because this tutorial is automatically generated we only use 5 traits to illustrate.} \end{figure} \intro{When having locations we can, again, use the \code{mqmplot.circle} function, now with the extra information} \begin{figure} <>= mqmplot.circle(multiloc, mqm_imp5, highlight=2) @ \caption{\Atintro. Circle plot 3 - Multiple metabolic traits with known locations. Highlight the second trait. The significant \qtl\ locations are depicted as solid red square circles. The known location of the trait is a red triangle. The splines show epistatic interactions (see also Figure~\ref{epistatic1}). The blue lines are locations which are modulating expression (higher or lower), the green lines show a flip in effect.\label{circle3}} \end{figure} \clearpage \section{Overview of all \mqm\ functions} \begin{table}[ht] \caption{Added functionality} \centering \begin{tabular}{| l | l | } \hline mqmaugment:& data augmentation \\ mqmscan:& \mqm\ modelling and scanning \\ mqmsetcofactors:& Set cofactors at markers (or at fixed locations) \\ find.markerindex:& Change marker numbering into mqmformat \\ mqmscanall:& mqmscanall scans all traits using \mqm\\ mqmpermutation:& Single trait permutation \\ mqmscanfdr:& Genome wide False Discovery Rates (FDR) \\ mqmprocesspermutation:& Creates an R/qtl permutationobject \\ & \hspace{0.25in} from the output of the \code{mqmpermutation} function \\ mqmplot.multitrait:& plot multiple traits (MQMmulti object) \\ mqmplot.directedqtl:& plot of single trait with added \qtl\ effect\\ mqmplot.permutations:& plot to show single trait permutations \\ mqmplot.singletrait:& plot of single trait analysis with information content \\ mqmplot.circle:& Genome plot of \qtl\ in a circle (optional: Use of location information)\\ mqmplot.cistrans:& Genomewide plot of cis- and trans-\qtl, above a threshold \\ addloctocross:& Adding genetic locations for traits \\ mqmtestnormal:& Test normality of a trait \\ \hline \end{tabular} \label{tbl:tabel1} \end{table} \clearpage \begin{thebibliography}{9} \bibitem{broman09} Broman, K.W.; 2009 \emph{A brief tour of R/qtl.} http://www.rqtl.org/tutorials/rqtltour.pdf. \bibitem{rqtlbook} Broman, K. W.; Sen, \'S; 2009 \emph{A Guide to QTL Mapping with R/qtl.} Springer. \bibitem{broman03} Broman, K.W.; Wu, H.; Sen, S.; Churchill, G.A.; 2003 \emph{R/qtl: QTL mapping in experimental crosses.} Bioinformatics, 19:889--890. \bibitem{jansen07} Jansen R. C.; 2007 \emph{Chapter 18 - Quantitative trait loci in inbred lines.} Handbook of Statistical Genetics, 3rd edition. Wiley. \bibitem{tierney04} Tierney, L.; Rossini, A.; Li, N.; and Sevcikova, H.; 2004 \emph{The snow Package: Simple Network of Workstations.} Version 0.2-1. \bibitem{tierney03} Rossini, A.; Tierney, L.; and Li, N.; 2003 \emph{Simple parallel statistical computing.} R. University of Washington Biostatistics working paper series, 193. \bibitem{jansen01} Jansen R. C.; Nap J.P.; 2001 \emph{Genetical genomics: the added value from segregation.} Trends in Genetics, 17, 388--391. \bibitem{jansen94} Jansen R. C.; Stam P.; 1994 \emph{High resolution of quantitative traits into multiple loci via interval mapping.} Genetics, 136, 1447--1455. \bibitem{jansen94b} Jansen R.C.; 1994 \emph{Controlling the Type I and Type II Errors in Mapping Quantitative Trait Loci.} Genetics, Vol 138, 871--881. \bibitem{Churchill94} Churchill, G. A.; and Doerge, R. W.; 1994 \emph{Empirical threshold values for quantitative trait mapping.} Genetics 138, 963--971. \bibitem{jansen93} Jansen R. C.; 1993 \emph{Interval mapping of multiple quantitative trait loci.} Genetics, 135, 205--211. \bibitem{Dempster77} Dempster, A. P.; Laird, N. M. and Rubin, D. B.; 1977 \emph{Maximum likelihood from incomplete data via the EM algorithm.} J. Roy. Statist. Soc. B, 39, 1--38. \bibitem{CIMa} Zeng, Z. B.; 1993 \emph{Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci.} Proc. Natl. Acad. Sci. USA, 90, 10972--10976. \bibitem{CIMb} Zeng, Z. B.; 1994 \emph{Precision mapping of quantitative trait loci.} Genetics, 136, 1457--1468 \bibitem{sugiyama} Sugiyama, F.; Churchill, G.A.; Higgins, D.C.; Johns, C.; Makaritsis, K.P.; Gavras, H.; Paigen, B.; 2001 \emph{Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci.} Genomics, 71, 70--77. \bibitem{Keurentjes2006} Keurentjes, J. J.; Fu, J.; de Vos, C. H.; Lommen, A.; Hall, R. D.; Bino, R. J.; van der Plas, L. H.; Jansen, R. C.; Vreugdenhil, D.; Koornneef, M.; 2006 \emph{The genetics of plant metabolism.} Nature Genetics. 38, 842--849. \bibitem{Koornneef1998} Alonso-Blanco, C.; Peeters, A. J.; Koornneef, M.; Lister, C.; Dean, C.; van den Bosch, N.; Pot, J.; Kuiper, M. T.; 1998 \emph{ Development of an AFLP based linkage map of Ler, Col; Cvi Arabidopsis thaliana ecotypes; construction of a Ler/Cvi recombinant inbred line population.} Plant J. 14, 259--271. \bibitem{breitling08} Breitling, R.; Li, Y.; Tesson, B. M.; Fu, J.; Wu, C.; Wiltshire, T.; Gerrits, A.; Bystrykh, L. V.; de Haan, G.; Su, A. I.; Jansen, R. C.; 2008 \emph{Genetical genomics: spotlight on QTL hotspots.} PLoS Genet. 4/10. \end{thebibliography} \end{document} qtl/inst/doc/Sources/MQM/mqm/0000755000175100001440000000000012567121772015473 5ustar hornikusersqtl/inst/doc/Sources/MQM/mqm/description.txt0000644000175100001440000000266312422233634020555 0ustar hornikusersMultiple QTL Mapping (MQM) provides a sensitive approach for mapping quantititive trait loci (QTL) in experimental populations. MQM adds higher statistical power compared to many other methods. The theoretical framework of MQM was introduced and explored by Ritsert Jansen, explained in the `Handbook of Statistical Genetics' (see references), and used effectively in practical research, with the commercial `mapqtl' software package. Here we present the first free and open source implementation of MQM, with extra features like high performance parallelization on multi-CPU computers, new plots and significance testing. MQM is an automatic three-stage procedure in which, in the first stage, missing data is `augmented'. In other words, rather than guessing one likely genotype, multiple genotypes are modeled with their estimated probabilities. In the second stage important markers are selected by multiple regression and backward elimination. In the third stage a QTL is moved along the chromosomes using these pre-selected markers as cofactors, except for the markers in the window around the interval under study. QTL are (interval) mapped using the most `informative' model through maximum likelihood. A refined and automated procedure for cases with large numbers of marker cofactors is included. The method internally controls false discovery rates (FDR) and lets users test different QTL models by elimination of non-significant cofactors. qtl/inst/doc/Sources/MQM/mqm/standard_references.txt0000644000175100001440000000176512422233634022235 0ustar hornikusers \item Arends D, Prins P, Jansen RC. R/qtl: High-throughput multiple QTL mapping. \emph{Bioinformatics}, to appear \item Jansen RC, (2007) Quantitative trait loci in inbred lines. Chapter 18 of \emph{Handbook of Stat. Genetics} 3rd edition. John Wiley & Sons, Ltd. \item Jansen RC, Nap JP (2001), Genetical genomics: the added value from segregation. \emph{Trends in Genetics}, \bold{17}, 388--391. \item Jansen RC, Stam P (1994), High resolution of quantitative traits into multiple loci via interval mapping. \emph{Genetics}, \bold{136}, 1447--1455. \item Jansen RC (1993), Interval mapping of multiple quantitative trait loci. \emph{Genetics}, \bold{135}, 205--211. \item Swertz MA, Jansen RC. (2007), Beyond standardization: dynamic software infrastructures for systems biology. \emph{Nat Rev Genet.} \bold{3}, 235--243. \item Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. \emph{J. Roy. Statist. Soc.} B, \bold{39}, 1--38. qtl/inst/doc/Sources/MQM/mqm/standard_example.txt0000644000175100001440000000077012422233634021542 0ustar hornikusersdata(map10) # Genetic map modeled after mouse # simulate a cross (autosomes 1-10) qtl <- c(3,15,1,0) # QTL model: chr, pos'n, add've & dom effects cross <- sim.cross(map10[1:10],qtl,n=100,missing.prob=0.01) # MQM crossaug <- mqmaugment(cross) # Augmentation cat(crossaug$mqm$Nind,'real individuals retained in dataset', crossaug$mqm$Naug,'individuals augmented\n') result <- mqmscan(crossaug) # Scan # show LOD interval of the QTL on chr 3 lodint(result,chr=3) qtl/inst/doc/Sources/MQM/mqm/standard_seealso.txt0000644000175100001440000000117312422233634021540 0ustar hornikusers \item The MQM tutorial: \url{http://www.rqtl.org/tutorials/MQM-tour.pdf} \item \code{\link{MQM}} - MQM description and references \item \code{\link{mqmscan}} - Main MQM single trait analysis \item \code{\link{mqmscanall}} - Parallellized traits analysis \item \code{\link{mqmaugment}} - Augmentation routine for estimating missing data \item \code{\link{mqmautocofactors}} - Set cofactors using marker density \item \code{\link{mqmsetcofactors}} - Set cofactors at fixed locations \item \code{\link{mqmpermutation}} - Estimate significance levels \item \code{\link{scanone}} - Single QTL scanning qtl/inst/doc/Sources/MQM/mqm/limitations.txt0000644000175100001440000000063112422233634020557 0ustar hornikusers The current implementation of R/qtl-MQM has the following limitations: (1) MQM is limited to experimental crosses F2, BC, and selfed RIL, (2) MQM does not treat sex chromosomes differently from autosomal chromosomes - though one can introduce sex as a cofactor. Future versions of R/qtl-MQM may improve on these points. Check the website and change log (\url{http://www.rqtl.org/STATUS.txt}) for updates. qtl/inst/doc/Sources/MQM/mqm/advantages_latex.txt0000644000175100001440000000072312422233634021537 0ustar hornikusersR/qtl-MQM has the following advantages: \begin{itemize} \item Higher power, as long as the QTL explain a reasonable amount of variation \item Protection against overfitting, because it fixes the residual variance from the full model. For this reason more parameters (cofactors) can be used compared to, for example, CIM \item Prevention of ghost QTL (between two QTL in coupling phase) \item Detection of negating QTL (QTL in repulsion phase) \end{itemize} qtl/inst/doc/Sources/MQM/mqm/advantages_Rd.txt0000644000175100001440000000075012422233634020767 0ustar hornikusersR/qtl-MQM has the following advantages: \itemize{ \item Higher power to detect linked as well as unlinked QTL, as long as the QTL explain a reasonable amount of variation \item Protection against overfitting, because it fixes the residual variance from the full model. For this reason more parameters (cofactors) can be used compared to, for example, CIM \item Prevention of ghost QTL (between two QTL in coupling phase) \item Detection of negating QTL (QTL in repulsion phase)} qtl/inst/doc/Sources/MQM/mqm/significance_references.txt0000644000175100001440000000046212422233634023050 0ustar hornikusers \item Bruno M. Tesson, Ritsert C. Jansen (2009) Chapter 3.7. Determining the significance threshold \emph{eQTL Analysis in Mice and Rats} \bold{1}, 20--25 \item Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. \emph{Genetics} \bold{138}, 963--971. qtl/inst/doc/Sources/MQM/mqm/parallelisation_references.txt0000644000175100001440000000047112422233634023611 0ustar hornikusers \item Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing. \emph{R. UW Biostatistics working paper series} University of Washington. \bold{193} \item Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. 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Rî0ˆäÓ‘,G@¤JX XD+̶’Œ¶y ¶bXÄDº|‘Ì A$— ˆ”þ –u‘¶â ’5D ¨Hn?Éé2MÜ,R "}Á¶¤pH±>° <`½Àê}À¶4‚I=É2Q Äæÿv?Ø^¦T°¿fmû—ýØmÁ R<D 2H°,ïAc!• endstream endobj startxref 237981 %%EOF qtl/inst/doc/new_multiqtl.R0000644000175100001440000003326512422233634015473 0ustar hornikusers################################################### ### chunk number 1: ################################################### #line 40 "new_multiqtl.Rnw" options(width=87, digits=3, scipen=4) ################################################### ### chunk number 2: myround ################################################### #line 45 "new_multiqtl.Rnw" source("myround.R") ################################################### ### chunk number 3: loaddata ################################################### #line 137 "new_multiqtl.Rnw" library(qtl) data(hyper) ################################################### ### chunk number 4: simgeno eval=FALSE ################################################### ## #line 150 "new_multiqtl.Rnw" ## hyper <- sim.geno(hyper, step=2, n.draws=128, err=0.001) ################################################### ### chunk number 5: runsimgeno ################################################### #line 153 "new_multiqtl.Rnw" file <- "Rcache/simgeno.RData" if(file.exists(file)) { load(file) } else { set.seed(94743379) #line 150 "new_multiqtl.Rnw#from line#158#" hyper <- sim.geno(hyper, step=2, n.draws=128, err=0.001) #line 159 "new_multiqtl.Rnw" save(hyper, file=file) } ################################################### ### chunk number 6: makeqtl ################################################### #line 171 "new_multiqtl.Rnw" qtl <- makeqtl(hyper, chr=c(1, 4, 6, 15), pos=c(67.3, 30, 60, 17.5)) ################################################### ### chunk number 7: print.qtl ################################################### #line 178 "new_multiqtl.Rnw" qtl ################################################### ### chunk number 8: plotqtl eval=FALSE ################################################### ## #line 185 "new_multiqtl.Rnw" ## plot(qtl) ################################################### ### chunk number 9: plotqtlplot ################################################### #line 191 "new_multiqtl.Rnw" par(mar=c(4.1,4.1,4.1,0.1), cex.axis=0.8, cex.main=1, cex=0.7) #line 185 "new_multiqtl.Rnw#from line#192#" plot(qtl) #line 193 "new_multiqtl.Rnw" ################################################### ### chunk number 10: fitqtl1 ################################################### #line 208 "new_multiqtl.Rnw" out.fq <- fitqtl(hyper, qtl=qtl, formula=y~Q1+Q2+Q3*Q4) summary(out.fq) ################################################### ### chunk number 11: refineqtl eval=FALSE ################################################### ## #line 231 "new_multiqtl.Rnw" ## rqtl <- refineqtl(hyper, qtl=qtl, formula=y~Q1+Q2+Q3*Q4, verbose=FALSE) ################################################### ### chunk number 12: runrefineqtl ################################################### #line 234 "new_multiqtl.Rnw" file <- "Rcache/refineqtl.RData" if(file.exists(file)) { load(file) } else { #line 231 "new_multiqtl.Rnw#from line#238#" rqtl <- refineqtl(hyper, qtl=qtl, formula=y~Q1+Q2+Q3*Q4, verbose=FALSE) #line 239 "new_multiqtl.Rnw" save(rqtl, file=file) } ################################################### ### chunk number 13: printrefineqtl ################################################### #line 247 "new_multiqtl.Rnw" rqtl ################################################### ### chunk number 14: fitqtl2 ################################################### #line 255 "new_multiqtl.Rnw" out.fq2 <- fitqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4, dropone=FALSE) summary(out.fq2) ################################################### ### chunk number 15: lodprofile eval=FALSE ################################################### ## #line 269 "new_multiqtl.Rnw" ## plotLodProfile(rqtl) ################################################### ### chunk number 16: plotlodprofile ################################################### #line 276 "new_multiqtl.Rnw" par(mar=c(4.1,4.1,4.1,0.1), cex=0.8) plotLodProfile(rqtl) ################################################### ### chunk number 17: addint ################################################### #line 318 "new_multiqtl.Rnw" addint(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4) ################################################### ### chunk number 18: addint2 ################################################### #line 327 "new_multiqtl.Rnw" addint(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3+Q4) ################################################### ### chunk number 19: addqtl eval=FALSE ################################################### ## #line 344 "new_multiqtl.Rnw" ## out.aq <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4) ################################################### ### chunk number 20: runnaddqtl ################################################### #line 347 "new_multiqtl.Rnw" file <- "Rcache/addqtl.RData" if(file.exists(file)) { load(file) } else { #line 344 "new_multiqtl.Rnw#from line#351#" out.aq <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4) #line 352 "new_multiqtl.Rnw" save(out.aq, file=file) } ################################################### ### chunk number 21: maxaddqtl ################################################### #line 362 "new_multiqtl.Rnw" max(out.aq) ################################################### ### chunk number 22: plotaddqtl eval=FALSE ################################################### ## #line 368 "new_multiqtl.Rnw" ## plot(out.aq) ################################################### ### chunk number 23: plotaddqtlplot ################################################### #line 374 "new_multiqtl.Rnw" par(mar=c(4.1,4.1,4.1,0.1)) #line 368 "new_multiqtl.Rnw#from line#375#" plot(out.aq) #line 376 "new_multiqtl.Rnw" ################################################### ### chunk number 24: addqtlint eval=FALSE ################################################### ## #line 388 "new_multiqtl.Rnw" ## out.aqi <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4+Q4*Q5) ################################################### ### chunk number 25: runaddqtlint ################################################### #line 392 "new_multiqtl.Rnw" file <- "Rcache/addqtlint.RData" if(file.exists(file)) { load(file) } else { #line 388 "new_multiqtl.Rnw#from line#396#" out.aqi <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4+Q4*Q5) #line 397 "new_multiqtl.Rnw" save(out.aqi, file=file) } ################################################### ### chunk number 26: plotaddqtlint eval=FALSE ################################################### ## #line 404 "new_multiqtl.Rnw" ## plot(out.aqi) ################################################### ### chunk number 27: plotaddqtlintplot ################################################### #line 410 "new_multiqtl.Rnw" par(mar=c(4.1,4.1,4.1,0.1)) #line 404 "new_multiqtl.Rnw#from line#411#" plot(out.aqi) #line 412 "new_multiqtl.Rnw" ################################################### ### chunk number 28: plotaddqtlint2 eval=FALSE ################################################### ## #line 424 "new_multiqtl.Rnw" ## plot(out.aqi - out.aq) ################################################### ### chunk number 29: plotaddqtlint2plot ################################################### #line 430 "new_multiqtl.Rnw" par(mar=c(4.1,4.1,4.1,0.1)) #line 424 "new_multiqtl.Rnw#from line#431#" plot(out.aqi - out.aq) #line 432 "new_multiqtl.Rnw" ################################################### ### chunk number 30: addpair eval=FALSE ################################################### ## #line 465 "new_multiqtl.Rnw" ## out.ap <- addpair(hyper, qtl=rqtl, chr=1, formula=y~Q2+Q3*Q4, verbose=FALSE) ################################################### ### chunk number 31: runaddpair ################################################### #line 468 "new_multiqtl.Rnw" file <- "Rcache/addpair.RData" if(file.exists(file)) { load(file) } else { #line 465 "new_multiqtl.Rnw#from line#472#" out.ap <- addpair(hyper, qtl=rqtl, chr=1, formula=y~Q2+Q3*Q4, verbose=FALSE) #line 473 "new_multiqtl.Rnw" save(out.ap, file=file) } ################################################### ### chunk number 32: summaddpair ################################################### #line 481 "new_multiqtl.Rnw" summary(out.ap) ################################################### ### chunk number 33: plotaddpair eval=FALSE ################################################### ## #line 495 "new_multiqtl.Rnw" ## plot(out.ap, lower="cond-int", upper="cond-add") ################################################### ### chunk number 34: plotaddpairplot ################################################### #line 501 "new_multiqtl.Rnw" plot(out.ap, lower="cond-int", upper="cond-add", layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) ################################################### ### chunk number 35: addpair2 eval=FALSE ################################################### ## #line 534 "new_multiqtl.Rnw" ## out.ap2 <- addpair(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3+Q5*Q6+Q3:Q5, chr=c(7,15), ## verbose=FALSE) ################################################### ### chunk number 36: runaddpair2 ################################################### #line 538 "new_multiqtl.Rnw" file <- "Rcache/addpair2.RData" if(file.exists(file)) { load(file) } else { #line 534 "new_multiqtl.Rnw#from line#542#" out.ap2 <- addpair(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3+Q5*Q6+Q3:Q5, chr=c(7,15), verbose=FALSE) #line 543 "new_multiqtl.Rnw" save(out.ap2, file=file) } ################################################### ### chunk number 37: summaddpair2 ################################################### #line 567 "new_multiqtl.Rnw" summary(out.ap2) ################################################### ### chunk number 38: plotaddpair2 eval=FALSE ################################################### ## #line 592 "new_multiqtl.Rnw" ## plot(out.ap2) ################################################### ### chunk number 39: plotaddpair2plot ################################################### #line 598 "new_multiqtl.Rnw" plot(out.ap2, layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) ################################################### ### chunk number 40: addtoqtl ################################################### #line 644 "new_multiqtl.Rnw" rqtl <- addtoqtl(hyper, rqtl, 1, 43.3) rqtl ################################################### ### chunk number 41: replaceqtl ################################################### #line 657 "new_multiqtl.Rnw" rqtl <- replaceqtl(hyper, rqtl, 1, 1, 73.3) rqtl ################################################### ### chunk number 42: reorderqtl ################################################### #line 667 "new_multiqtl.Rnw" rqtl <- reorderqtl(rqtl, c(5,1:4)) rqtl ################################################### ### chunk number 43: dropfromqtl ################################################### #line 674 "new_multiqtl.Rnw" rqtl <- dropfromqtl(rqtl, 2) rqtl ################################################### ### chunk number 44: stepqtl1 eval=FALSE ################################################### ## #line 717 "new_multiqtl.Rnw" ## stepout1 <- stepwiseqtl(hyper, additive.only=TRUE, max.qtl=6, ## verbose=FALSE) ################################################### ### chunk number 45: runstepqtl1 ################################################### #line 721 "new_multiqtl.Rnw" file <- "Rcache/stepqtl1.RData" if(file.exists(file)) { load(file) } else { #line 717 "new_multiqtl.Rnw#from line#725#" stepout1 <- stepwiseqtl(hyper, additive.only=TRUE, max.qtl=6, verbose=FALSE) #line 726 "new_multiqtl.Rnw" save(stepout1, file=file) } ################################################### ### chunk number 46: printstepqtl1 ################################################### #line 733 "new_multiqtl.Rnw" stepout1 ################################################### ### chunk number 47: stepqtl2 eval=FALSE ################################################### ## #line 744 "new_multiqtl.Rnw" ## stepout2 <- stepwiseqtl(hyper, max.qtl=6, keeptrace=TRUE, ## verbose=FALSE) ################################################### ### chunk number 48: runstepqtl2 ################################################### #line 748 "new_multiqtl.Rnw" file <- "Rcache/stepqtl2.RData" if(file.exists(file)) { load(file) } else { #line 744 "new_multiqtl.Rnw#from line#752#" stepout2 <- stepwiseqtl(hyper, max.qtl=6, keeptrace=TRUE, verbose=FALSE) #line 753 "new_multiqtl.Rnw" save(stepout2, file=file) } ################################################### ### chunk number 49: printstepqtl2 ################################################### #line 761 "new_multiqtl.Rnw" stepout2 ################################################### ### chunk number 50: attributenames ################################################### #line 774 "new_multiqtl.Rnw" names(attributes(stepout2)) ################################################### ### chunk number 51: thetrace ################################################### #line 784 "new_multiqtl.Rnw" thetrace <- attr(stepout2, "trace") thetrace[[1]] ################################################### ### chunk number 52: traceplot eval=FALSE ################################################### ## #line 800 "new_multiqtl.Rnw" ## par(mfrow=c(4,3)) ## for(i in seq(along=thetrace)) ## plotModel(thetrace[[i]], chronly=TRUE, ## main=paste(i, ": pLOD =", ## round(attr(thetrace[[i]], "pLOD"), 2))) ################################################### ### chunk number 53: plottraceplot ################################################### #line 810 "new_multiqtl.Rnw" par(mar=c(0.6,0.1,2.1,0.6)) #line 800 "new_multiqtl.Rnw#from line#811#" par(mfrow=c(4,3)) for(i in seq(along=thetrace)) 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out.em, ylim=c(-0.3, 0.3), ylab="LOD(HK)-LOD(EM)") sug <- sim.geno(sug, step=1, n.draws=64) out.imp <- scanone(sug, method="imp") plot(out.em, out.hk, out.imp, col=c("blue", "red", "green")) plot(out.em, out.hk, out.imp, col=c("blue", "red", "green"), chr=c(7,15)) plot(out.imp - out.em, out.hk - out.em, col=c("green", "red"), ylim=c(-1,1)) ############################################################ # Permutation tests ############################################################ load(url("http://www.rqtl.org/various.RData")) operm <- scanone(sug, method="hk", n.perm=1000) plot(operm) summary(operm) summary(operm, alpha=c(0.05, 0.2)) summary(out.hk, perms=operm, alpha=0.2, pvalues=TRUE) ############################################################ # Interval estimates of QTL location ############################################################ lodint(out.hk, chr=7) bayesint(out.hk, chr=7) lodint(out.hk, chr=7, expandtomarkers=TRUE) bayesint(out.hk, chr=7, expandtomarkers=TRUE) lodint(out.hk, chr=7, drop=2) bayesint(out.hk, chr=7, prob=0.99) lodint(out.hk, chr=15) bayesint(out.hk, chr=15) ############################################################ # QTL effects ############################################################ max(out.hk) mar <- find.marker(sug, chr=7, pos=47.7) plotPXG(sug, marker=mar) effectplot(sug, mname1=mar) effectplot(sug, mname1="7@47.7") max(out.hk, chr=15) mar2 <- find.marker(sug, chr=15, pos=12) plotPXG(sug, marker=mar2) effectplot(sug, mname1="15@12") plotPXG(sug, marker=c(mar, mar2)) plotPXG(sug, marker=c(mar2, mar)) effectplot(sug, mname1="7@47.7", mname2="15@12") effectplot(sug, mname2="7@47.7", mname1="15@12") ############################################################ # Other phenotypes ############################################################ out.hr <- scanone(sug, pheno.col=2, method="hk") out.bw <- scanone(sug, pheno.col="bw", method="hk") out.logbw <- scanone(sug, pheno.col=log(sug$pheno$bw), method="hk") out.all <- scanone(sug, pheno.col=1:4, method="hk") summary(out.all, threshold=3) summary(out.all, threshold=3, lodcolumn=4) summary(out.all, threshold=3, format="allpeaks") summary(out.all, threshold=3, format="allpheno") summary(out.all, threshold=3, format="tabByCol") summary(out.all, threshold=3, format="tabByChr") ############################################################ # Two-dimensional, two-QTL scans ############################################################ sug <- calc.genoprob(sug, step=2) out2 <- scantwo(sug, method="hk") plot(out2) plot(out2, lower="fv1") plot(out2, lower="fv1", upper="av1") operm2 <- scantwo(sug, method="hk", n.perm=5) summary(out2, perms=operm2, alpha=0.2, pvalues=TRUE) ############################################################ # Multiple-QTL analyses ############################################################ sug <- calc.genoprob(sug, step=1) qtl <- makeqtl(sug, chr=c(7,15), pos=c(47.7, 12), what="prob") out.fq <- fitqtl(sug, qtl=qtl, method="hk") summary(out.fq) summary(fitqtl(sug, qtl=qtl, method="hk", get.ests=TRUE, dropone=FALSE)) out.fqi <- fitqtl(sug, qtl=qtl, method="hk", formula=y~Q1*Q2) out.fqi <- fitqtl(sug, qtl=qtl, method="hk", formula=y~Q1+Q2+Q1:Q2) summary(out.fqi) addint(sug, qtl=qtl, method="hk") rqtl <- refineqtl(sug, qtl=qtl, method="hk") rqtl summary(out.fqr <- fitqtl(sug, qtl=rqtl, method="hk")) plotLodProfile(rqtl) plot(out.hk, chr=c(7,15), col="red", add=TRUE) out.aq <- addqtl(sug, qtl=rqtl, method="hk") plot(out.aq) print(pen <- calc.penalties(operm2)) out.sq <- stepwiseqtl(sug, max.qtl=5, penalties=pen, method="hk", verbose=2) out.sq # end of rqtltour2.R qtl/inst/doc/bcsft.Rnw0000644000175100001440000010136712566656320014426 0ustar hornikusers\documentclass[12pt,fullpage]{article} %\VignetteIndexEntry{Users Guide for New BCsFt Tools for R/qtl} \usepackage{fullpage} \marginparwidth 0pt \oddsidemargin 0pt \evensidemargin 0pt \topmargin 0pt \textwidth 16cm \textheight 21cm \usepackage{fancyheadings} \usepackage{amsmath} \usepackage{graphicx} \raggedbottom \lhead{\bf R/qtl bcsft vignette} \usepackage{cite} \bibliographystyle{plos} \chead{} \rhead{} \lfoot{} \cfoot{} \rfoot{} \setlength{\parindent}{0cm} \addtolength{\parskip}{\baselineskip} \setlength{\footrulewidth}{\headrulewidth} \usepackage{Sweave} \begin{document} \title{Users Guide for New $BC_sF_t$ Tools for R/qtl} \author{Laura M. Shannon \and Brian S. Yandell \and Karl Broman} \date{29 January 2013} \maketitle \section*{Introduction} Historically QTL mapping studies have employed a variety of crossing schemes including: backcrosses \cite{Tanks}, sib-mating \cite{Sib}, selfing \cite{Collard}, RI lines \cite{fly}, and generations of random mating within mapping populations \cite{Darvasi}. Different cross designs offer different advantages. Backcrossing allows for the isolation of limited regions of the donor parent genome in an otherwise recurrent parent background. Selfing and sib-mating in an intercross provide the opportunity to examine all genotype combinations and observe dominance. RI lines allow for multiple phenotype measures on a single line. Random mating increases recombination frequency. In order to use a combination of these cross types and access their various benefits, a more flexible analysis approach is needed. \begin{figure} \includegraphics{why_we_need_a_new_program.pdf} \caption{An illustration of QTL geneotype inference in populations created through different crossing structures. All images are of a chromosome section including 2 markers (A and B) and a putative QTL (Q). Chromosomal segments are pink when they share a genotype with the lower case parent and black when they share a genotype with the capital parent. Regions where the genotype cannot be observed are dashed. Regions where the genotype is unknown are gray.} \label{Crossovers} \end{figure} This guide develops methods to analyze advanced backcrosses and lines created by repeated selfing by extending features of R/qtl \cite{Broman}. Interval mapping requires estimating the probable genotype of a putative QTL based on the neighboring markers \cite{Lander}. The probability that a loci between two genotyped markers is of a given genotype depends on the recombination history of the population, which depends on the type of cross. In Figure \ref{Crossovers} we have two markers, A and B, each with two possible allele genotypes, capital or lower case. Let us assume that markers A and B are spaced such that double crossovers in a single generation are unlikely. Let Q be the position of the putative QTL between A and B. When the observed genotypes at A and B are both homozygous capital in an $F_2$ or $BC_1$ the genotype at Q is most likely homozygous capital (Figure \ref{Crossovers} part B). However, in an $F_3$, Q might be heterozygous or homozygous for either parent (Figure \ref{Crossovers} part C). Similarly, in a $BC_2$, Q might be homozygous capital or heterozygous, when the observered genotype is AB/AB. Each generation brings an additional opportunity for crossing over within the interval, increasing the likelihood that Q will not share a genotype with A and B. This has real consequences when determining genotype probabilities (Figure \ref{Probs}). \begin{figure} \includegraphics{genotypeprobabilities.pdf} \caption{ The probability that a pair of loci is of a given genotype based on the transition probabilities from the known genotype of marker one (As) to the unknown genotype of the putative QTL (Bs).} \label{Probs} \end{figure} Genetic map creation is also based on recombination history. Assuming an $F_2$ or $BC_1$ and sufficiently close markers to make double crossovers in a single generation improbable, individuals which are homozygous for the recurrent parent allele at two adjacent markers exhibit no recombination events between those two markers (figure \ref{Crossovers} part B). However, in an $F_3$ the state of being homozygous for the recurrent parent allele at neighboring markers can be accomplished with 0, 1, or 4 recombination events (figure \ref{Crossovers} part C). If an $F_3$ is treated as an $F_2$, an individual with 2 adjacent markers homozygous for the same parent will be counted as having undergone 0 recombination events. However, the actual expected number of recombination events for the described individual is: \[r^4+\frac{r(1-r)}{8}\approx r/8~,\] where $r$ is the recombination frequency. Therefore, treating an $F_3$ as an $F_2$ would artificially shorten the map length (Figure \ref{recomb}). The number of recombination events between two markers depends on the recombination frequency and cross history, and the number of recombination events in agregate determines the map length. \begin{figure} \includegraphics{recombinationcount.pdf} \caption{Estimated recombination counts between pairs of markers with observed genotypes.} \label{recomb} \end{figure} In this guide we present our method for analyzing mapping populations with advanced cross histories while avoiding the pitfalls described above. Specifically, we address populations resulting from repeated backcrossing ($BC_s$), repeated selfing ($F_t$), and backcrossing followed by selfing ($BC_sF_t$). The first section is a tutorial on how to use the new tools. The second section lays out the way we derived the equations for probabilities and recombination counts, which allow for the analysis of advanced cross histories. The third section contains a technical description of the modifications to the code of the previous release of R/qtl \cite{Broman}. \section*{Tutorial} These changes to R/qtl are mostly internal. The one thing that does change for the user is reading in the data. Data can be read in using {\em read.cross()} as for all other crosses. We will use the listeria sample data from R/qtl below. <<>>= library(qtl) listeria.bc2s3<-read.cross(format="csv", file=system.file(file.path("sampledata", "listeria.csv"), package = "qtl"), BC.gen=2, F.gen=3) @ Here's another way to convert a cross. Suppose the R/qtl hyper data was really a $BC_3$ (or $BC_3F_0$). You can convert it as follows: <<>>= data(hyper) hyper3 <- convert2bcsft(hyper, BC.gen = 3) @ We will briefly highlight the difference in results between crosses analyzed using the traditional program and those analyzed using our new tools. However, we do not discuss the entire process of QTL mapping. Please refer to the tutorials available through rqtl.org or A Guide to QTL Mapping with R/qtl by Karl Broman \cite{bromanbook} for guidence on complete analysis. First we compare the maps for the listeria data set (figure \ref{lismap}). % est.map(listeria.bc2s3) takes time <<>>= listeria.f2<-read.cross(format="csv", file=system.file(file.path("sampledata", "listeria.csv"), package = "qtl")) map.bc2s3 <- est.map(listeria.bc2s3) map.f2<-est.map(listeria.f2) @ Now, we will compare the maps for the hyper data (figure \ref{hypmap}). <<>>= map.bc1 <- est.map(hyper) map.bc3<-est.map(hyper3) @ \begin{figure} <>= plot(map.f2, map.bc2s3, label=FALSE, main="") @ \caption{A comaprison of genetic maps of the listeria data set analyzed as though it were a $F_2$ (left) and as though it were a $BC_2F_3$ (right).} \label{lismap} \end{figure} \begin{figure} <>= plot(map.bc1, map.bc3, label=FALSE, main="") @ \caption{ A comparison of genetic maps of the hyper data set analyzed as though it were a $BC_1$ (left) and as though it were a $BC_3$(right).} \label{hypmap} \end{figure} % calc.genoprob takes time. In both cases the map length is smaller when the cross is analyzed as a $BC_sF_t$ because the same number of recombination events are attributed to multiple generations. In order to demonstrate that the cross history makes a real difference in outcome of a QTL analysis, we asign the same map to both cross objects regardless of cross history for direct comparisson. Comparing identical data sets with identical maps using the {\em scanone} command illustraits that position-wise LOD score also depends on cross history (figures \ref{lisscan} and \ref{hypscan}) . <<>>= listeria.bc2s3<-replace.map(listeria.bc2s3, map.f2) listeria.f2<-replace.map(listeria.f2, map.f2) listeria.f2<-calc.genoprob(listeria.f2, step=1 ) one.f2<-scanone(listeria.f2, method="em",pheno.col=1) listeria.bc2s3<-calc.genoprob(listeria.bc2s3, step=1 ) one.bc2s3<-scanone(listeria.bc2s3, method="em",pheno.col=1) @ \begin{figure} <>= plot(one.f2, one.bc2s3, col=c("red", "purple")) @ \caption{LOD plots for simple interval mapping with the listeria data set. The red curves are from analysis as though the population were a $F_2$. The purple curves are from analysis as though the population were a $BC_2F_3$. Both were analyzed using the same map distances to facilitate comparison} \label{lisscan} \end{figure} <<>>= hyper3<-replace.map(hyper3, map.bc1) hyper<-replace.map(hyper, map.bc1) hyper<-calc.genoprob(hyper, step=1 ) one.hyp<-scanone(hyper, method="em",pheno.col=1) hyper3<-calc.genoprob(hyper3, step=1 ) one.hyp3<-scanone(hyper3, method="em",pheno.col=1) @ \begin{figure} <>= plot(one.hyp, one.hyp3, col=c("red", "purple")) @ \caption{LOD plots for simple interval mapping with the hyper data set. The red curves are from analysis as though the population were a $BC_1$. The purple curves are from analysis as though the population were a $BC_3$. Both were analyzed using the same map distances to facilitate comparison} \label{hypscan} \end{figure} \section*{Calculations} Allowing for the analysis of $BC_SF_T$ crosses in R/qtl required two new sets of calculations: genotype probabilities for different cross histories and recombination counts for these cross histories. The genotype probabilities were derived based on Jiang and Zeng's \cite{jiang} calculations and the recombination counts are estimated using a golden section search. \subsection*{Genotype Probabilities} Jiang and Zeng \cite{jiang} provide a guide for calculating genotype frequencies resulting from several types of crosses of inbred lines. Although they examine many cases ($F_2$, selfed $F_t$, random mating $F_t$, backcross from selfed $F_t$, and $BC_s$) they do not address all possible cross structures. Most notably, they do not discuss $BC_sF_t$ crosses. In this section we derive the equations for calculating genotype probabilities for a $BC_sF_t$ cross. The equations we arrived at are heavily based on those of Jiang and Zeng. However they have been modified both to address $BC_sF_t$ cross histories and to function within the context of the existing R/qtl program. We include all the implemented equations, both new and modified, below. QTL mapping requires estimating the putative QTL genotype based on the observed genotypes of flanking markers. In all cases there are 2 parental inbred lines. Line 1 will be indicated by capital letters, while line 2 will be indicated by lower case letters. A particular descendant of these lines has a known genotype at locus A (indicated with $A$ or $a$), however the genotype at locus B (indicated with $B$ or $b$), the putative QTL, has not been observed. The genotype at locus B is dependent on the genotype at locus A, the recombination rate between locus A and locus B ($r$), and the cross history. \subsubsection*{Backcross $BC_S$} The simplest case is a $BC_1$ with line 1 as the reccurrent parent. Let $q$ be a vector of the frequency of all possible genotypes of loci A and B \[q = \left[\begin{array}{cccc}freq(\frac{AB}{AB}) & freq(\frac{Ab}{AB}) & freq(\frac{aB}{AB}) & freq(\frac{ab}{AB})\end{array}\right] = \left[\begin{array}{cccc}\frac{w}{2} & \frac{r}{2} & \frac{r}{2} & \frac{w}{2}\end{array}\right] \] where $w=1-r$. After a subsequent generation of backcrossing the genotype frequencies will change based on the probability that a pair of loci with a particular genotype will produce offspring of each genotype when backcrossed to the recurrent parent. We will call this the transition probability. In order to calculate $q$ for a $BC_2$ we will need transition probabilities for all possible genotype combinations. Let $M$ be the matrix of transition probabilities. \[M=\left[\begin{array}{llll} P \left( \frac{AB}{AB}| \frac{AB}{AB}\right) & P \left(\frac{AB}{AB}|\frac{AB}{Ab}\right) & P \left( \frac{AB}{AB} |\frac{AB}{aB}\right) & P \left( \frac{AB}{AB}|\frac{AB}{ab}\right)\\[5pt] P \left(\frac{AB}{Ab} |\frac{AB}{AB} \right) & P \left((\frac{AB}{Ab} | \frac{AB}{Ab}\right) & P \left( \frac{AB}{Ab} |\frac{AB}{aB}\right) & P \left( \frac{AB}{Ab} |\frac{AB}{ab}\right)\\[5pt] P \left( \frac{AB}{aB}|\frac{AB}{AB} \right) & P \left( \frac{AB}{aB}|\frac{AB}{Ab}\right) & P \left( \frac{AB}{aB}| \frac{AB}{aB}\right) & P \left( \frac{AB}{aB}|\frac{AB}{ab}\right)\\[5pt] P \left( \frac{AB}{ab}| \frac{AB}{AB}\right) & P \left( \frac{AB}{ab}| \frac{AB}{Ab}\right) & P \left( \frac{AB}{ab}| \frac{AB}{aB}\right) & P \left( \frac{AB}{ab}| \frac{AB}{ab}\right)\end{array} \right] = \left[\begin{array}{cccc} \frac{w}{2} & \frac{r}{2} & \frac{r}{2} & \frac {w}{2}\\[5pt] 0 & \frac{1}{2} & 0 & \frac{1}{2}\\[5pt] 0 & 0 & \frac{1}{2} & \frac{1}{2}\\[5pt] 0 & 0 & 0 & 1 \end{array}\right] \] The frequency vector from the $BC_1$ can then be multiplied by the transition matrix to arrive at a frequency vector for a $BC_2$: \[q_{BC_2} = qM~. \] With each subsequent generation of backcrossing it is necessary to multiply by the transtion matrix again. The equation for determining genotype frequencies based on any number of backcross generations ($s$) is: \[q_{BC_s}=qM^{s-1}~.\] This can be further simplified \cite{Bulmer}. $P(s,0)$ is a set of probabilities for all genotype combinations at two loci in a $BC_s$ population. It is equivalent to $q_{BC_s}$, but organized differently to make it easier to read. \[P(s,0) =\begin{array}{ccc} & BB & Bb\\[5pt] AA & A_{11} & A_{12}\\[5pt] Aa & A_{12} & A_{22} \end{array} \] When $s=1$ \[A_{11}=A_{12}=\frac{w}{2}\] \[A_{12} = \frac{r}{2} \] For any value of $s$ \[A_{11}= \frac{2^s-2+w^s}{2^s} \] \[A_{12} = \frac{1-w^s}{2^s} \] \[A_{22}= \frac{w^s}{2^s} \] Note the symmetry on the diagonal of recombinant alleles (Ab/AB and aB/AB) but not on the diagonal with only non-recombinant alleles (AB/AB and ab/AB). This asymmetry is due to the fact that $ab$ alleles are only introduced in the $F_1$ and therefore all such alleles remaining in the population have never recombined where as $AB$ alleles are introduced every generation. Genotype frequencies can be calculated for all types of crosses using a vector of initial frequencies and a transition matrix. \subsubsection*{Repeated Selfing $F_t$} Next, we will discuss the calculations for genotype frequencies from an $F_t$ population resulting from repeated selfing. This crossing structure is also sometimes refered to as an $S_t$, but we are using $F_t$ to be consistant with the notation used by R/qtl. The major difference between the calculations for an $F_t$ and a $BC_s$ is that while in a backcross one allele is always AB, so there are only 4 genotype possibilities, in an $F_t$ there are 10 genotype possibilities. \[q_{F_1}=\left[\begin{array}{cccccccccc}\frac{AB}{AB} & \frac{AB}{Ab} & \frac{Ab}{Ab} & \frac{AB}{aB} & \frac{AB}{ab} & \frac{Ab}{aB} & \frac{Ab}{ab} & \frac{aB}{aB} & \frac{aB}{ab} & \frac{ab}{ab}\end{array}\right] =\left[\begin{array}{cccccccccc}0&0&0&0&1&0&0&0&0&0\end{array}\right] \] The transition matrix for an $F_t$ is the same as Jiang and Zeng \cite{jiang}: \[N=\left[\begin{array}{cccccccccc} 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\[5pt] \frac{1}{4} & \frac{1}{2} & \frac{1}{4} & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\[5pt] 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\[5pt] \frac{1}{4} & 0 & 0 & \frac{1}{2} & 0 & 0 & 0 & \frac{1}{4} & 0 & 0 \\[5pt] \frac{w^2}{4} & \frac{rw}{2} & \frac{r^2}{4} & \frac{rw}{2}& \frac{w^2}{2} & \frac{r^2}{2} & \frac{rw}{2} & \frac{r^2}{4} & \frac{rw}{2} & \frac{w^2}{4}\\[5pt] \frac{r^2}{4} & \frac{rw}{2} & \frac{w^2}{4} & \frac{rw}{2}& \frac{r^2}{2} & \frac{w^2}{2} & \frac{rw}{2} & \frac{w^2}{4} & \frac{rw}{2} & \frac{r^2}{4}\\[5pt] 0 & 0 & \frac{1}{4} & 0 & 0 & 0 & \frac{1}{2} & 0 & 0 & \frac{1}{4} \\[5pt] 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\[5pt] 0 & 0 & 0 & 0 & 0 & 0 & 0 & \frac{1}{4} & \frac{1}{2} & \frac{1}{4} \\[5pt] 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1\end{array} \right] \] Again, these can be multiplied to arrive at the probability of all genotypes in the $F_t$. \[q_{F_t}=q_{F_1}N^{t-1}\] This can be simplified. $P(0,t)$ contains the probabilities for all genotype combinations for two loci in an $F_t$ population (once again it is equivalent to $q_{F_t}$ reorganizes). \[P(0,t) = \begin{array}{ccccc} & BB & Bb & bB & bb\\[5pt] AA & B_{11} & B_{12} & B_{12} & B_{14}\\[5pt] Aa & B_{12} & B_{22} & B_{23} & B_{12}\\[5pt] aA & B_{12} & B_{23} & B_{22} & B_{12}\\[5pt] aa & B_{14} & B_{12} & B_{12} & B_{11} \end{array} \] The probabilities $B_{ij}$ of ending up in a particular genotype after $t$ generations can be modeled in terms of generations spent in the double heterozygous stage (at least 1, as $F_1$ is a double heterozygote), the probability of moving from that genotype to either one of the intermediate stages or to a double homozygote, the time spent at an intermediate stage (could be 0), and the probability of moving from an intermediate stage to a double homozygote. There are four transient states (double heterozygotes), 8 intermediate states (single heterozygotes) and 4 absorbing states (double heterozygotes). The only genotypes which can produce all other genotypes are the transient double heterozygotes ($B_{22}$ and $B_{23}$). Therefore with each generation there is an exponential decay in the probability of remaining in the double heterozygous state. In order to remain in the double heterozygous state there either has to be no recombination ($w^2$) or a double recombination event ($r^2$) in every generation. In order to model this we reparameterize $w^2$ and $r^2$ as $\beta$ and $\gamma$, specifically $\beta+\gamma=w^2$ while $\beta-\gamma = r^2$. $\beta$ is also the probability of remaining in a double heterozygous state given that the line started in one of the two double heterozygous states in a single generation. \[B_{22}= \frac{\beta^{t-1}+\gamma^{t-1}}{2} \] \[B_{23}= \frac{\beta^{t-1}-\gamma^{t-1}}{2} \] \[ \beta=\frac{w^2+r^2}{2} \] \[\gamma= \frac{w^2 - r^2}{2}\] The 8 intermediate states, with one locus homozygous and one heterozygous. During one of the previous generations, one locus was fixed while the other remained heterozygous. There are two exponential decays, with the transition point unknown. After some simplification, this can be expressed as$B_{12}$: \[B_{12}=\frac{rw\left(\frac{1}{2^{t-1}}-\beta^{t-1}\right)}{1-2\beta} \] Finally, the four absorbing states, heterozygous at both loci, can be reached from a number of paths, involving simultaneous or separate fixation of both loci. The calculations are more involved, but simplify ty $B_{11}$ or $B_{14}$: \[B_{11}= f(w,r)= \frac{1}{8} \left[ w^2 \left(g \left(\beta, t \right) + g \left( \gamma, t \right) \right) + r^2 \left(g \left( \beta, t \right) - g \left( \gamma, t \right) \right) \right] + \frac{rw}{5} \left[g \left( \beta ,t \right) + g \left(2\beta , t-1 \right) \right] \] \[B_{14}=f(r,w) \] With: \[g\left( \beta, t \right) = (1-\beta^{t-1}) / (1-\beta)~.\] Unlike P(s,0), P(0,t) is symmetric on both diagonals because both parental alleles are equally present in the $F_1$ and never introgressed again. One major difference between working with a backcross and an $F_t$ is that while in a backcross phase is always known, in an $F_t$ phase cannot be observed. When dealing with phase unknown data the two heterozygote cases can be collapsed as follows: \[ \begin{array}{cccc} & BB & Bb & bb\\[5pt] AA & B_{11} & 2B_{12} & B_{14}\\[5pt] Aa & 2B_{12} & 2\left(B_{22}+ B_{23}\right) & 2B_{12}\\[5pt] aa & B_{14} & 2B_{12} & B_{11} \end{array} \] Since we cannot distinguish between the two heterozygote classes we add them and report the frequency of both. The final difference between backcross and $F_t$ calculations is that for $F_t$ populations it is possible to have partially informative markers. Partially informative markers can only be interpreted as not belonging to a particular homozygous class. For instance if a marker were measured using the presence or absence of a band on a gel, heterozygotes would be indistinguishable from the homozygous present class. We will refer to partially informative markers as either "not AA" or "not aa". In order to calculate the probability of partially informative markers we add the probabilities of the genotypes we cannot distinguish between, much like the phase unknown case above. For example, $not\; AA/BB$ could be $Aa/BB$, $aA/BB$, or $aa/BB$ so we sum all of those probabilties to get $2B_{12}+B_{14}$. All other genotypes with partially informative markers can be calculated similarly. \subsubsection*{Backcrossing followed by selfing $BC_sF_t$} The described equations for the $BC_s$ and the $F_t$ form the basis for the $BC_sF_t$. The two types of crosses can be thought of sequentially. The $BC_s$ that forms the first steps of the $BC_SF_t$ is exactly the same as the $BC_S$ on it's own. The difference between calculating an $F_t$ which follows several genetations of backcrossing and one which follows an $F_1$ is the vector of starting genotype frequencies. In this case the starting genotype frequencies can be supplied by $q_{BC_S}=qM^{s-1}$. The six genotypes not represented all have starting frequency 0. \[q_{BC_SF_0} =\left[\begin{array}{cccccccccc} \frac{2^s-2+w^s}{2^s} & \frac{1-w^s}{2^s} & 0 & \frac{1-w^s}{2^s} & \frac{w^s}{2^s} & 0 & 0 & 0 & 0 & 0\end{array}\right] \] The $q$ resulting from this modification of $q_{BC_S}$ can be multiplied by the $F_t$ transition matrix. Much like in the previous cases this can be simplified. $P(s,t)$ contains the probabilities of all possible genotype combinations at two loci for a $BC_sF_t$. Below, we explicitly identify the parts of the equations from the backcross (A) and selfing (B) probablities. \[P(s,t) = \begin{array}{ccccc} & BB & Bb & bB & bb\\[5pt] AA & C_{11} & C_{12} & C_{12} & C_{14}\\[5pt] Aa & C_{12} & C_{22} & C_{23} & C_{24}\\[5pt] aA & C_{12} & C_{23} & C_{22} & C_{24}\\[5pt] aa & C_{14} & C_{24} & C_{24} & C_{44} \end{array} \] Where: \[C_{22}= A_{22}(s)B_{22}(t) \] \[C_{23}= A_{22}(s)B_{23}(t) \] \[C_{12}= A_{22}(s)B_{12}(t) + A_{12}(s)\left(\frac{1}{2}\right)^t \] \[C_{24}=A_{22}(s)B_{12}(t) \] \[C_{11}= A_{22}(s)B_{11}(t)+A_{12}(s)\left(1-\left(\frac{1}{2}\right)^t\right)+A_{11}(s) \] \[C_{14}= A_{22}(s)B_{11}(t)+A_{12}(s)\left(1-\left(\frac{1}{2}\right)^t\right) \] \[C_{44}=A_{22}(s)B_{11}(t) \] Because these probabilities depend on the backcross probabilities there is only symmetry on one diagonal when $s>0$. Partially informative markers and phase unknown data can be treated the same way as an $F_t$. \subsection*{Recombination Counts} In the previous implementation of R/qtl recombination counts were calculated, however for advanced crossing schemes there is no direct analytic solution. Instead we implemented a hill climbing algorithm using a golden section search \cite{Kiefer} which determines the most probable recombination frequency, rather than calculating an actual value. The search space starts between 0 and 0.5 (all possible recombination frequencies). The golden section search relies on comparing three points (figure \ref{search}). To start with the points are $r=0$, $r=0.5$, and $r=r_1$, where the value of $r_1$ is determined so that the ratio of a to a+b is equal to the ratio of a to b. Then a new point ($r=r_2$) is added in the larger interval so that the ratio of d to a is equal to the ratio of c to d. The set of 3 $r$ values containing the highest maximum likelihood (as compared to the null model of unlinked markers $r=0.5$) are kept, and the remaining value is dropped (in this case $r=0.5$). The search algorithm starts again with 0, $r_1$, and $r_2$ as the three points. This process repeats until tolerance for the minimum improvement in likelihood is reached, then the $r$ value with the highest likelihood is reported as the maximum likelihodd estimate used in the map. This provides an accurate estimate of recombination frequency. \begin{figure} \includegraphics{goldensectionsearch.pdf} \caption{An illustration of the golden section search} \label{search} \end{figure} \subsection*{A Note on Intercrosses and Random Matings} These equations are accurate for $BC_sF_t$ when $F_t$ refers to any number of selfed generations. We have not implemented code to address advanced intercross lines resulting from sib mating or random mating within an advanced cross. Below we sketch ideas to develop these algorithms. In an $F_2$, selfing and sib-mating are interchangeable in terms of calculations because the entire population has an identical $F_1$ genotype. However after the $F_2$, calculations get more complicated for sib-mated populations. Each $F_2$ is sib-mated to create an $F_3$. Sib-mating brings the added complication that we need to think about families instead of individuals. There are 10 possible $F_2$ genotypes leading to 55 possible combinations of cross parents and their next generation families. A transition matrix ($L$) analagous to the one for selfing ($N$) would have to be 55 x 55 and account for the probability that a family that resulted from a cross between a particular set of parents in the $F_{t-2}$ would yield a cross between another set of parents in the $F_{t-1}$. This would be multiplied by a vector of 55 starting probabilities for the $F_1$ ($q_{F_1}^*$), these being probabilities of genotypes for specific crosses rather than for individuals. Of course, for the $F_1$, there is only one type of cross $AB/ab X AB/ab$ which can be the parents of the $F_2$, and the probability of the other 54 types of crosses is 0. Multiplying these successively will result in the probabilities of the crosses that produce the $F_t$, since our actual question is the probability of the genotypes of the $F_t$ where one individual is selected from each family, we will need a second matrix, K. This matrix will give the probability for each of the 10 possible genotypes results based on the 55 possible crosses. The final result will be the probability of the 10 genotypes ($q_{F_t}$). The equation for the genotype probabilities after t generations of intercrossing, then is: \[q_{F_t}=(q_{F_1}L^{t-2})K~.\] Note that the selfed $F_t$ is a special case with only the 10 selfings of the 55 possible crosses being non-zero. In general, this formal equation can be simplified substantially by using symmetry arguments, and implemented in a similar manner to the selfing case. Transient and absorbing states can be handled in an analogous but somewhat more complicated manner to the selfed case. However, the devil is in the details! We consider the case of random mating after $s$ generations of backcross and $t$ generations of selfing. We begin with the $q_{BC_s F_t}$ 10-vector of phase-known genotype frequencies, and multiply by a 10$\times$4 matrix ($J$) to convert these frequencies into the four possible two-locus alleles ($u$). These allele frequencies are cross multiplied ($u_Tu$) to create a 4$\times$4 matrix of random mating frequencis of genotypes, which are then reduced to the 10-vector format of phase-known genotype frequencies ($q_{BC_s F_t R}$). Eight of the rows of the matrix $J$ are simple (0, 0.5 or 1 values), while the middle two involve the possible recombinants and non-recombinants: \[J= \begin{array}{ccccc} & AB & Ab & aB & ab \\[5pt] \frac{AB}{AB} & 1 & 0 & 0 & 0\\[5pt] \frac{AB}{Ab} & \frac{1}{2} & \frac{1}{2} & 0 & 0\\[5pt] \frac{Ab}{Ab} & 0 & 1 & 0 & 0 \\[5pt] \frac{AB}{aB} & \frac{1}{2} & 0 & \frac{1}{2} & 0 \\[5pt] \frac{AB}{ab} & \frac{w}{2} & \frac{r}{2} & \frac{r}{2} & \frac{w}{2} \\[5pt] \frac{aB}{Ab} & \frac{r}{2} & \frac{w}{2} & \frac{w}{2} & \frac{r}{2}\\[5pt] \frac{ab}{Ab} & 0 & \frac{1}{2} & 0 & \frac{1}{2} \\[5pt] \frac{aB}{aB} & 0 & 0 & 1 & 0 \\[5pt] \frac{ab}{aB} & 0 & 0 & \frac{1}{2} & \frac{1}{2} \\[5pt] \frac{ab}{ab} & 0 & 0 & 0 & 1 \end{array} \] Extension to sib-mating instead of selfing follows directly. Extension to selfing or sib-mating after random mating follows as for the $F_t$ approach outlined earlier. Again, the devil is in the details. \section*{Code modifications in R/qtl} Our goal was to make R/qtl fully functional for a wider variety of cross types. Ideally these changes will be minimally visible to the user after inputing the cross type. In order to create an identical user experience we had to modify all R routines so that they would recognize the \texttt{bcsft} cross type. Cross objects in R/qtl all have the attribute "class" consisting of 2 parts: one which identifies it as a cross object and one which specifies the cross type (\texttt{bc}, \texttt{f2}, \texttt{riself}, \texttt{risib}, etc.). We added an additional option, \texttt{bcsft}. A major difference between the previous cross types and \texttt{bcsft}, all other cross types are specific. In that there are no options for types of backcrosses, it's just a backcross. With the $BC_sF_t$ we intentionally created a more flexible cross type, where the generation number can be set by the user. This means that we don't have to go back and add a cross type every time we want to analyze a population with a different history. The way we have created this flexibity is by adding the attribute \texttt{cross.scheme} to cross objects. The \texttt{cross.scheme} consists of two numbers, the first is the generations of backcrossing (s), the second is the generations of selfing (t). The addition of a cross type and an attribute allow all R routines to recognize all types of $BC_sF_t$ crosses. The previous sections detailed the way genotype probabilities and recombination counts are calculated for $BC_sF_t$ crosses. These calculations are contained within the specific \texttt{init}, \texttt{emit}, and \texttt{step} functions for \texttt{bcsft} within the C code. All three of these functions are used in the Hidden Markov Model (HMM). The \texttt{init} function determines the probability of true genotypes. The \texttt{emit} function determines the probability of observed genotypes given the true genotypes, while the \texttt{step} function determines the probability of a genotype at a particular locus given the genotype at a linked locus as described in the previous section. We created $BC_sF_t$ versions of all of these functions which follow the same format and work the same way as the existing versions, except for when they are called by \texttt{ est.map}. We did not find a closed form solution for calculating the number of recombination events between pairs of markers in a $BC_sF_t$ and so we implemented a golden section search as part of \texttt{est.map} instead. There is a second difference between the way the HMM is implemented for $BC_sF_t$ and all other types of crosses, leading to an improvement in efficiency with no effect on the estimates. Previously the probabilities for each pair of markers for each individual were calculated independently given the recombination rate between those markers in the entire data set. For the $BC_sF_t$ the entire set of probabilities is calculated once for a set of markers given the recombination rate and then applied to all individuals. In a population with 100 lines, this is the difference between 10 calculations (1 for each possible genotype combination) and 100 calculations. This method could be readily expanded to analyze populations with mixed cross histories (where some lines have undergone more generations of selfing or backcrossing than others). Recombination rates could be calculated across all individuals and then probabilities would be calculated separately for each cross history and applied to pairs of markers in an individual according to cross history. However, record keeping about cross histories for each individual line would need to be implemented in the package. Genotype probabilities differ for the autosomes and sex chromosomes. While this is not an issue for selfed populations it could be an issue in an advanced backcross or advanced intercross populations. We have arranged for proper handling of the X chromosome. Basically in an $F_t$ the X chromosome is treated as though it were the product of a $BC_t$. The only real change here is that we created the capacity to keep track of $t$. 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qtl/inst/doc/geneticmaps.R0000644000175100001440000010125012422233634015234 0ustar hornikusers### R code from vignette source 'geneticmaps.Rnw' ################################################### ### code chunk number 1: geneticmaps.Rnw:38-40 ################################################### options(width=87, digits=3, scipen=4) set.seed(61777369) ################################################### ### code chunk number 2: myround ################################################### source("myround.R") ################################################### ### code chunk number 3: loaddata ################################################### library(qtl) data(mapthis) ################################################### ### code chunk number 4: readdata (eval = FALSE) ################################################### ## mapthis <- read.cross("csv", "http://www.rqtl.org/tutorials", "mapthis.csv", ## estimate.map=FALSE) ################################################### ### code chunk number 5: summarycross ################################################### summary(mapthis) ################################################### ### code chunk number 6: plotmissing (eval = FALSE) ################################################### ## plotMissing(mapthis) ################################################### ### code chunk number 7: plotmissingplot ################################################### par(mar=c(4.1,4.1,0.6,1.1)) plotMissing(mapthis, main="") ################################################### ### code chunk number 8: plotntyped (eval = FALSE) ################################################### ## par(mfrow=c(1,2), las=1) ## plot(ntyped(mapthis), ylab="No. typed markers", main="No. genotypes by individual") ## plot(ntyped(mapthis, "mar"), ylab="No. typed individuals", ## main="No. genotypes by marker") ################################################### ### code chunk number 9: plotntypedplot ################################################### par(mfrow=c(1,2), las=1, cex=0.8) plot(ntyped(mapthis), ylab="No. typed markers", main="No. genotypes by individual") plot(ntyped(mapthis, "mar"), ylab="No. typed individuals", main="No. genotypes by marker") ################################################### ### code chunk number 10: dropind ################################################### mapthis <- subset(mapthis, ind=(ntyped(mapthis)>50)) ################################################### ### code chunk number 11: dropmarkers ################################################### nt.bymar <- ntyped(mapthis, "mar") todrop <- names(nt.bymar[nt.bymar < 200]) mapthis <- drop.markers(mapthis, todrop) ################################################### ### code chunk number 12: comparegeno (eval = FALSE) ################################################### ## cg <- comparegeno(mapthis) ## hist(cg[lower.tri(cg)], breaks=seq(0, 1, len=101), xlab="No. matching genotypes") ## rug(cg[lower.tri(cg)]) ################################################### ### code chunk number 13: comparegenoplot ################################################### cg <- comparegeno(mapthis) par(mar=c(4.1,4.1,0.1,0.6),las=1) hist(cg[lower.tri(cg)], breaks=seq(0, 1, len=101), xlab="No. matching genotypes", main="") rug(cg[lower.tri(cg)]) ################################################### ### code chunk number 14: matchingpairs ################################################### wh <- which(cg > 0.9, arr=TRUE) wh <- wh[wh[,1] < wh[,2],] wh ################################################### ### code chunk number 15: matchinggenotypes ################################################### g <- pull.geno(mapthis) table(g[144,], g[292,]) table(g[214,], g[216,]) table(g[238,], g[288,]) ################################################### ### code chunk number 16: dropmismatches ################################################### for(i in 1:nrow(wh)) { tozero <- !is.na(g[wh[i,1],]) & !is.na(g[wh[i,2],]) & g[wh[i,1],] != g[wh[i,2],] mapthis$geno[[1]]$data[wh[i,1],tozero] <- NA } ################################################### ### code chunk number 17: omitdup ################################################### mapthis <- subset(mapthis, ind=-wh[,2]) ################################################### ### code chunk number 18: finddupmar ################################################### print(dup <- findDupMarkers(mapthis, exact.only=FALSE)) ################################################### ### code chunk number 19: genotable ################################################### gt <- geno.table(mapthis) gt[gt$P.value < 0.05/totmar(mapthis),] ################################################### ### code chunk number 20: dropbadmarkers ################################################### todrop <- rownames(gt[gt$P.value < 1e-10,]) mapthis <- drop.markers(mapthis, todrop) ################################################### ### code chunk number 21: genofreqbyind (eval = FALSE) ################################################### ## g <- pull.geno(mapthis) ## gfreq <- apply(g, 1, function(a) table(factor(a, levels=1:3))) ## gfreq <- t(t(gfreq) / colSums(gfreq)) ## par(mfrow=c(1,3), las=1) ## for(i in 1:3) ## plot(gfreq[i,], ylab="Genotype frequency", main=c("AA", "AB", "BB")[i], ## ylim=c(0,1)) ################################################### ### code chunk number 22: plotgenofreqbyind ################################################### g <- pull.geno(mapthis) gfreq <- apply(g, 1, function(a) table(factor(a, levels=1:3))) gfreq <- t(t(gfreq) / colSums(gfreq)) par(mfrow=c(1,3), las=1) for(i in 1:3) plot(gfreq[i,], ylab="Genotype frequency", main=c("AA", "AB", "BB")[i], ylim=c(0,1)) ################################################### ### code chunk number 23: triangleplot ################################################### source("holmans_triangle.R") par(mar=rep(0.1,4), pty="s") triplot(labels=c("AA","AB","BB")) tripoints(gfreq, cex=0.8) tripoints(c(0.25, 0.5, 0.25), col="red", lwd=2, cex=1, pch=4) ################################################### ### code chunk number 24: pairwiselinkage ################################################### mapthis <- est.rf(mapthis) ################################################### ### code chunk number 25: checkAlleles ################################################### checkAlleles(mapthis, threshold=5) ################################################### ### code chunk number 26: lodvrf (eval = FALSE) ################################################### ## rf <- pull.rf(mapthis) ## lod <- pull.rf(mapthis, what="lod") ## plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") ################################################### ### code chunk number 27: lodvrfplot ################################################### rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") par(mar=c(4.1,4.1,0.6,0.6), las=1, cex=0.8) plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") ################################################### ### code chunk number 28: forminitialgroups ################################################### lg <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6) table(lg[,2]) ################################################### ### code chunk number 29: reorganizemarkers ################################################### mapthis <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6, reorgMarkers=TRUE) ################################################### ### code chunk number 30: plotrf (eval = FALSE) ################################################### ## plotRF(mapthis, alternate.chrid=TRUE) ################################################### ### code chunk number 31: plotrfplot ################################################### par(mar=c(4.1,4.1,2.1,2.1), las=1) plotRF(mapthis, main="", alternate.chrid=TRUE) ################################################### ### code chunk number 32: plotrfonemarker (eval = FALSE) ################################################### ## rf <- pull.rf(mapthis) ## lod <- pull.rf(mapthis, what="lod") ## mn4 <- markernames(mapthis, chr=4) ## par(mfrow=c(2,1)) ## plot(rf, mn4[3], bandcol="gray70", ylim=c(0,1), alternate.chrid=TRUE) ## abline(h=0.5, lty=2) ## plot(lod, mn4[3], bandcol="gray70", alternate.chrid=TRUE) ################################################### ### code chunk number 33: plotrfonemarkerplot ################################################### par(mar=c(4.1,4.1,1.1,0.6), las=1) rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") mn4 <- markernames(mapthis, chr=4) par(mfrow=c(2,1)) plot(rf, mn4[3], bandcol="gray70", ylim=c(0,1), alternate.chrid=TRUE) abline(h=0.5, lty=2) plot(lod, mn4[3], bandcol="gray70", alternate.chrid=TRUE) ################################################### ### code chunk number 34: genocrosstab ################################################### geno.crosstab(mapthis, mn4[3], mn4[1]) mn5 <- markernames(mapthis, chr=5) geno.crosstab(mapthis, mn4[3], mn5[1]) ################################################### ### code chunk number 35: switchalleles ################################################### toswitch <- markernames(mapthis, chr=c(5, 7:11)) mapthis <- switchAlleles(mapthis, toswitch) ################################################### ### code chunk number 36: plotrfagain (eval = FALSE) ################################################### ## mapthis <- est.rf(mapthis) ## plotRF(mapthis, alternate.chrid=TRUE) ################################################### ### code chunk number 37: plotrfagainplot ################################################### mapthis <- est.rf(mapthis) par(mar=c(4.1,4.1,2.1,2.1), las=1) plotRF(mapthis, main="", alternate.chrid=TRUE) ################################################### ### code chunk number 38: lodvrfagain (eval = FALSE) ################################################### ## rf <- pull.rf(mapthis) ## lod <- pull.rf(mapthis, what="lod") ## plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") ################################################### ### code chunk number 39: lodvrfagainplot ################################################### rf <- pull.rf(mapthis) lod <- pull.rf(mapthis, what="lod") par(mar=c(4.1,4.1,0.6,0.6), las=1, cex=0.8) plot(as.numeric(rf), as.numeric(lod), xlab="Recombination fraction", ylab="LOD score") ################################################### ### code chunk number 40: formgroupsagain ################################################### lg <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6) table(lg[,2]) ################################################### ### code chunk number 41: reorganizemarkersagain ################################################### mapthis <- formLinkageGroups(mapthis, max.rf=0.35, min.lod=6, reorgMarkers=TRUE) ################################################### ### code chunk number 42: plotrfyetagain (eval = FALSE) ################################################### ## plotRF(mapthis) ################################################### ### code chunk number 43: plotrfyetagainplot ################################################### mapthis <- est.rf(mapthis) par(mar=c(4.1,4.1,1.6,1.6), las=1) plotRF(mapthis, main="") ################################################### ### code chunk number 44: orderchrfive (eval = FALSE) ################################################### ## mapthis <- orderMarkers(mapthis, chr=5) ################################################### ### code chunk number 45: orderchrfiverun ################################################### file <- "Rcache/order5.RData" if(file.exists(file)) { load(file) } else { mapthis <- orderMarkers(mapthis, chr=5) save(mapthis, file=file) } ################################################### ### code chunk number 46: chrfivemap ################################################### pull.map(mapthis, chr=5) ################################################### ### code chunk number 47: ripplechr5run ################################################### file <- "Rcache/rip5.RData" if(file.exists(file)) { load(file) } else { rip5 <- ripple(mapthis, chr=5, window=7) save(rip5, file=file) } ################################################### ### code chunk number 48: ripplechr5 (eval = FALSE) ################################################### ## rip5 <- ripple(mapthis, chr=5, window=7) ################################################### ### code chunk number 49: summaryripple5 ################################################### summary(rip5) ################################################### ### code chunk number 50: ripplechr5likrun ################################################### file <- "Rcache/rip5lik.RData" if(file.exists(file)) { load(file) } else { rip5lik <- ripple(mapthis, chr=5, window=4, method="likelihood", error.prob=0.005) save(rip5lik, file=file) } ################################################### ### code chunk number 51: ripplechr5lik (eval = FALSE) ################################################### ## rip5lik <- ripple(mapthis, chr=5, window=4, method="likelihood", ## error.prob=0.005) ################################################### ### code chunk number 52: summaryripple5lik ################################################### summary(rip5lik) ################################################### ### code chunk number 53: compareorder ################################################### compareorder(mapthis, chr=5, c(1:7,9,8), error.prob=0.01) compareorder(mapthis, chr=5, c(1:7,9,8), error.prob=0.001) compareorder(mapthis, chr=5, c(1:7,9,8), error.prob=0) ################################################### ### code chunk number 54: switchorder ################################################### mapthis <- switch.order(mapthis, chr=5, c(1:7,9,8), error.prob=0.005) pull.map(mapthis, chr=5) ################################################### ### code chunk number 55: orderchrfour (eval = FALSE) ################################################### ## mapthis <- orderMarkers(mapthis, chr=4) ## pull.map(mapthis, chr=4) ################################################### ### code chunk number 56: orderchrfourrun ################################################### file <- "Rcache/order4.RData" if(file.exists(file)) { load(file) } else { mapthis <- orderMarkers(mapthis, chr=4) pull.map(mapthis, chr=4) save(mapthis, file=file) } pull.map(mapthis, chr=4) ################################################### ### code chunk number 57: ripplechr4run ################################################### file <- "Rcache/rip4.RData" if(file.exists(file)) { load(file) } else { rip4 <- ripple(mapthis, chr=4, window=7) save(rip4, file=file) } ################################################### ### code chunk number 58: ripplechr4 (eval = FALSE) ################################################### ## rip4 <- ripple(mapthis, chr=4, window=7) ################################################### ### code chunk number 59: summaryripple4 ################################################### summary(rip4) ################################################### ### code chunk number 60: ripplechr4likrun ################################################### file <- "Rcache/rip4lik.RData" if(file.exists(file)) { load(file) } else { rip4lik <- ripple(mapthis, chr=4, window=4, method="likelihood", error.prob=0.005) save(rip4lik, file=file) } ################################################### ### code chunk number 61: ripplechr4lik (eval = FALSE) ################################################### ## rip4lik <- ripple(mapthis, chr=4, window=4, method="likelihood", ## error.prob=0.005) ################################################### ### code chunk number 62: summaryripple4lik ################################################### summary(rip4lik) ################################################### ### code chunk number 63: switchmarkers4 ################################################### mapthis <- switch.order(mapthis, chr=4, c(1:8,10,9), error.prob=0.005) pull.map(mapthis, chr=4) ################################################### ### code chunk number 64: orderchrthree (eval = FALSE) ################################################### ## mapthis <- orderMarkers(mapthis, chr=3) ## pull.map(mapthis, chr=3) ################################################### ### code chunk number 65: orderchrthreerun ################################################### file <- "Rcache/order3.RData" if(file.exists(file)) { load(file) } else { mapthis <- orderMarkers(mapthis, chr=3) pull.map(mapthis, chr=3) save(mapthis, file=file) } pull.map(mapthis, chr=3) ################################################### ### code chunk number 66: ripplechr3run ################################################### file <- "Rcache/rip3.RData" if(file.exists(file)) { load(file) } else { rip3 <- ripple(mapthis, chr=3, window=7) save(rip3, file=file) } ################################################### ### code chunk number 67: ripplechr3 (eval = FALSE) ################################################### ## rip3 <- ripple(mapthis, chr=3, window=7) ################################################### ### code chunk number 68: summaryripple3 ################################################### summary(rip3) ################################################### ### code chunk number 69: ripplechr3likrun ################################################### file <- "Rcache/rip3lik.RData" if(file.exists(file)) { load(file) } else { rip3lik <- ripple(mapthis, chr=3, window=4, method="likelihood", error.prob=0.005) save(rip3lik, file=file) } ################################################### ### code chunk number 70: ripplechr3lik (eval = FALSE) ################################################### ## rip3lik <- ripple(mapthis, chr=3, window=4, method="likelihood", ## error.prob=0.005) ################################################### ### code chunk number 71: summaryripple3lik ################################################### summary(rip3lik) ################################################### ### code chunk number 72: orderchrtwo (eval = FALSE) ################################################### ## mapthis <- orderMarkers(mapthis, chr=2) ## pull.map(mapthis, chr=2) ################################################### ### code chunk number 73: orderchrtworun ################################################### file <- "Rcache/order2.RData" if(file.exists(file)) { load(file) } else { mapthis <- orderMarkers(mapthis, chr=2) pull.map(mapthis, chr=2) save(mapthis, file=file) } pull.map(mapthis, chr=2) ################################################### ### code chunk number 74: ripplechr2run ################################################### file <- "Rcache/rip2.RData" if(file.exists(file)) { load(file) } else { rip2 <- ripple(mapthis, chr=2, window=7) save(rip2, file=file) } ################################################### ### code chunk number 75: ripplechr2 (eval = FALSE) ################################################### ## rip2 <- ripple(mapthis, chr=2, window=7) ################################################### ### code chunk number 76: summaryripple2 ################################################### summary(rip2) ################################################### ### code chunk number 77: ripplechr2likrun ################################################### file <- "Rcache/rip2lik.RData" if(file.exists(file)) { load(file) } else { rip2lik <- ripple(mapthis, chr=2, window=4, method="likelihood", error.prob=0.005) save(rip2lik, file=file) } ################################################### ### code chunk number 78: ripplechr2lik (eval = FALSE) ################################################### ## rip2lik <- ripple(mapthis, chr=2, window=4, method="likelihood", ## error.prob=0.005) ################################################### ### code chunk number 79: summaryripple2lik ################################################### summary(rip2lik) ################################################### ### code chunk number 80: comparexo2lik (eval = FALSE) ################################################### ## pat2 <- apply(rip2[,1:24], 1, paste, collapse=":") ## pat2lik <- apply(rip2lik[,1:24], 1, paste, collapse=":") ## rip2 <- rip2[match(pat2lik, pat2),] ## plot(rip2[,"obligXO"], rip2lik[,"LOD"], xlab="obligate crossover count", ## ylab="LOD score") ################################################### ### code chunk number 81: comparexo2likplot ################################################### par(las=1, mar=c(4.1,4.1,1.1,0.1), cex=0.8) pat2 <- apply(rip2[,1:24], 1, paste, collapse=":") pat2lik <- apply(rip2lik[,1:24], 1, paste, collapse=":") rip2 <- rip2[match(pat2lik, pat2),] plot(rip2[,"obligXO"], rip2lik[,"LOD"], xlab="obligate crossover count", ylab="LOD score") ################################################### ### code chunk number 82: orderchrone (eval = FALSE) ################################################### ## mapthis <- orderMarkers(mapthis, chr=1) ## pull.map(mapthis, chr=1) ################################################### ### code chunk number 83: orderchronerun ################################################### file <- "Rcache/order1.RData" if(file.exists(file)) { load(file) } else { mapthis <- orderMarkers(mapthis, chr=1) pull.map(mapthis, chr=1) save(mapthis, file=file) } pull.map(mapthis, chr=1) ################################################### ### code chunk number 84: ripplechr1run ################################################### file <- "Rcache/rip1.RData" if(file.exists(file)) { load(file) } else { rip1 <- ripple(mapthis, chr=1, window=7) save(rip1, file=file) } ################################################### ### code chunk number 85: ripplechr1 (eval = FALSE) ################################################### ## rip1 <- ripple(mapthis, chr=1, window=7) ################################################### ### code chunk number 86: summaryripple1 ################################################### summary(rip1) ################################################### ### code chunk number 87: ripplechr1likrun ################################################### file <- "Rcache/rip1lik.RData" if(file.exists(file)) { load(file) } else { rip1lik <- ripple(mapthis, chr=1, window=4, method="likelihood", error.prob=0.005) save(rip1lik, file=file) } ################################################### ### code chunk number 88: ripplechr1lik (eval = FALSE) ################################################### ## rip1lik <- ripple(mapthis, chr=1, window=4, method="likelihood", ## error.prob=0.005) ################################################### ### code chunk number 89: summaryripple1lik ################################################### summary(rip1lik) ################################################### ### code chunk number 90: summarymap ################################################### summaryMap(mapthis) ################################################### ### code chunk number 91: savesummarymap ################################################### firstsummary <- summaryMap(mapthis) ################################################### ### code chunk number 92: plotmap (eval = FALSE) ################################################### ## plotMap(mapthis, show.marker.names=TRUE) ################################################### ### code chunk number 93: plotmapplot ################################################### par(las=1, mar=c(4.1,4.1,1.1,0.1), cex=0.8) plotMap(mapthis, main="", show.marker.names=TRUE) ################################################### ### code chunk number 94: plotrfonemoretime (eval = FALSE) ################################################### ## plotRF(mapthis) ################################################### ### code chunk number 95: plotrfonemoretimeplot ################################################### par(mar=c(4.1,4.1,1.6,1.6), las=1) plotRF(mapthis, main="") ################################################### ### code chunk number 96: plotrfafterreorder (eval = FALSE) ################################################### ## messedup <- switch.order(mapthis, chr=1, c(1:11,23:33,12:22), ## error.prob=0.005) ## plotRF(messedup, chr=1) ################################################### ### code chunk number 97: plotrfafterreorderplot ################################################### par(mar=c(4.1,4.1,1.6,1.6), las=1, pty="s", cex=0.8) messedup <- switch.order(mapthis, chr=1, c(1:11,23:33,12:22), error.prob=0.005) plotRF(messedup, chr=1, main="") ################################################### ### code chunk number 98: plotmapmessedup (eval = FALSE) ################################################### ## plotMap(messedup, show.marker.names=TRUE) ################################################### ### code chunk number 99: plotmapmessedupplot ################################################### par(las=1, mar=c(4.1,4.1,1.1,0.1), cex=0.8) plotMap(messedup, main="", show.marker.names=TRUE) ################################################### ### code chunk number 100: droponemarker (eval = FALSE) ################################################### ## dropone <- droponemarker(mapthis, error.prob=0.005) ################################################### ### code chunk number 101: droponemarkerrun ################################################### file <- "Rcache/dropone.RData" if(file.exists(file)) { load(file) } else { dropone <- droponemarker(mapthis, error.prob=0.005) save(dropone, file=file) } ################################################### ### code chunk number 102: plotdropone (eval = FALSE) ################################################### ## par(mfrow=c(2,1)) ## plot(dropone, lod=1, ylim=c(-100,0)) ## plot(dropone, lod=2, ylab="Change in chromosome length") ################################################### ### code chunk number 103: plotdroponeplot ################################################### par(mar=c(4.1,4.1,1.6,0.1), mfrow=c(2,1), cex=0.8) plot(dropone, lod=1, ylim=c(-100,0)) plot(dropone, lod=2, ylab="Change in chr length (cM)") ################################################### ### code chunk number 104: worstmarkers ################################################### summary(dropone, lod.column=2) ################################################### ### code chunk number 105: dropbadmarkers ################################################### badmar <- rownames(summary(dropone, lod.column=2))[1:3] mapthis <- drop.markers(mapthis, badmar) ################################################### ### code chunk number 106: reestimatemap ################################################### newmap <- est.map(mapthis, error.prob=0.005) mapthis <- replace.map(mapthis, newmap) summaryMap(mapthis) ################################################### ### code chunk number 107: savenewsummary ################################################### secondsummary <- summaryMap(mapthis) ################################################### ### code chunk number 108: countxo (eval = FALSE) ################################################### ## plot(countXO(mapthis), ylab="Number of crossovers") ################################################### ### code chunk number 109: countxoplot ################################################### par(mar=c(4.1,4.1,0.6,0.6), cex=0.8) plot(countXO(mapthis), ylab="Number of crossovers") thecounts <- countXO(mapthis) worst <- rev(sort(thecounts, decreasing=TRUE)[1:2]) ################################################### ### code chunk number 110: drophighxoind ################################################### mapthis <- subset(mapthis, ind=(countXO(mapthis) < 50)) ################################################### ### code chunk number 111: rip5again ################################################### summary(rip <- ripple(mapthis, chr=5, window=7)) summary(rip <- ripple(mapthis, chr=5, window=2, method="likelihood", error.prob=0.005)) ################################################### ### code chunk number 112: switchchr5again ################################################### mapthis <- switch.order(mapthis, chr=5, c(1:7,9,8), error.prob=0.005) pull.map(mapthis, chr=5) ################################################### ### code chunk number 113: reestmapagain ################################################### newmap <- est.map(mapthis, error.prob=0.005) mapthis <- replace.map(mapthis, newmap) summaryMap(mapthis) ################################################### ### code chunk number 114: savethirdsummary ################################################### thirdsummary <- summaryMap(mapthis) ################################################### ### code chunk number 115: studyerrorrate (eval = FALSE) ################################################### ## loglik <- err <- c(0.001, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02) ## for(i in seq(along=err)) { ## cat(i, "of", length(err), "\n") ## tempmap <- est.map(mapthis, error.prob=err[i]) ## loglik[i] <- sum(sapply(tempmap, attr, "loglik")) ## } ## lod <- (loglik - max(loglik))/log(10) ################################################### ### code chunk number 116: runstudyerrorrate ################################################### file <- "Rcache/errorrate.RData" if(file.exists(file)) { load(file) } else { loglik <- err <- c(0.001, 0.0025, 0.005, 0.0075, 0.01, 0.0125, 0.015, 0.0175, 0.02) for(i in seq(along=err)) { cat(i, "of", length(err), "\n") tempmap <- est.map(mapthis, error.prob=err[i]) loglik[i] <- sum(sapply(tempmap, attr, "loglik")) } lod <- (loglik - max(loglik))/log(10) save(err, lod, file=file) } ################################################### ### code chunk number 117: ploterrorratelik (eval = FALSE) ################################################### ## plot(err, lod, xlab="Genotyping error rate", xlim=c(0,0.02), ## ylab=expression(paste(log[10], " likelihood"))) ################################################### ### code chunk number 118: ploterrorratelikplot ################################################### par(mar=c(4.1,4.1,0.6,0.6), las=1) plot(err, lod, xlab="Genotyping error rate", xlim=c(0,0.02), ylab=expression(paste(log[10], " likelihood"))) ################################################### ### code chunk number 119: errorlod (eval = FALSE) ################################################### ## mapthis <- calc.errorlod(mapthis, error.prob=0.005) ################################################### ### code chunk number 120: runerrorlod ################################################### file <- "Rcache/errorlod.RData" if(file.exists(file)) { load(file) } else { mapthis <- calc.errorlod(mapthis, error.prob=0.005) save(mapthis, file=file) } ################################################### ### code chunk number 121: toperrorlod ################################################### print(toperr <- top.errorlod(mapthis, cutoff=6)) ################################################### ### code chunk number 122: plotgeno (eval = FALSE) ################################################### ## plotGeno(mapthis, chr=1, ind=toperr$id[toperr$chr==1], ## cutoff=6, include.xo=FALSE) ################################################### ### code chunk number 123: plotgenoplot ################################################### par(mar=c(4.1,4.1,0.6,0.6), las=1, cex.axis=0.9) plotGeno(mapthis, chr=1, ind=toperr$id[toperr$chr==1], main="", cex=0.8, include.xo=FALSE, cutoff=6) ################################################### ### code chunk number 124: dropgenotypes ################################################### mapthis.clean <- mapthis for(i in 1:nrow(toperr)) { chr <- toperr$chr[i] id <- toperr$id[i] mar <- toperr$marker[i] mapthis.clean$geno[[chr]]$data[mapthis$pheno$id==id, mar] <- NA } ################################################### ### code chunk number 125: segdis (eval = FALSE) ################################################### ## gt <- geno.table(mapthis, scanone.output=TRUE) ## par(mfrow=c(2,1)) ## plot(gt, ylab=expression(paste(-log[10], " P-value"))) ## plot(gt, lod=3:5, ylab="Genotype frequency") ## abline(h=c(0.25, 0.5), lty=2, col="gray") ################################################### ### code chunk number 126: plotsegdis ################################################### gt <- geno.table(mapthis, scanone.output=TRUE) par(mar=c(4.1,4.1,0.6,0.6), las=1, mfrow=c(2,1), cex=0.8) plot(gt, ylab=expression(paste(-log[10], " P-value"))) plot(gt, lod=3:5, ylab="Genotype frequency") abline(h=c(0.25, 0.5), lty=2, col="gray") ################################################### ### code chunk number 127: plotfinalmap (eval = FALSE) ################################################### ## plotMap(mapthis, show.marker.names=TRUE) ################################################### ### code chunk number 128: plotfinalmapplot ################################################### par(las=1, mar=c(4.6,4.6,0.6,0.6), cex=0.8) plotMap(mapthis, main="", show.marker.names=TRUE) qtl/inst/doc/new_multiqtl.pdf0000644000175100001440000056451612422233634016053 0ustar hornikusers%PDF-1.4 %Çì¢ 5 0 obj <> stream xœÕ]YÇ‘~'„ý =õ,Øå¼~1VÆÚÖîZÂ,ü`îÃ’Zq†Ô4iJþõ™UyUwI v±&+È8¾82ûç 1É ÿÉÿÿìöÑï~ð/èí…¼øËòôÓ£ŸÉü‘[_üá >ÒLÚ8uqõâ‘Ìͽ‚ÿºI˜pquûèï»ÿ¼“ˆÒxv7ð,µ³.ì>\îµ×“~÷ârM´ ÁÙÝ{lâŒRjw·>>{Ïʈ ÔîGì0(«Ù½ÁO• !æÖVx½;`ßj2Ê•}Ï­…Ž»û4¾€Ó¬|0f÷ „f÷víï5(]°uðèá6O*5a3y™f¢LÜÝ^j1Å b^bÓsZ"üA½ æÁ&½Ç)I«¥Û})')ö»+ü»Ð8ÞwiÚ[˜Žá¼ ¸œj÷|"ï4åhüNú Âÿ\ývAÊ)Z+iëÍdŒ‰Wß=ºú׿ïþ‚}(`·»¦ñ¬° ¯»Þk¥'äîoøè&ããî8 Õ"ZW´~³vrKÛb°FòÎï`ÝJ¨ v±?µóøfŠNîþxÔ<çhqÂD!¡Ó§H©¨àïy(\[ƒo‹™Sƒ_S¿†Pk Ñ} Ë# ‰dõB¤';¶ÂyYÚèÄJJ+"-K†Hlǧ£ 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qtl/inst/doc/bcsft.R0000644000175100001440000000525112566656320014054 0ustar hornikusers### R code from vignette source 'bcsft.Rnw' ################################################### ### code chunk number 1: bcsft.Rnw:70-74 ################################################### library(qtl) listeria.bc2s3<-read.cross(format="csv", file=system.file(file.path("sampledata", "listeria.csv"), package = "qtl"), BC.gen=2, F.gen=3) ################################################### ### code chunk number 2: bcsft.Rnw:78-80 ################################################### data(hyper) hyper3 <- convert2bcsft(hyper, BC.gen = 3) ################################################### ### code chunk number 3: bcsft.Rnw:88-92 ################################################### listeria.f2<-read.cross(format="csv", file=system.file(file.path("sampledata", "listeria.csv"), package = "qtl")) map.bc2s3 <- est.map(listeria.bc2s3) map.f2<-est.map(listeria.f2) ################################################### ### code chunk number 4: bcsft.Rnw:97-99 ################################################### map.bc1 <- est.map(hyper) map.bc3<-est.map(hyper3) ################################################### ### code chunk number 5: bcsft.Rnw:103-104 ################################################### plot(map.f2, map.bc2s3, label=FALSE, main="") ################################################### ### code chunk number 6: bcsft.Rnw:111-112 ################################################### plot(map.bc1, map.bc3, label=FALSE, main="") ################################################### ### code chunk number 7: bcsft.Rnw:122-130 ################################################### listeria.bc2s3<-replace.map(listeria.bc2s3, map.f2) listeria.f2<-replace.map(listeria.f2, map.f2) listeria.f2<-calc.genoprob(listeria.f2, step=1 ) one.f2<-scanone(listeria.f2, method="em",pheno.col=1) listeria.bc2s3<-calc.genoprob(listeria.bc2s3, step=1 ) one.bc2s3<-scanone(listeria.bc2s3, method="em",pheno.col=1) ################################################### ### code chunk number 8: bcsft.Rnw:134-135 ################################################### plot(one.f2, one.bc2s3, col=c("red", "purple")) ################################################### ### code chunk number 9: bcsft.Rnw:141-149 ################################################### hyper3<-replace.map(hyper3, map.bc1) hyper<-replace.map(hyper, map.bc1) hyper<-calc.genoprob(hyper, step=1 ) one.hyp<-scanone(hyper, method="em",pheno.col=1) hyper3<-calc.genoprob(hyper3, step=1 ) one.hyp3<-scanone(hyper3, method="em",pheno.col=1) ################################################### ### code chunk number 10: bcsft.Rnw:153-154 ################################################### plot(one.hyp, one.hyp3, col=c("red", "purple")) qtl/inst/doc/MQM-tour.pdf0000644000175100001440000264042612423516626014753 0ustar hornikusers%PDF-1.5 %äðíø 7 0 obj <> stream xÚEQMoà ½ïWøH¤A1 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endobj startxref 736137 %%EOF qtl/inst/doc/MQM-tour.R0000644000175100001440000004401712423521651014365 0ustar hornikusers### R code from vignette source 'MQM-tour.Rnw' ################################################### ### code chunk number 1: setseed ################################################### set.seed(19696527) ################################################### ### code chunk number 2: MQM-tour.Rnw:144-147 ################################################### library(qtl) data(map10) simcross <- sim.cross(map10, type="f2", n.ind=100, missing.prob=0.02) ################################################### ### code chunk number 3: missingdata (eval = FALSE) ################################################### ## geno.image(simcross) ################################################### ### code chunk number 4: missingdataplot ################################################### geno.image(simcross) ################################################### ### code chunk number 5: MQM-tour.Rnw:221-223 ################################################### # displays warning because MQM ignores the X chromosome in an F2 augmentedcross <- mqmaugment(simcross, minprob=1.0) ################################################### ### code chunk number 6: augment1 (eval = FALSE) ################################################### ## geno.image(augmentedcross) ################################################### ### code chunk number 7: augment1plot ################################################### geno.image(augmentedcross) ################################################### ### code chunk number 8: MQM-tour.Rnw:249-250 ################################################### augmentedcross <- mqmaugment(simcross, minprob=0.1) ################################################### ### code chunk number 9: augment2 (eval = FALSE) ################################################### ## geno.image(augmentedcross) ################################################### ### code chunk number 10: augment2plot ################################################### geno.image(augmentedcross) ################################################### ### code chunk number 11: augment3 ################################################### data(multitrait) msim5 <- simulatemissingdata(multitrait, 5) msim10 <- simulatemissingdata(multitrait, 10) msim80 <- simulatemissingdata(multitrait, 80) ################################################### ### code chunk number 12: augment4 ################################################### maug5 <- mqmaugment(msim5) maug10 <- mqmaugment(msim10, minprob=0.25) maug80 <- mqmaugment(msim80, minprob=0.80) ################################################### ### code chunk number 13: augmentMinProb ################################################### maug10minprob <- mqmaugment(msim10, minprob=0.001, verbose=TRUE) maug10minprobImpute <- mqmaugment(msim10, minprob=0.001, strategy="impute", verbose=TRUE) # check how many individuals are expanded: nind(maug10minprob) nind(maug10minprobImpute) ################################################### ### code chunk number 14: augment5 ################################################### mqm5 <- mqmscan(maug5) mqm10 <- mqmscan(maug10) mqm80 <- mqmscan(maug80) ################################################### ### code chunk number 15: augment5b ################################################### msim5 <- calc.genoprob(msim5) one5 <- scanone(msim5) msim10 <- calc.genoprob(msim10) one10 <- scanone(msim10) msim80 <- calc.genoprob(msim80) one80 <- scanone(msim80) ################################################### ### code chunk number 16: augment6 (eval = FALSE) ################################################### ## op <- par(mfrow = c(2,2)) ## plot(mqm5, mqm10, mqm80, col=c("green","blue","red"), main="MQM missing data") ## legend("topleft", c("MQM 5%","MQM 10%","MQM 80%"), col=c("green","blue","red"), lwd=1) ## plot(one5, mqm5, main="5% missing", col=c("black","green")) ## legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ## plot(one10, mqm10, main="10% missing", col=c("black","blue")) ## legend("topleft", c("scanone","MQM"), col=c("black","blue"), lwd=1) ## plot(one80, mqm80, main="80% missing", col=c("black","red")) ## legend("topleft", c("scanone","MQM"), col=c("black","red"), lwd=1) ################################################### ### code chunk number 17: MQM-tour.Rnw:353-354 ################################################### op <- par(mfrow = c(2,2)) plot(mqm5, mqm10, mqm80, col=c("green","blue","red"), main="MQM missing data") legend("topleft", c("MQM 5%","MQM 10%","MQM 80%"), col=c("green","blue","red"), lwd=1) plot(one5, mqm5, main="5% missing", col=c("black","green")) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) plot(one10, mqm10, main="10% missing", col=c("black","blue")) legend("topleft", c("scanone","MQM"), col=c("black","blue"), lwd=1) plot(one80, mqm80, main="80% missing", col=c("black","red")) legend("topleft", c("scanone","MQM"), col=c("black","red"), lwd=1) ################################################### ### code chunk number 18: MQM-tour.Rnw:379-382 ################################################### data(multitrait) maug_min1 <- mqmaugment(multitrait, minprob=1.0) mqm_min1 <- mqmscan(maug_min1) ################################################### ### code chunk number 19: MQM-tour.Rnw:389-391 ################################################### mgenop <- calc.genoprob(multitrait, step=5) m_one <- scanone(mgenop) ################################################### ### code chunk number 20: MQM-tour.Rnw:399-401 ################################################### maug <- mqmaugment(multitrait) mqm <- mqmscan(maug) ################################################### ### code chunk number 21: MinprobMulti ################################################### plot(m_one, mqm_min1, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ################################################### ### code chunk number 22: MQM-tour.Rnw:426-427 ################################################### real_markers <- mqmextractmarkers(mqm) ################################################### ### code chunk number 23: MQM-tour.Rnw:448-453 ################################################### max(mqm) find.marker(maug, chr=5, pos=35) multitoset <- find.markerindex(maug, "GH.117C") setcofactors <- mqmsetcofactors(maug, cofactors=multitoset) mqm_co1 <- mqmscan(maug, setcofactors) ################################################### ### code chunk number 24: Cofactor4multi (eval = FALSE) ################################################### ## par(mfrow = c(2,1)) ## plot(mqmgetmodel(mqm_co1)) ## plot(mqm_co1) ################################################### ### code chunk number 25: MQM-tour.Rnw:470-472 ################################################### # plot after adding first cofactor par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_co1)) plot(mqm_co1) ################################################### ### code chunk number 26: Cofactor4bMULTI (eval = FALSE) ################################################### ## plot(m_one, mqm_co1, col=c("black","green"), lty=1:2) ## legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ################################################### ### code chunk number 27: MQM-tour.Rnw:489-490 ################################################### plot(m_one, mqm_co1, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ################################################### ### code chunk number 28: MQM-tour.Rnw:510-514 ################################################### # summary(mqm_co1) multitoset <- c(multitoset, find.markerindex(maug, find.marker(maug,4,10))) setcofactors <- mqmsetcofactors(maug,cofactors=multitoset) mqm_co2 <- mqmscan(maug, setcofactors) ################################################### ### code chunk number 29: twowaycomparison (eval = FALSE) ################################################### ## par(mfrow = c(2,1)) ## plot(mqmgetmodel(mqm_co2)) ## plot(mqm_co1, mqm_co2, col=c("blue","green"), lty=1:2) ## legend("topleft", c("one cofactor","two cofactors"), col=c("blue","green"), ## lwd=1) ################################################### ### code chunk number 30: MQM-tour.Rnw:529-530 ################################################### par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_co2)) plot(mqm_co1, mqm_co2, col=c("blue","green"), lty=1:2) legend("topleft", c("one cofactor","two cofactors"), col=c("blue","green"), lwd=1) ################################################### ### code chunk number 31: threewaycomparisonmulti (eval = FALSE) ################################################### ## plot(mqm, mqm_co1, mqm_co2, col=c("green","red","blue"), lty=1:3) ## legend("topleft", c("no cofactors","one cofactor","two cofactors"), ## col=c("green","red","blue"), lwd=1) ################################################### ### code chunk number 32: MQM-tour.Rnw:549-551 ################################################### # plot closeup of threeway comparison plot(mqm, mqm_co1, mqm_co2, col=c("green","red","blue"), lty=1:3) legend("topleft", c("no cofactors","one cofactor","two cofactors"), col=c("green","red","blue"), lwd=1) ################################################### ### code chunk number 33: MQM-tour.Rnw:609-613 (eval = FALSE) ################################################### ## autocofactors <- mqmautocofactors(maug, 50) ## mqm_auto <- mqmscan(maug, autocofactors) ## setcofactors <- mqmsetcofactors(maug, 5) ## mqm_backw <- mqmscan(maug, setcofactors) ################################################### ### code chunk number 34: MQM-tour.Rnw:616-620 ################################################### autocofactors <- mqmautocofactors(maug, 50) mqm_auto <- mqmscan(maug, autocofactors) setcofactors <- mqmsetcofactors(maug, 5) mqm_backw <- mqmscan(maug, setcofactors) ################################################### ### code chunk number 35: ManualAutoStart (eval = FALSE) ################################################### ## par(mfrow = c(2,1)) ## mqmplot.cofactors(maug, autocofactors, justdots=TRUE) ## mqmplot.cofactors(maug, setcofactors, justdots=TRUE) ################################################### ### code chunk number 36: MQM-tour.Rnw:632-634 ################################################### # plot result of cofactor selection par(mfrow = c(2,1)) mqmplot.cofactors(maug, autocofactors, justdots=TRUE) mqmplot.cofactors(maug, setcofactors, justdots=TRUE) ################################################### ### code chunk number 37: ManualAuto (eval = FALSE) ################################################### ## par(mfrow = c(2,1)) ## plot(mqmgetmodel(mqm_backw)) ## plot(mqmgetmodel(mqm_auto)) ################################################### ### code chunk number 38: MQM-tour.Rnw:650-652 ################################################### # plot result of cofactor backward elimination par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_backw)) plot(mqmgetmodel(mqm_auto)) ################################################### ### code chunk number 39: Backward1multi (eval = FALSE) ################################################### ## par(mfrow = c(2,1)) ## plot(mqmgetmodel(mqm_backw)) ## plot(mqm_backw) ################################################### ### code chunk number 40: MQM-tour.Rnw:667-669 ################################################### # plot result of cofactor backward elimination par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_backw)) plot(mqm_backw) ################################################### ### code chunk number 41: Backward2 (eval = FALSE) ################################################### ## plot(m_one, mqm_backw, col=c("black","green"), lty=1:2) ## legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ################################################### ### code chunk number 42: Backward2multi (eval = FALSE) ################################################### ## plot(m_one, mqm_backw, col=c("black","green"), lty=1:2) ## legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ################################################### ### code chunk number 43: MQM-tour.Rnw:695-696 ################################################### plot(m_one, mqm_backw, col=c("black","green"), lty=1:2) legend("topleft", c("scanone","MQM"), col=c("black","green"), lwd=1) ################################################### ### code chunk number 44: FigLowAlpha (eval = FALSE) ################################################### ## mqm_backw_low <- mqmscan(maug, setcofactors, cofactor.significance=0.002) ## par(mfrow = c(2,1)) ## plot(mqmgetmodel(mqm_backw_low)) ## plot(mqm_backw,mqm_backw_low, col=c("blue","green"), lty=1:2) ## legend("topleft", c("Significance=0.02","Significance=0.002"), ## col=c("blue","green"), lwd=1) ################################################### ### code chunk number 45: MQM-tour.Rnw:733-734 ################################################### mqm_backw_low <- mqmscan(maug, setcofactors, cofactor.significance=0.002) par(mfrow = c(2,1)) plot(mqmgetmodel(mqm_backw_low)) plot(mqm_backw,mqm_backw_low, col=c("blue","green"), lty=1:2) legend("topleft", c("Significance=0.02","Significance=0.002"), col=c("blue","green"), lwd=1) ################################################### ### code chunk number 46: AutoCofactor (eval = FALSE) ################################################### ## mqmplot.singletrait(mqm_backw_low, extended=TRUE) ################################################### ### code chunk number 47: MQM-tour.Rnw:763-764 ################################################### mqmplot.singletrait(mqm_backw_low, extended=TRUE) ################################################### ### code chunk number 48: QTLeffects (eval = FALSE) ################################################### ## dirresults <- mqmplot.directedqtl(multitrait, mqm_backw_low) ################################################### ### code chunk number 49: MQM-tour.Rnw:797-798 ################################################### dirresults <- mqmplot.directedqtl(multitrait, mqm_backw_low) ################################################### ### code chunk number 50: MainEffectsD1 (eval = FALSE) ################################################### ## plotPXG(multitrait, marker="GH.117C") ################################################### ### code chunk number 51: MQM-tour.Rnw:816-817 ################################################### plotPXG(multitrait, marker="GH.117C") ################################################### ### code chunk number 52: epistatic1 (eval = FALSE) ################################################### ## effectplot(multitrait, mname1="GH.117C", mname2="GA1") ################################################### ### code chunk number 53: MQM-tour.Rnw:838-839 ################################################### effectplot(multitrait, mname1="GH.117C", mname2="GA1") ################################################### ### code chunk number 54: epistatic2 (eval = FALSE) ################################################### ## effectplot(multitrait, mname1="PVV4", mname2="GH.117C") ################################################### ### code chunk number 55: MQM-tour.Rnw:865-866 ################################################### effectplot(multitrait, mname1="PVV4", mname2="GH.117C") ################################################### ### code chunk number 56: MQM-tour.Rnw:909-912 ################################################### require(snow) results <- mqmpermutation(maug, scanfunction=mqmscan, cofactors=setcofactors, n.cluster=2, n.perm=25, batchsize=25) ################################################### ### code chunk number 57: MQM-tour.Rnw:915-916 ################################################### mqmplot.permutations(results) ################################################### ### code chunk number 58: MQM-tour.Rnw:927-929 ################################################### resultsrqtl <- mqmprocesspermutation(results) summary(resultsrqtl) ################################################### ### code chunk number 59: MQM-tour.Rnw:950-953 ################################################### data(multitrait) m_imp <- fill.geno(multitrait) mqmscanfdr(m_imp, mqmscanall, cofactors=setcofactors, n.cluster=2) ################################################### ### code chunk number 60: MQM-tour.Rnw:997-1000 ################################################### data(multitrait) m_imp <- fill.geno(multitrait) mqm_imp5 <- mqmscan(m_imp, pheno.col=1:5, n.cluster=2) ################################################### ### code chunk number 61: MQM-tour.Rnw:1003-1004 ################################################### mqmplot.multitrait(mqm_imp5, type="image") ################################################### ### code chunk number 62: MQM-tour.Rnw:1012-1015 ################################################### cofactorlist <- mqmsetcofactors(m_imp, 3) mqm_imp5 <- mqmscan(m_imp, pheno.col=1:5 , cofactors=cofactorlist, n.cluster=2) ################################################### ### code chunk number 63: MQM-tour.Rnw:1018-1019 ################################################### mqmplot.multitrait(mqm_imp5, type="image") ################################################### ### code chunk number 64: MQM-tour.Rnw:1029-1030 ################################################### mqmplot.multitrait(mqm_imp5, type="lines") ################################################### ### code chunk number 65: MQM-tour.Rnw:1050-1051 ################################################### mqmplot.circle(m_imp, mqm_imp5) ################################################### ### code chunk number 66: MQM-tour.Rnw:1060-1061 ################################################### mqmplot.circle(m_imp, mqm_imp5, highlight=2) ################################################### ### code chunk number 67: MQM-tour.Rnw:1083-1086 ################################################### data(locations) multiloc <- addloctocross(m_imp, locations) mqmplot.cistrans(mqm_imp5, multiloc, 5, FALSE, TRUE) ################################################### ### code chunk number 68: MQM-tour.Rnw:1099-1100 ################################################### mqmplot.circle(multiloc, mqm_imp5, highlight=2) qtl/inst/doc/new_summary_scanone.R0000644000175100001440000001256612422233634017024 0ustar hornikusers################################################### ### chunk number 1: ################################################### #line 38 "new_summary_scanone.Rnw" options(width=77) ################################################### ### chunk number 2: loaddata ################################################### #line 66 "new_summary_scanone.Rnw" library(qtl) data(fake.f2) ################################################### ### chunk number 3: loadresults ################################################### #line 71 "new_summary_scanone.Rnw" load("fakef2_results.RData") ################################################### ### chunk number 4: scanoneA eval=FALSE ################################################### ## #line 77 "new_summary_scanone.Rnw" ## fake.f2 <- calc.genoprob(fake.f2, step=2.5) ## out.f2 <- scanone(fake.f2, method="hk") ################################################### ### chunk number 5: summaryscanoneA ################################################### #line 85 "new_summary_scanone.Rnw" summary(out.f2, threshold=3, df=TRUE) ################################################### ### chunk number 6: scanonepermA eval=FALSE ################################################### ## #line 103 "new_summary_scanone.Rnw" ## operm1.f2 <- scanone(fake.f2, method="hk", n.perm=500, perm.Xsp=TRUE) ## operm2.f2 <- scanone(fake.f2, method="hk", n.perm=500, perm.Xsp=TRUE) ## operm.f2 <- c(operm1.f2, operm2.f2) ################################################### ### chunk number 7: summaryscanonepermA ################################################### #line 113 "new_summary_scanone.Rnw" summary(operm.f2, alpha=c(0.05, 0.20)) ################################################### ### chunk number 8: summaryscaononeB ################################################### #line 120 "new_summary_scanone.Rnw" summary(out.f2, perms=operm.f2, alpha=0.05, pvalues=TRUE) ################################################### ### chunk number 9: thestrata ################################################### #line 131 "new_summary_scanone.Rnw" sex <- fake.f2$pheno$sex pgm <- fake.f2$pheno$pgm strata <- sex + 2*pgm table(strata) ################################################### ### chunk number 10: scanonepermB eval=FALSE ################################################### ## #line 140 "new_summary_scanone.Rnw" ## operm1.f2strat <- scanone(fake.f2, method="hk", n.perm=250, ## perm.Xsp=TRUE, perm.strata=strata) ## operm2.f2strat <- scanone(fake.f2, method="hk", n.perm=250, ## perm.Xsp=TRUE, perm.strata=strata) ## operm3.f2strat <- scanone(fake.f2, method="hk", n.perm=250, ## perm.Xsp=TRUE, perm.strata=strata) ## operm4.f2strat <- scanone(fake.f2, method="hk", n.perm=250, ## perm.Xsp=TRUE, perm.strata=strata) ## operm.f2strat <- c(operm1.f2strat, operm2.f2strat, operm3.f2strat, ## operm4.f2strat) ################################################### ### chunk number 11: summaryscanonepermB ################################################### #line 154 "new_summary_scanone.Rnw" summary(operm.f2strat, alpha=c(0.05, 0.20)) ################################################### ### chunk number 12: fakebc ################################################### #line 162 "new_summary_scanone.Rnw" data(fake.bc) ################################################### ### chunk number 13: loaddataB ################################################### #line 166 "new_summary_scanone.Rnw" load("fakebc_results.RData") ################################################### ### chunk number 14: scanoneB eval=FALSE ################################################### ## #line 172 "new_summary_scanone.Rnw" ## fake.bc <- calc.genoprob(fake.bc, step=2.5) ## out.bc <- scanone(fake.bc, pheno.col=1:2, method="hk") ################################################### ### chunk number 15: summaryscanoneC ################################################### #line 181 "new_summary_scanone.Rnw" summary(out.bc, threshold=3) ################################################### ### chunk number 16: summaryscanoneD ################################################### #line 187 "new_summary_scanone.Rnw" summary(out.bc, threshold=3, lodcolumn=2) ################################################### ### chunk number 17: summaryscanoneE ################################################### #line 193 "new_summary_scanone.Rnw" summary(out.bc, threshold=3, format="allpheno") ################################################### ### chunk number 18: summaryscanoneF ################################################### #line 205 "new_summary_scanone.Rnw" summary(out.bc, threshold=c(3,2.5), format="allpeaks") ################################################### ### chunk number 19: scanonepermC eval=FALSE ################################################### ## #line 212 "new_summary_scanone.Rnw" ## operm.bc <- scanone(out.bc, pheno.col=1:2, method="hk", n.perm=1000) ################################################### ### chunk number 20: summaryscanonepermC ################################################### #line 217 "new_summary_scanone.Rnw" summary(operm.bc, alpha=0.05) ################################################### ### chunk number 21: summaryscanoneG ################################################### #line 223 "new_summary_scanone.Rnw" summary(out.bc, perms=operm.bc, alpha=0.05, format="allpeaks", pvalues=TRUE) qtl/inst/doc/rqtltour.R0000644000175100001440000002500312422233634014632 0ustar hornikusers############################################################## # R code for "A brief tour of R/qtl" # # Karl W Broman, kbroman@biostat.wisc.edu # University of Wisconsin Madison # # http://www.rqtl.org # # 21 March 2012 ############################################################## save.image() library(qtl) ls() help(read.cross) ?read.cross ############################################################ # Example 1: Hypertension ############################################################ data(hyper) ls() ?hyper summary(hyper) nind(hyper) nphe(hyper) nchr(hyper) totmar(hyper) nmar(hyper) plot(hyper) plotMissing(hyper) plotMap(hyper) plotPheno(hyper, pheno.col=1) plotMap(hyper, chr=c(1, 4, 6, 7, 15), show.marker.names=TRUE) plotMissing(hyper, reorder=TRUE) hyper <- drop.nullmarkers(hyper) totmar(hyper) hyper <- est.rf(hyper) plotRF(hyper) plotRF(hyper, chr=c(1,4)) plotRF(hyper, chr=6) plotMissing(hyper, chr=6) newmap <- est.map(hyper, error.prob=0.01) plotMap(hyper, newmap) hyper <- replace.map(hyper, newmap) hyper <- calc.errorlod(hyper, error.prob=0.01) top.errorlod(hyper) plotGeno(hyper, chr=16, ind=c(24:34, 71:81)) plotInfo(hyper) plotInfo(hyper, chr=c(1,4,15)) plotInfo(hyper, chr=c(1,4,15), method="entropy") plotInfo(hyper, chr=c(1,4,15), method="variance") hyper <- calc.genoprob(hyper, step=1, error.prob=0.01) out.em <- scanone(hyper) out.hk <- scanone(hyper, method="hk") hyper <- sim.geno(hyper, step=2, n.draws=16, error.prob=0.01) out.imp <- scanone(hyper, method="imp") summary(out.em) summary(out.em, threshold=3) summary(out.hk, threshold=3) summary(out.imp, threshold=3) max(out.em) max(out.hk) max(out.imp) plot(out.em, chr=c(1,4,15)) plot(out.em, out.hk, out.imp, chr=c(1,4,15)) plot(out.em, chr=c(1,4,15)) plot(out.hk, chr=c(1,4,15), col="blue", add=TRUE) plot(out.imp, chr=c(1,4,15), col="red", add=TRUE) operm.hk <- scanone(hyper, method="hk", n.perm=1000) summary(operm.hk, alpha=0.05) summary(out.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE) save.image() hyper <- calc.genoprob(hyper, step=5, error.prob=0.01) out2.hk <- scantwo(hyper, method="hk") summary(out2.hk, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6)) summary(out2.hk, thresholds=c(6.0, 4.7, Inf, 4.7, 2.6)) plot(out2.hk) plot(out2.hk, chr=c(1,4,6,15)) max(out2.hk) operm2.hk <- scantwo(hyper, method="hk", n.perm=100) summary(operm2.hk) summary(out2.hk, perms=operm2.hk, pvalues=TRUE, alphas=c(0.05, 0.05, 0, 0.05, 0.05)) chr <- c(1, 1, 4, 6, 15) pos <- c(50, 76, 30, 70, 20) qtl <- makeqtl(hyper, chr, pos) my.formula <- y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q4:Q5 out.fitqtl <- fitqtl(hyper, qtl=qtl, formula=my.formula) summary(out.fitqtl) ls() rm(list=ls()) ############################################################ # Example 2: Genetic mapping ############################################################ data(badorder) summary(badorder) plot(badorder) badorder <- est.rf(badorder) plotRF(badorder) plotRF(badorder, chr=1) newmap <- est.map(badorder, verbose=TRUE) plotMap(badorder, newmap) plotRF(badorder, chr=2:3) pull.map(badorder, chr=2) pull.map(badorder, chr=3) badorder <- movemarker(badorder, "D2M937", 3, 48) badorder <- movemarker(badorder, "D3M160", 2, 28.8) plotRF(badorder, chr=2:3) rip1 <- ripple(badorder, chr=1, window=6) summary(rip1) rip2 <- ripple(badorder, chr=1, window=3, err=0.01, method="likelihood") summary(rip2) badorder.rev <- switch.order(badorder, 1, rip1[2,]) rip1r <- ripple(badorder.rev, chr=1, window=6) summary(rip1r) badorder.rev <- switch.order(badorder.rev, 1, rip1r[2,]) rip2r <- ripple(badorder.rev, chr=1, window=3, err=0.01) summary(rip2r) badorder.rev <- est.rf(badorder.rev) plotRF(badorder.rev, 1) ############################################################ # Example 3: Listeria susceptibility ############################################################ data(listeria) summary(listeria) plot(listeria) plotMissing(listeria) listeria$pheno$logSurv <- log(listeria$pheno[,1]) plot(listeria) listeria <- est.rf(listeria) plotRF(listeria) plotRF(listeria, chr=c(5,13)) newmap <- est.map(listeria, error.prob=0.01) plotMap(listeria, newmap) listeria <- replace.map(listeria, newmap) listeria <- calc.errorlod(listeria, error.prob=0.01) top.errorlod(listeria) top.errorlod(listeria, cutoff=3.5) plotGeno(listeria, chr=13, ind=61:70, cutoff=3.5) listeria <- calc.genoprob(listeria, step=2) out.2p <- scanone(listeria, pheno.col=3, model="2part", upper=TRUE) summary(out.2p) summary(out.2p, threshold=4.5) summary(out.2p, format="allpeaks", threshold=3) summary(out.2p, format="allpeaks", threshold=c(4.5,3,3)) plot(out.2p) plot(out.2p, lodcolumn=2) plot(out.2p, lodcolumn=1:3, chr=c(1,5,13,15)) operm.2p <- scanone(listeria, model="2part", pheno.col=3, upper=TRUE, n.perm=25) summary(operm.2p, alpha=0.05) summary(out.2p, format="allpeaks", perms=operm.2p, alpha=0.05, pvalues=TRUE) y <- listeria$pheno$logSurv my <- max(y, na.rm=TRUE) z <- as.numeric(y==my) y[y==my] <- NA listeria$pheno$logSurv2 <- y listeria$pheno$binary <- z plot(listeria) out.mu <- scanone(listeria, pheno.col=4) plot(out.mu, out.2p, lodcolumn=c(1,3), chr=c(1,5,13,15), col=c("blue","red")) out.p <- scanone(listeria, pheno.col=5, model="binary") plot(out.p, out.2p, lodcolumn=c(1,2), chr=c(1,5,13,15), col=c("blue","red")) out.p.alt <- scanone(listeria, pheno.col=as.numeric(listeria$pheno$T264==264), model="binary") out.np1 <- scanone(listeria, model="np", ties.random=TRUE) out.np2 <- scanone(listeria, model="np", ties.random=FALSE) plot(out.np1, out.np2, col=c("blue","red")) plot(out.2p, out.np1, out.np2, chr=c(1,5,13,15)) ############################################################ # Example 4: Covariates in QTL mapping ############################################################ data(fake.bc) summary(fake.bc) plot(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2.5) out.nocovar <- scanone(fake.bc, pheno.col=1:2) sex <- fake.bc$pheno$sex out.acovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex) summary(out.nocovar, threshold=3, format="allpeaks") summary(out.acovar, threshold=3, format="allpeaks") plot(out.nocovar, out.acovar, chr=c(2, 5)) plot(out.nocovar, out.acovar, chr=c(2, 5), lodcolumn=2) out.icovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex, intcovar=sex) summary(out.icovar, threshold=3, format="allpeaks") plot(out.acovar, out.icovar, chr=c(2,5), col=c("blue", "red")) plot(out.acovar, out.icovar, chr=c(2,5), lodcolumn=2, col=c("blue", "red")) out.sexint <- out.icovar - out.acovar plot(out.sexint, lodcolumn=1:2, chr=c(2,5), col=c("green", "purple")) seed <- ceiling(runif(1, 0, 10^8)) set.seed(seed) operm.acovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex, method="hk", n.perm=100) set.seed(seed) operm.icovar <- scanone(fake.bc, pheno.col=1:2, addcovar=sex, intcovar=sex, method="hk", n.perm=100) operm.sexint <- operm.icovar - operm.acovar summary(operm.sexint, alpha=c(0.05, 0.20)) summary(out.sexint, perms=operm.sexint, alpha=0.1, format="allpeaks", pvalues=TRUE) ############################################################ # Example 5: Multiple QTL mapping ############################################################ rm(list=ls()) data(hyper) hyper <- sim.geno(hyper, step=2.5, n.draws=16, err=0.01) out1 <- scanone(hyper, method="imp") plot(out1) max(out1) find.marker(hyper, 4, 29.5) g <- pull.geno(hyper)[,"D4Mit164"] mean(is.na(g)) g <- pull.geno(fill.geno(hyper))[,"D4Mit164"] out1.c4 <- scanone(hyper, method="imp", addcovar=g) plot(out1, out1.c4, col=c("blue", "red")) plot(out1.c4 - out1, ylim=c(-3,3)) abline(h=0, lty=2, col="gray") out1.c4i <- scanone(hyper, method="imp", addcovar=g, intcovar=g) plot(out1.c4i - out1.c4) out2 <- scantwo(hyper, method="imp") summary(out2, thr=c(6.0, 4.7, Inf, 4.7, 2.6)) summary( subset(out2, chr=1) ) summary( subset(out2, chr=c(7,15)) ) plot(out2, chr=c(1,4,6,7,15)) plot(out2, chr=1, lower="cond-add") plot(out2, chr=c(6,15), lower="cond-int") plot(out2, chr=c(7,15), lower="cond-int") out2.c4 <- scantwo(hyper, method="imp", addcovar=g, chr=c(1,6,7,15)) summary(out2.c4, thr=c(6.0, 4.7, Inf, 4.7, 2.6)) summary( subset(out2.c4, chr=1) ) summary( subset(out2.c4, chr=c(7,15)) ) plot(out2.c4) plot(out2.c4, chr=1, lower="cond-int") plot(out2.c4, chr=c(6,15), lower="cond-int") plot(out2.c4, chr=c(7,15), lower="cond-int") out2sub <- subset(out2, chr=c(1,6,7,15)) plot(out2.c4 - out2sub, allow.neg=TRUE, lower="cond-int") qc <- c(1, 1, 4, 6, 15) qp <- c(43.3, 78.3, 30.0, 62.5, 18.0) qtl <- makeqtl(hyper, chr=qc, pos=qp) myformula <- y ~ Q1+Q2+Q3+Q4+Q5 + Q4:Q5 out.fq <- fitqtl(hyper, qtl=qtl, formula = myformula) summary(out.fq) out.fq <- fitqtl(hyper, qtl=qtl, formula = myformula, drop=FALSE, get.ests=TRUE) summary(out.fq) revqtl <- refineqtl(hyper, qtl=qtl, formula = myformula) revqtl plot(revqtl) out.fq2 <- fitqtl(hyper, qtl=revqtl, formula=myformula) summary(out.fq2) out1.c4r <- addqtl(hyper, qtl=revqtl, formula=y~Q3) plot(out1.c4, out1.c4r, col=c("blue", "red")) plot(out1.c4r - out1.c4, ylim=c(-1.7, 1.7)) abline(h=0, lty=2, col="gray") out2.c4r <- addpair(hyper, qtl=revqtl, formula=y~Q3, chr=c(1,6,7,15)) plot(out2.c4r - out2.c4, lower="cond-int", allow.neg=TRUE) out.1more <- addqtl(hyper, qtl=revqtl, formula=myformula) plot(out.1more) out.iw4 <- addqtl(hyper, qtl=revqtl, formula=y~Q1+Q2+Q3+Q4+Q5+Q4:Q5+Q6+Q5:Q6) plot(out.iw4) out.2more <- addpair(hyper, qtl=revqtl, formula=myformula, chr=c(2,5,7,15)) plot(out.2more, lower="cond-int") out.ai <- addint(hyper, qtl=revqtl, formula=myformula) out.ai qtl2 <- addtoqtl(hyper, revqtl, 7, 53.6) qtl2 qtl3 <- dropfromqtl(qtl2, index=2) qtl3 qtl4 <- replaceqtl(hyper, qtl3, index=1, chr=1, pos=50) qtl4 qtl5 <- reorderqtl(qtl4, c(1:3,5,4)) qtl5 stepout.a <- stepwiseqtl(hyper, additive.only=TRUE, max.qtl=6) stepout.a stepout.i <- stepwiseqtl(hyper, max.qtl=6) stepout.i ############################################################ # Example 6: Internal data structure ############################################################ data(fake.bc) class(fake.bc) names(fake.bc) fake.bc$pheno[1:10,] names(fake.bc$geno) sapply(fake.bc$geno, class) names(fake.bc$geno[[3]]) fake.bc$geno[[3]]$data[1:5,] fake.bc$geno[[3]]$map names(fake.bc$geno[[3]]) fake.bc <- calc.genoprob(fake.bc, step=10, err=0.01) names(fake.bc$geno[[3]]) fake.bc <- sim.geno(fake.bc, step=10, n.draws=8, err=0.01) names(fake.bc$geno[[3]]) fake.bc <- argmax.geno(fake.bc, step=10, err=0.01) names(fake.bc$geno[[3]]) fake.bc <- calc.errorlod(fake.bc, err=0.01) names(fake.bc$geno[[3]]) names(fake.bc) fake.bc <- est.rf(fake.bc) names(fake.bc) # end of rqtltour.R qtl/inst/doc/rqtltour.pdf0000644000175100001440000044645412567121773015235 0ustar hornikusers%PDF-1.5 %¿÷¢þ 1 0 obj << /Type /ObjStm /Length 4252 /Filter /FlateDecode /N 91 /First 781 >> stream xœÍ\ÛrÛF}߯ÀÛ&•20÷ËÖVªlËVœH¶";Ž­<Ð$aC‘ 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################################################### ## library(qtl) ## data(hyper) ################################################### ### code chunk number 3: loadresults ################################################### load("hyper_results.RData") ################################################### ### code chunk number 4: scantwo (eval = FALSE) ################################################### ## hyper <- calc.genoprob(hyper, step=2.5) ## out2 <- scantwo(hyper) ################################################### ### code chunk number 5: summaryscantwoA ################################################### summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6)) ################################################### ### code chunk number 6: summaryscantwoB ################################################### summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), what="full") summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), what="add") summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), what="int") ################################################### ### code chunk number 7: summaryscantwoC ################################################### summary(out2, allpairs=FALSE) ################################################### ### code chunk number 8: summaryscantwoD ################################################### summary(out2, thresholds=c(6.0, 4.7, 4.4, 4.7, 2.6), df=TRUE) ################################################### ### code chunk number 9: oldsummaryscantwo ################################################### summaryScantwoOld(out2, thresholds=c(6, 4, 4)) ################################################### ### code chunk number 10: scantwoperm (eval = FALSE) ################################################### ## operm2A <- scantwo(hyper, n.perm=200) ## operm2B <- scantwo(hyper, n.perm=200) ## operm2C <- scantwo(hyper, n.perm=200) ## operm2D <- scantwo(hyper, n.perm=200) ## operm2E <- scantwo(hyper, n.perm=200) ## operm2 <- c(operm2A, operm2B, operm2C, operm2D, operm2E) ################################################### ### code chunk number 11: summaryscantwoperm ################################################### summary(operm2, alpha=c(0.05,0.20)) ################################################### ### code chunk number 12: summaryscantwopermB ################################################### summary(out2, perms=operm2, alphas=rep(0.05, 5)) ################################################### ### code chunk number 13: summaryscantwopermC ################################################### summary(out2, perms=operm2, alphas=c(0.05, 0.05, 0, 0.05, 0.05)) ################################################### ### code chunk number 14: summaryscantwoD ################################################### summary(out2, perms=operm2, alphas=c(0.05, 0.05, 0, 0.05, 0.05), pvalues=TRUE) ################################################### ### code chunk number 15: plotscantwoA (eval = FALSE) ################################################### ## plot(out2, chr=c(1,4,6,15)) ################################################### ### code chunk number 16: plotscantwoAplot ################################################### plot(out2, chr=c(1,4,6,15),layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) ################################################### ### code chunk number 17: plotscantwoB (eval = FALSE) ################################################### ## plot(out2, chr=c(1,4,6,15), upper="cond-int") ################################################### ### code chunk number 18: plotscantwoBplot ################################################### plot(out2, chr=c(1,4,6,15), upper="cond-int", layout=list(cbind(1,2),c(5,1)), mar1=c(4,4,0,0)+0.1, mar2=c(4,2,0,2)+0.1) qtl/inst/doc/new_summary_scanone.pdf0000644000175100001440000010375112422233634017371 0ustar hornikusers%PDF-1.4 %Çì¢ 5 0 obj <> stream xœí\[o·~wû#„¼ä¨ˆ6¼_Ò¦€ƒ¤A›m]5ôÁ¶,©d):rRÿûÎ w—C.¹:²%'‚ÉŠ‡K9·o†ÃýaO 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SET (CMAKE_MODULE_PATH ${MAP_ROOT}/tools/cmake-support/modules) IF(NOT IS_DIRECTORY ${CMAKE_MODULE_PATH}) MESSAGE(FATAL_ERROR "Can not find BioLib context") ENDIF(NOT IS_DIRECTORY ${CMAKE_MODULE_PATH}) ENDIF(NOT BUILD_LIBS) FIND_PACKAGE(Map REQUIRED) FIND_PACKAGE(RLibs REQUIRED) # require some Rlib functionality at runtime INCLUDE_DIRECTORIES(../../src) # INCLUDE_DIRECTORIES(${R_INCLUDE_PATH}) BUILD_CLIB() ADD_LIBRARY(${LIBNAME} SHARED ../../src/countXO.c ../../src/countXO.h ../../src/discan.c ../../src/discan_covar.c ../../src/discan_covar.h ../../src/discan.h ../../src/effectscan.c ../../src/effectscan.h ../../src/findDupMarkers_notexact.c ../../src/findDupMarkers_notexact.h ../../src/fitqtl_hk.c ../../src/fitqtl_hk.h ../../src/fitqtl_imp.c ../../src/fitqtl_imp.h ../../src/forwsel.c ../../src/forwsel.h ../../src/hmm_4way.c ../../src/hmm_4way.h ../../src/hmm_bc.c ../../src/hmm_bc.h ../../src/hmm_bci.c ../../src/hmm_bci.h ../../src/hmm_f2.c ../../src/hmm_f2.h ../../src/hmm_f2i.c ../../src/hmm_f2i.h ../../src/hmm_main.c ../../src/hmm_main.h ../../src/hmm_ri4self.c ../../src/hmm_ri4self.h ../../src/hmm_ri4sib.c ../../src/hmm_ri4sib.h ../../src/hmm_ri8self.c ../../src/hmm_ri8self.h ../../src/hmm_ri8sib.c ../../src/hmm_ri8sib.h ../../src/info.c ../../src/info.h ../../src/lapackutil.c ../../src/lapackutil.h ../../src/mqmaugment.cpp ../../src/mqmaugment.h ../../src/mqmdatatypes.cpp ../../src/mqmdatatypes.h ../../src/mqmeliminate.cpp ../../src/mqmeliminate.h ../../src/mqmextra.cpp ../../src/mqmextra.h ../../src/mqm.h ../../src/mqmmapqtl.cpp ../../src/mqmmapqtl.h ../../src/mqmmixture.cpp ../../src/mqmmixture.h ../../src/mqmprob.cpp ../../src/mqmprob.h ../../src/mqmregression.cpp ../../src/mqmregression.h ../../src/mqmscan.cpp ../../src/mqmscan.h ../../src/ril48_reorg.c ../../src/ril48_reorg.h ../../src/ripple.c ../../src/ripple.h ../../src/scanone_ehk.c ../../src/scanone_ehk.h ../../src/scanone_em.c ../../src/scanone_em_covar.c ../../src/scanone_em_covar.h ../../src/scanone_em.h ../../src/scanone_hk.c ../../src/scanone_hk.h ../../src/scanone_imp.c ../../src/scanone_imp.h ../../src/scanone_mr.c ../../src/scanone_mr.h ../../src/scanone_np.c ../../src/scanone_np.h ../../src/scantwo_binary_em.c ../../src/scantwo_binary_em.h ../../src/scantwo_em.c ../../src/scantwo_em.h ../../src/scantwo_hk.c ../../src/scantwo_hk.h ../../src/scantwo_imp.c ../../src/scantwo_imp.h ../../src/scantwo_mr.c ../../src/scantwo_mr.h ../../src/simulate.c ../../src/simulate.h ../../src/simulate_ril.c ../../src/simulate_ril.h ../../src/stahl_mf.c ../../src/stahl_mf.h ../../src/standalone.h ../../src/summary_scantwo.c ../../src/summary_scantwo.h ../../src/util.c ../../src/util.h ../../src/vbscan.c ../../src/vbscan.h ) # ---- The following may be required for OSX # TARGET_LINK_LIBRARIES(${LIBNAME} ${ZLIB_NAME}) INSTALL_CLIB() qtl/inst/contrib/biolib/README0000644000175100001440000000032212422233634015623 0ustar hornikusersThis directory contains information for creating a shared library that can be linked against C and SWIG mappings for Perl, Ruby etc. See the BioLib project at http://biolib.open-bio.org/ for more information. qtl/inst/contrib/scripts/0000755000175100001440000000000012424512007015171 5ustar hornikusersqtl/inst/contrib/scripts/check_rqtl.sh0000755000175100001440000000065312422233634017657 0ustar hornikusers#! /bin/sh # # Usage: contrib/scripts/check_rqtl.sh path=$1 echo "* Run all R CMD check for R/qtl on $path" if [ ! -z $path -a -d $path ]; then cd $path fi echo -n "Using: " pwd if [ ! -d "contrib" ]; then echo "Incorrect path for R/qtl source" exit 1 fi cwd=`pwd` echo "* Run R CMD check" cd $cwd sh contrib/scripts/cleanup.sh R CMD check . if [ "$?" -ne "0" ]; then echo "Test 'R CMD check' failed" exit 1 fi qtl/inst/contrib/scripts/cleanup.sh0000755000175100001440000000176412422233634017173 0ustar hornikusers#! /bin/sh if [ ! -d R ] ; then echo Only run cleanup from base dir fi rm -rvf ..Rcheck/ rm inst/tests/junk* for x in cmake_install.cmake CTestTestfile.cmake CMakeCache.txt Makefile do rm -vf $x find . -name $x -exec rm -v \{\} \; done find . -name CMakeFiles -exec rm -rvf \{\} \; find . -name Testing -exec rm -rvf \{\} \; find . -name *.so -exec rm -v \{\} \; find . -name *.o -exec rm -v \{\} \; find . -name *.a -exec rm -v \{\} \; find . -name *dump -exec rm -v \{\} \; find . -name *.aux -exec rm -v \{\} \; find . -name *.dvi -exec rm -v \{\} \; find . -name *.log -exec rm -v \{\} \; find . -name *.orig -exec rm -v \{\} \; find . -name *.eps -exec rm -v \{\} \; find . -name *~ -exec rm -v \{\} \; find . -name Rplots.pdf -exec rm -v \{\} \; find . -name *.Rout -exec rm -v \{\} \; find src/ -name *.dll -exec rm -v \{\} \; rm inst/doc/Sources/MQM/MQM-tour.tex mv inst/doc/Sources/MQM/MQM-tour.R inst/doc/ mv inst/doc/Sources/MQM/MQM-tour.pdf inst/doc/ rm -rvf src-i386/ rm -rvf src-x86_64/ qtl/inst/contrib/scripts/repl_inputs.rb0000755000175100001440000000543312422233634020076 0ustar hornikusers#! /usr/bin/ruby # # This is a script to replace all \input{} statements in the Rd documentation # and copyright statements in file headers. This to prevent duplication of text # all over the place. In the future we may use the \Sexpr{} macro - from R 2.10.0. # # In principle a line like # # % \input{"inst/docs/mqm/limitations.txt"} # REPLACE THIS # } # # will inject the contents of that file - replacing the text until the next # closing curly brace. There are some other replacement commands that # merely replace LaTeX like macros on the next line # # % \mqmcopyright # default copyright # # Usage: # # ruby ./contrib/scripts/repl_inputs.rb inputfile(s) # # Example # # ruby ./contrib/scripts/repl_inputs.rb man/*.Rd # REPL = { # Keywords for C/C++ headers 'mqmcopyright1' => " * Copyright (c) 1998-2010, Ritsert C Jansen, Danny Arends, Pjotr Prins and Karl W Broman", 'license' => ' * Published under the terms of the GNU Lesser General Public License 3 (GPL3)', # Keywords for R man pages 'mqmauthors' => "Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman \\email{kbroman@biostat.wisc.edu}", 'dannyauthor' => "Danny Arends \\email{danny.arends@gmail.com}", 'dannyrutgerauthors' => "Danny Arends \\email{danny.arends@gmail.com} ; Rutger Brouwer", 'crossobject' => 'An object of class \code{cross}. See \code{\link{read.cross}} for details.', 'mqmscanobject' => 'An object returned by \code{mqmscan}, including cofactors and QTL model.', 'mqmcofactors' => 'List of cofactors to be analysed in the QTL model. To set cofactors use \code{\link{mqmautocofactors}} or \code{mqmsetcofactors}}.', 'phenocol' => 'Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers.', 'verbose' => 'Display more output on verbose=TRUE' } ARGV.each do | fn | raise "File not found #{fn}!" if !File.exist?(fn) print "\nParsing #{fn}..." buf = nil File.open(fn) { | f | buf = f.read } # parse buffer and strip between inputs outbuf = [] inside_input = false skipone = false buf.each do | s | if s.strip =~ /^%\s+\\input\{\"(\S+?)\"\}/ inputfn = $1 print "\nInjecting #{inputfn}" inside_input = true # inject inputfn raise "File not found #{inputfn}!" if !File.exist?(inputfn) outbuf.push s outbuf.push File.new(inputfn).read outbuf.push "% -----^^ "+inputfn.strip+" ^^-----\n" end # Now check keywords REPL.each do | k, v | if s.strip =~ /%\s+\\#{k}/ outbuf.push v + " % \\"+k+"\n" skipone = true next end end inside_input = false if s.strip == '}' outbuf.push s if !inside_input and !skipone skipone = false end File.open(fn,"w") do | f | f.print outbuf end end qtl/inst/contrib/scripts/run_all_tests.sh0000755000175100001440000000224112424512007020405 0ustar hornikusers#! /bin/sh # # Usage: inst/contrib/scripts/run_all_tests.sh . [options] # # Example: # # ./inst/contrib/scripts/run_all_tests.sh . --library=/my/libs path=$1 Roptions=$2 echo "* Run all functional/regression/unit/check tests for R/qtl on $path" if [ ! -z $path -a -d $path ]; then cd $path fi echo -n "Using: " pwd if [ ! -d "inst/contrib" ]; then echo "Incorrect path for R/qtl source" exit 1 fi cwd=`pwd` sh inst/contrib/scripts/cleanup.sh echo "* Run the standard MQM regression tests - without R install" cd $cwd cd inst/contrib/bin rm CMakeCache.txt cmake . make clean make make test if [ "$?" -ne "0" ]; then echo "Test 'standalone' failed" exit 1 fi echo "* Run R CMD check $Roptions $cwd" cd $cwd sh inst/contrib/scripts/cleanup.sh R CMD check $Roptions . if [ "$?" -ne "0" ]; then echo "Test 'R CMD check' failed" exit 1 fi echo "* Run the R regression tests - with R install from CMakeLists.txt" cd $cwd sh inst/contrib/scripts/cleanup.sh cd inst/contrib/bin rm CMakeCache.txt cmake -DTEST_R=TRUE . make clean make make testR if [ "$?" -ne "0" ]; then echo "Test 'R regression tests' failed" exit 1 fi echo "* Generate PDF's" echo "== skipped ==" qtl/inst/contrib/scripts/update_header.rb0000755000175100001440000001145412422233634020324 0ustar hornikusers#! /usr/bin/ruby # # This script updates the header of a source file # # by Pjotr Prins require 'parsedate' class GitLogEntry attr_reader :id, :author, :date, :comment def initialize buf raise "Git log problem "+buf.to_s if buf[0] !~ /^commit/ @id = buf[0] pos = 1 pos += 1 if buf[pos] =~ /^Merge:/ buf[pos] =~ /^Author: (.*) 0 @list.push GitLogEntry.new(buf) buf = [] end buf.push s end @list.push GitLogEntry.new(buf) end def modified @list.each do | commit | next if commit.comment.join =~ /header/i return ParseDate.parsedate(commit.date) end 'unknown' end def authors modifiedby = [] @list.each do | commit | next if commit.comment.join =~ /header/i modifiedby.push commit.author end modifiedby.uniq end end def git_info(source, mainauthor) gitlog = GitLog.new(source) modifiedby = [] return gitlog.modified, gitlog.authors end def file_R(buf, source) outbuf = [] # parse for modified by lastmodified, modifiedby = git_info(source,"anny") d = lastmodified t = Time.mktime(d[0],d[1],d[2]) # parse for methods methods = "" buf.each do | s | if s =~ /^(\S+)\s+<-/ methods += $1 + "\n# " end end inside_header = true buf.each do | s | if inside_header and s !~ /^#/ # now inject header outbuf.push < #include #include #include #include #include #include #include #include using namespace std; FILE* redirect_info; // Redirect output for testing bool checkfileexists(const char *filename) { ifstream myfile; bool exists; myfile.open(filename); exists = myfile.is_open(); myfile.close(); return exists; } double mytruncate(double n, double p = 3){ int sign = 0; if(n >= 0){ sign = 1; }else{ sign = -1; } double val = fabs((pow(10,p)) * n); val = floor(val); val /= pow(10,p); return (double) sign * val; } void unittest_pbeta(void){ for(int df1=1;df1<50;df1++){ for(int df2=1;df2 0.0001) fprintf(redirect_info,"df1:%d df2:%d hw:%f prob:%.5f\n",df1,df2,halfway,mytruncate(prob,5)); } } } } void unittest_dnorm(void){ for(double variance=1.0;variance < 100.0;variance += 0.5){ for(double residual=1.0;residual < variance;residual += 0.5){ double prob = dnorm(residual,0,sqrt(variance),0); if(prob > 0.00001) fprintf(redirect_info,"%f %f %f\n",variance,residual,mytruncate(prob,5)); } } } static struct option long_options[] = { {0, 0, 0, 0} }; int main(int argc, char** argv){ printf("testing external functions used in mqm\n"); int option_index = 0; char c; char* outputfile = NULL; bool pbetaflag= false; bool dnormflag= false; //Parsing of arguments while ((c = getopt_long(argc, argv, "dpo:",long_options, &option_index)) != -1) switch (c) { case 'p': pbetaflag = true; break; case 'd': dnormflag = true; break; case 'o': outputfile = optarg; printf("outputfile=%s\n",outputfile); break; default: break; } //Check the output file if (outputfile != NULL){ // Open outputstream if specified - using C type for redirection FILE *fout = stdout; if (outputfile){ fout = fopen(outputfile,"w"); redirect_info = fout; } if(pbetaflag) unittest_pbeta(); if(dnormflag) unittest_dnorm(); printf("done\n"); return 0; }else{ printf("No output file\n"); return 1; } } qtl/inst/contrib/bin/CMakeLists.txt0000644000175100001440000002530412424512007017016 0ustar hornikuserscmake_minimum_required(VERSION 2.6) IF (NOT CMAKE_MODULE_PATH) set(CMAKE_MODULE_PATH ".") ENDIF() SET(CMAKE_BUILD_TYPE Debug) IF(NOT WIN32) EXECUTE_PROCESS(COMMAND grep Version ../../../DESCRIPTION OUTPUT_STRIP_TRAILING_WHITESPACE OUTPUT_VARIABLE _VERSION) STRING(REGEX REPLACE "Version: " "" RQTL_VERSION "${_VERSION}") ELSE() # Cannot assume we have grep on Windows - should be replaced with # CMake logic (FIXME) SET(RQTL_VERSION 1.15-3) ENDIF() MESSAGE("RQTL_VERSION=${RQTL_VERSION}") IF ( CMAKE_BUILD_TYPE MATCHES Debug ) ADD_DEFINITIONS(-Wall -D_DEBUG) ENDIF ( CMAKE_BUILD_TYPE MATCHES Debug ) # CMake testing ENABLE_TESTING() ADD_CUSTOM_TARGET(quicktest COMMAND ctest -E "TestR") if(${CMAKE_SIZEOF_VOID_P} EQUAL 8) set(BITS64 TRUE) endif() # ---- The following MACRO is just to keep the FindRLibs.cmake happy MACRO(ASSERT_FOUNDMAP) SET(BUILD_LIBS TRUE) SET(BIOLIB_R_LIBRARY UNUSED) ENDMACRO() FIND_PACKAGE(RLibs) include_directories (../../../src) # Compiler options add_definitions(-DSTANDALONE) # ADD_DEFINITIONS(-pedantic) ADD_DEFINITIONS(-DENABLE_C99_MACROS) IF(NOT WIN32) add_definitions(-pg) set(LINK_FLAGS "-pg") ENDIF() link_libraries(${R_LIBRARY} ${R_BLAS_LIBRARY}) add_executable (mqm ../../../src/simulate.c ../../../src/simulate.h ../../../src/mqmaugment.cpp ../../../src/mqmeliminate.h ../../../src/mqmmapqtl.h ../../../src/mqmregression.cpp ../../../src/mqmaugment.h ../../../src/mqmmixture.cpp ../../../src/mqmregression.h ../../../src/mqmdatatypes.cpp ../../../src/mqmmixture.h ../../../src/mqmscan.cpp ../../../src/mqmdatatypes.h ../../../src/mqm.h ../../../src/mqmprob.cpp ../../../src/mqmscan.h ../../../src/mqmeliminate.cpp ../../../src/mqmmapqtl.cpp ../../../src/mqmprob.h ../../../src/standalone.h ../../../src/util.h ../../../src/util.c mqmmain.cpp ) add_executable (mqmdebugout mqmdebugout.cpp ) SET(RTEST scripts/r.sh) SET(RTESTOUTPUT rtest/regression) # Test R version (updating only the C libraries) ADD_CUSTOM_TARGET(testR_Clibs COMMAND ${R_EXECUTABLE} CMD build --no-docs ../../ COMMAND ${R_EXECUTABLE} CMD INSTALL --libs-only qtl_${RQTL_VERSION}.tar.gz COMMAND ${CMAKE_COMMAND} -E remove qtl_${RQTL_VERSION}.tar.gz COMMAND ctest -R TestR) # Test R version only ADD_CUSTOM_TARGET(testR COMMAND ${R_EXECUTABLE} CMD build --no-docs ../../ COMMAND ${R_EXECUTABLE} CMD INSTALL qtl_${RQTL_VERSION}.tar.gz COMMAND ${CMAKE_COMMAND} -E remove qtl_${RQTL_VERSION}.tar.gz COMMAND ctest -R TestR) # All tests, including R ADD_CUSTOM_TARGET(testall COMMAND ${R_EXECUTABLE} CMD build --no-docs ../../ COMMAND ${R_EXECUTABLE} CMD INSTALL qtl_${RQTL_VERSION}.tar.gz COMMAND ${CMAKE_COMMAND} -E remove qtl_${RQTL_VERSION}.tar.gz COMMAND ctest) IF(WIN32) SET(MQMEXE "mqm.exe") SET(MQMDEBUGOUT "mqmdebugout.exe") ELSE() SET(MQMEXE "./mqm") SET(MQMDEBUGOUT "./mqmdebugout") ENDIF() # Tracetest ADD_CUSTOM_TARGET(tracetest COMMAND ${MQMEXE} -o T12trace.log -d1 -ptest/std/phenotypes1.txt -gtest/std/genotypes1.txt -mtest/std/markers1.txt -stest/std/settings1.txt -ctest/t12/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 ) # debugout output ADD_TEST(debugout_dnorm ${MQMDEBUGOUT} -d -o test/regression/debugout_dnorm.new) ADD_TEST(debugout_dnorm_cmp ${CMAKE_COMMAND} -E compare_files test/regression/debugout_dnorm.new test/regression/debugout_dnorm.txt) ADD_TEST(debugout_pbeta ${MQMDEBUGOUT} -p -o test/regression/debugout_pbeta.new) ADD_TEST(debugout_pbeta_cmp ${CMAKE_COMMAND} -E compare_files test/regression/debugout_pbeta.new test/regression/debugout_pbeta.txt) # Older test for comparison with (older test) MQM_test0.txt # to only run this test use 'ctest -R test0' ADD_TEST(TestMQMF2_test0 ${MQMEXE} -v -ptest/std/phenotypes1.txt -gtest/std/genotypes1.txt -mtest/std/markers1.txt -stest/std/settings1.txt --smin=-20 --smax=220 --sstep=5 --alpha=0.05 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t11out-test0.txt.rnew) ADD_TEST(TestMQMF2_test0_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t11out-test0.txt.rnew test/regression/t11out-test0.txt) # Run T11 ADD_TEST(TestMQMF2_T11 ${MQMEXE} -v -ptest/std/phenotypes1.txt -gtest/std/genotypes1.txt -mtest/std/markers1.txt -stest/std/settings1.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t11out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T11_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t11out.txt.rnew test/regression/t11out.txt) # Run T12 Dataset 1 F2 ADD_TEST(TestMQMF2_T12 ${MQMEXE} -v -ptest/std/phenotypes1.txt -gtest/std/genotypes1.txt -mtest/std/markers1.txt -stest/std/settings1.txt -ctest/t12/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t12out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T12_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t12out.txt.rnew test/regression/t12out.txt) # Run T13 ADD_TEST(TestMQMF2_T13 ${MQMEXE} -v -ptest/std/phenotypes1.txt -gtest/std/genotypes1.txt -mtest/std/markers1.txt -stest/std/settings1.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=y --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t13out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T13_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t13out.txt.rnew test/regression/t13out.txt) # Run T21 Dataset 2 BC=~ Hyper (genotypes NA -> 0) ADD_TEST(TestMQMBC_T21 ${MQMEXE} -v -ptest/std/phenotypes2.txt -gtest/std/genotypes2.txt -mtest/std/markers2.txt -stest/std/settings2.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t21out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMBC_T21_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t21out.txt.rnew test/regression/t21out.txt) # Run T22 ADD_TEST(TestMQMBC_T22 ${MQMEXE} -v -ptest/std/phenotypes2.txt -gtest/std/genotypes2.txt -mtest/std/markers2.txt -stest/std/settings2.txt -ctest/t22/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t22out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMBC_T22_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t22out.txt.rnew test/regression/t22out.txt) # Run T23 #crash on est.map ADD_TEST(TestMQMBC_T23 ${MQMEXE} -v -ptest/std/phenotypes2.txt -gtest/std/genotypes2.txt -mtest/std/markers2.txt -stest/std/settings2.txt -ctest/t23/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=y --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t23out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMNC_T23_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t23out.txt.rnew test/regression/t23out.txt) # Run T24 #crash due to augmentation ADD_TEST(TestMQMBC_T24 ${MQMEXE} -v -ptest/std/phenotypes2.txt -gtest/std/genotypes2m.txt -mtest/std/markers2.txt -stest/std/settings2.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=1 -o test/regression/t24out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMBC_T24_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t24out.txt.rnew test/regression/t24out.txt) # Run T25 ADD_TEST(TestMQMBC_T25 ${MQMEXE} -v -ptest/std/phenotypes2.txt -gtest/std/genotypes2m.txt -mtest/std/markers2.txt -stest/std/settings2.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=0.5 -o test/regression/t25out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMBC_T25_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t25out.txt.rnew test/regression/t25out.txt) # Run T31 Dataset 3 F2=~ Listeria ADD_TEST(TestMQMF2_T31 ${MQMEXE} -v -ptest/std/phenotypes3.txt -gtest/std/genotypes3.txt -mtest/std/markers3.txt -stest/std/settings3.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=0.5 -o test/regression/t31out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T31_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t31out.txt.rnew test/regression/t31out.txt) # Run T32 #if(BITS64) # MESSAGE("Skip T32 on 64-bits") #else() ADD_TEST(TestMQMF2_T32 ${MQMEXE} -v -ptest/std/phenotypes3.txt -gtest/std/genotypes3m.txt -mtest/std/markers3.txt -stest/std/settings3.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=0.5 -o test/regression/t32out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T32_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t32out.txt.rnew test/regression/t32out.txt) #endif() # Run T33 ADD_TEST(TestMQMF2_T33 ${MQMEXE} -v -ptest/std/phenotypes3.txt -gtest/std/genotypes3.txt -mtest/std/markers3.txt -stest/std/settings3.txt -ctest/t33/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --minprob=0.5 -o test/regression/t33out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T33_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t33out.txt.rnew test/regression/t33out.txt) # Run T32 ADD_TEST(TestMQMF2_T34 ${MQMEXE} -v -ptest/std/phenotypes3.txt -gtest/std/genotypes3m.txt -mtest/std/markers3.txt -stest/std/settings3.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=120 --miaugment=1 --minprob=1 -o test/regression/t34out.txt.rnew) # Immediately compare outputs against older results ADD_TEST(TestMQMF2_T34_cmp ${CMAKE_COMMAND} -E compare_files test/regression/t34out.txt.rnew test/regression/t34out.txt) IF(TEST_R) ADD_TEST(TestR_io ${RTEST} ../../tests test_io.R) ADD_TEST(TestR_qtl ${RTEST} ../../tests test_qtl.R) ADD_TEST(TestR_scanone_mr ${RTEST} rtest test_scanone_mr.R) ADD_TEST(TestR_scanone_mr_cmp ${CMAKE_COMMAND} -E compare_files ${RTESTOUTPUT}/scanone_mr.rnew ${RTESTOUTPUT}/scanone_mr.rtest) ADD_TEST(TestR_mqm_listeria1 ${RTEST} rtest test_mqm_listeria1.R) if(NOT BITS64) ADD_TEST(TestR_mqm_listeria1_cmp ${CMAKE_COMMAND} -E compare_files ${RTESTOUTPUT}/mqm_listeria1.rnew ${RTESTOUTPUT}/mqm_listeria1.rtest) endif() ADD_TEST(TestR_augmentation ${RTEST} rtest test_augmentation.R) ENDIF() qtl/inst/contrib/bin/wincompile.bat0000644000175100001440000000022612422233634017114 0ustar hornikusers del CMakeCache.txt cmake -G "MinGW Makefiles" . c:\mingw\bin\mingw32-make.exe clean c:\mingw\bin\mingw32-make.exe c:\mingw\bin\mingw32-make.exe test qtl/inst/contrib/bin/scripts/0000755000175100001440000000000012422233634015745 5ustar hornikusersqtl/inst/contrib/bin/scripts/cleanup.sh0000755000175100001440000000065212422233634017736 0ustar hornikusers#! /bin/bash for x in cmake_install.cmake CTestTestfile.cmake CMakeCache.txt Makefile do rm -vf $x find . -name $x -exec rm -v \{\} \; done find . -name CMakeFiles -exec rm -rvf \{\} \; find . -name Testing -exec rm -rvf \{\} \; find . -name *.so -exec rm -v \{\} \; find . -name *.dll -exec rm -v \{\} \; find . -name *.o -exec rm -v \{\} \; find . -name *.a -exec rm -v \{\} \; find . -name *dump -exec rm -v \{\} \; qtl/inst/contrib/bin/scripts/profiler.sh0000755000175100001440000000015012422233634020122 0ustar hornikusers#! /bin/bash echo "Starting profiling" cd test gprof ../src/sMQM -- -T=0 -V echo "Finalized profiling" qtl/inst/contrib/bin/scripts/r.sh0000755000175100001440000000016312422233634016545 0ustar hornikusers#!/bin/sh echo Testing $1/$2 echo -n "Using: " which R cd $1 R --no-save --no-restore --no-readline --slave < $2 qtl/inst/contrib/bin/scripts/regression_tests.sh0000755000175100001440000000027412422233634021711 0ustar hornikusers#! /bin/bash echo "Starting regression tests" cd test ls -l # valgrind ../src/mqm -T=0 -V > ../test/MQM_test0.txt ../mqm -T=0 -V > MQM_test0.txt echo "Finalized regression tests" exit 0 qtl/inst/contrib/bin/scripts/create-diff.sh0000644000175100001440000000055212422233634020454 0ustar hornikusers#! /bin/bash # # Create a diff from another repostitory (this one should be standalone # and the other a master). if [ ! -d .git ]; then echo Should be in root of repo exit 1 fi git checkout standalone cd src ls --color=never *.cpp *.h > standalone.lst git checkout master cat standalone.lst | grep -v main | xargs git diff standalone > standalone.patch qtl/inst/contrib/bin/scripts/regression_tests_windows.bat0000644000175100001440000000032112422233634023605 0ustar hornikusersecho "Starting regression tests" cd test dir PATH=%PATH%;c:\Program Files\R\R-2.9.1\bin # valgrind ../src/mqm -T=0 -V > ../test/MQM_test0.txt ..\mqm -T=0 -V > MQM_test0.txt echo "Finalized regression tests" qtl/inst/contrib/bin/mqmmain.cpp0000644000175100001440000004432412424512007016424 0ustar hornikusers/********************************************************************** * * mqmmain.cpp * * Copyright (c) 1996-2009 by * Ritsert C Jansen, Danny Arends, Pjotr Prins and Karl W Broman * * initial MQM C code written between 1996-2002 by Ritsert C. Jansen * improved for the R-language by Danny Arends, Pjotr Prins and Karl W. Broman * * Modified by Pjotr Prins and Danny Arends * last modified December 2009 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * **********************************************************************/ #include #include #include #include #include #include #include "mqm.h" #include using namespace std; FILE *redirect_info = stdout; int debuglevel = 0; bool checkfileexists(const char *filename) { ifstream myfile; bool exists; myfile.open(filename); exists = myfile.is_open(); myfile.close(); return exists; } struct algorithmsettings { unsigned int nind; int nmark; unsigned int npheno; int stepmin; int stepmax; unsigned int stepsize; unsigned int windowsize; double alpha; unsigned int maxiter; char estmap; unsigned int max_totalaugment; // always >= 0 unsigned int max_indaugment; // always >= 0 double neglect_unlikely; char suggestedcross; }; struct markersinformation { ivector markerchr; vector markerdistance; ivector markerparent; }; struct algorithmsettings loadmqmsetting(const char* filename,const algorithmsettings commandline, bool verbose) { algorithmsettings runsettings=commandline; if (verbose) printf("INFO: Loading settings from file\n"); ifstream instream(filename, ios::in); instream >> runsettings.nind >> runsettings.nmark >> runsettings.npheno; //instream >> runsettings.stepmin >> runsettings.stepmax >> runsettings.stepsize; //instream >> runsettings.windowsize >> runsettings.alpha; //instream >> runsettings.maxiter >> runsettings.estmap; //instream >> runsettings.max_totalaugment >> runsettings.max_indaugment >> runsettings.neglect_unlikely; instream >> runsettings.suggestedcross; if (verbose) { Rprintf("number of individuals: %d\n",runsettings.nind); Rprintf("number of markers: %d\n",runsettings.nmark); Rprintf("number of phenotypes: %d\n",runsettings.npheno); //Rprintf("stepmin: %d\n",runsettings.stepmin); //Rprintf("stepmax: %d\n",runsettings.stepmax); //Rprintf("stepsize: %d\n",runsettings.stepsize); //Rprintf("windowsize for dropping qtls: %d\n",runsettings.windowsize); //Rprintf("Alpha level considered to be significant: %f\n",runsettings.alpha); //Rprintf("Max iterations using EM: %d\n",runsettings.maxiter); //Rprintf("Re-estimating map-positions: %c\n",runsettings.estmap); Rprintf("Suggested cross: %c\n",runsettings.suggestedcross); //Rprintf("Data-augmentation parameters: max:%d maxind:%d neglect:%d\n",runsettings.max_totalaugment,runsettings.max_indaugment,runsettings.neglect_unlikely); } return runsettings; } MQMMarkerMatrix readgenotype(const char* filename,const unsigned int nind,const unsigned int nmar,const bool verbose) { unsigned int j = 0; //current marker unsigned int i = 0; //current individual MQMMarkerMatrix genomarkers = newMQMMarkerMatrix(nmar,nind); ifstream myfstream(filename, ios::in); char c; while (!myfstream.eof() && i < nind) { if (j < nmar) { myfstream >> c; genomarkers[j][i] = (MQMMarker)c; j++; } else { j = 0; i++; } } if (verbose) Rprintf("Individuals: %d\n",i); myfstream.close(); return genomarkers; } matrix readphenotype(const char* filename,const unsigned int nind,const unsigned int nphe,const bool verbose) { unsigned int p = 0; // current phenotype unsigned int i = 0; //current individual matrix phenovalues = newmatrix(nphe,nind); ifstream myfstream(filename, ios::in); while (!myfstream.eof()) { if (p < nphe) { myfstream >> phenovalues[p][i]; p++; } else { p = 0; i++; } } if (verbose) Rprintf("Individuals: %d\n",i); myfstream.close(); return phenovalues; } struct markersinformation readmarkerfile(const char* filename,const unsigned int nmar,const bool verbose) { unsigned int j = 0; //current marker markersinformation info; ivector markerchr = newivector(nmar); vector markerdistance= newvector(nmar); // std::string markernames[nmar]; ivector markerparent = newivector(nmar); //Parental genotype ifstream myfstream(filename, ios::in); while (!myfstream.eof() && j < nmar) { myfstream >> markerchr[j]; std::string markername; myfstream >> markername; myfstream >> markerdistance[j]; markerparent[j] = 12; //if (verbose) Rprintf("Marker %d: %s %d %f\n",j,markernames[j].c_str(),markerchr[j],markerdistance[j]); j++; } if (verbose) Rprintf("Markers: %d\n",j); myfstream.close(); info.markerchr=markerchr; info.markerdistance=markerdistance; info.markerparent=markerparent; return info; } unsigned int readcofactorfile(const char* filename,cvector *cofactors,const unsigned int nmar,const bool verbose) { //Cofactor is pass by value if (checkfileexists(filename)) { unsigned int j = 0; //current marker unsigned int num = 0; //number of co-factors encountered ifstream myfstream(filename, ios::in); while (!myfstream.eof()) { myfstream >> (*cofactors)[j]; if ((*cofactors)[j]!='0') num++; j++; } myfstream.close(); if (verbose) Rprintf("Cofactors/Markers: %d/%d\n",num,j); return num; } else { // No silent failures!! Rprintf("File not found %s",filename); exit(1); } } void printhelp(void) { printf ("Commandline switches:\n"); printf ("-h This help.\n"); printf ("-v Verbose (produce a lot of textoutput).\n"); printf ("-d(INT) DebugLevel -d0,-d1.\n"); printf ("-t(INT) Phenotype under analysis.\n"); printf ("-p(FILE_NAME) Phenotypes file in plain textformat.\n"); printf ("-g(FILE_NAME) Genotypes file in plain textformat.\n"); printf ("-m(FILE_NAME) Marker and Chromosome descriptionfile in plain textformat.\n"); printf ("-s(FILE_NAME) Settings file in plain textformat.\n"); printf ("-c(FILE_NAME) Optional Cofactors file to do backward elimination on in plain textformat.\n"); printf ("-o(FILE_NAME) Optional output file to save MQM-QTL mapping results in.\n"); printf ("--smin(INT) Start of mapping in Cm.\n"); printf ("--smax(INT) End of mapping in Cm.\n"); printf ("--sstep(INT) Stepsize of mapping in Cm.\n"); printf ("--alpha(FLOAT) Significance level.\n"); printf ("--window(INT) Windowsize for dropping QTLs in Cm.\n"); printf ("--maxiter(INT) Maximum number of EM iterations.\n"); printf ("--estmap(CHAR) Reestimate marker positions y/n?.\n"); printf ("--maugment(INT) Maximum size of augmented dataset.\n"); printf ("--miaugment(INT) Maximum number of individual replications inside a dataset.\n"); printf ("--minprob(FLOAT) Drop genotypes more unlikely that minprob.\n"); } //Functions void exit_on_error(const char *msg) { info("EXIT ERROR: %s",msg); printhelp(); exit(1); } void exit_on_error_gracefull(const char *msg) { info("EXIT ERROR: %s",msg); printhelp(); exit(0); } bool selectivelygenotyped(const MQMMarkerMatrix markers,const ivector chr,const unsigned int nind, const unsigned int nmar){ int currentchr =0; int count = 0; int countmissing = 0; for(unsigned int i = 0;i < nind; i++){ for(unsigned int j = 0;j < nmar; j++){ if(chr[j] > currentchr && currentchr != 0){ if(count==countmissing){ return TRUE; }else{ count = 0; countmissing = 0; } currentchr = chr[j]; }else{ if(markers[j][i]==9){ countmissing++; } count++; } } count=0; countmissing=0; currentchr=0; } return FALSE; } static struct option long_options[] = { {"smin", required_argument, 0, 'a'}, {"smax", required_argument, 0, 'b'}, {"sstep", required_argument, 0, 'n'}, {"alpha", required_argument, 0, 'e'}, {"window", required_argument, 0, 'f'}, {"maxiter", required_argument, 0, 'q'}, {"estmap", required_argument, 0, 'i'}, {"maugment", required_argument, 0, 'j'}, {"miaugment", required_argument, 0, 'k'}, {"minprob", required_argument, 0, 'l'}, {0, 0, 0, 0} }; int main(int argc,char *argv[]) { printf("MQM standalone version\n"); bool verbose = false; bool helpflag = false; unsigned int phenotype = 0; //analyse the first phenotype char *phenofile = NULL; char *genofile = NULL; char *markerfile = NULL; char *coffile = NULL; char *settingsfile = NULL; char *outputfile = NULL; struct algorithmsettings mqmalgorithmsettings; struct markersinformation mqmmarkersinfo; int index; // aligned with argc and optind signed int c; int option_index = 0; //Parsing of arguments while ((c = getopt_long(argc, argv, "vd:h:p:g:m:c:s:t:o:a:b:e:f:q:i:j:k:l:",long_options, &option_index)) != -1) switch (c) { case 'v': verbose = true; break; case 'h': helpflag = true; break; case 'd': debuglevel = atoi(optarg); break; case 't': //1 phenotype at a time phenotype = atoi(optarg); break; case 'p': phenofile = optarg; break; case 'g': genofile = optarg; break; case 'm': markerfile = optarg; break; case 's': settingsfile = optarg; break; case 'c': coffile = optarg; break; case 'o': outputfile = optarg; break; case 'a': mqmalgorithmsettings.stepmin = atoi(optarg); debug_trace("Option (a) smin: %d\n",mqmalgorithmsettings.stepmin); case 'b': mqmalgorithmsettings.stepmax = atoi(optarg); debug_trace("Option (b) smax: %d\n",mqmalgorithmsettings.stepmax); break; case 'n': mqmalgorithmsettings.stepsize = atoi(optarg); debug_trace("Option (n) ssize: %d\n",mqmalgorithmsettings.stepsize); break; case 'e': mqmalgorithmsettings.alpha = atof(optarg); debug_trace("Option (e) alpha: %f\n",mqmalgorithmsettings.alpha); break; case 'f': mqmalgorithmsettings.windowsize = atoi(optarg); debug_trace("Option (f) window: %d\n",mqmalgorithmsettings.windowsize); break; case 'q': mqmalgorithmsettings.maxiter = atoi(optarg); debug_trace("Option (q) maxiter: %d\n",mqmalgorithmsettings.maxiter); break; case 'i': mqmalgorithmsettings.estmap = optarg[0]; debug_trace("Option (i) estmap: %d\n",mqmalgorithmsettings.estmap); break; case 'j': mqmalgorithmsettings.max_totalaugment = atoi(optarg); debug_trace("Option (j) max_totalaugment: %d\n",mqmalgorithmsettings.max_totalaugment); break; case 'k': mqmalgorithmsettings.max_indaugment = atoi(optarg); debug_trace("Option (k) max_indaugment: %d\n",mqmalgorithmsettings.max_indaugment); break; case 'l': mqmalgorithmsettings.neglect_unlikely = atof(optarg); debug_trace("Option (l) minprob: %f\n",mqmalgorithmsettings.neglect_unlikely); break; default: fprintf(stderr, "Unknown option character '%c'.\n", optopt); } if (helpflag) { printhelp(); return 0; } else { //Check the output file if (outputfile) debug_trace("Output file specified: %s\n",outputfile); if (checkfileexists(outputfile)) debug_trace("Outputfile exists\n !!! overwriting previous outputfile !!!\n"); // Open outputstream if specified - using C type for redirection FILE *fout = stdout; if (outputfile){ fout = fopen(outputfile,"w"); redirect_info = fout; } debug_trace ("Options for MQM:\n"); //Verbose & debug debug_trace ("verbose = %d, debuglevel = %d\n",verbose, debuglevel); //Needed files if (!phenofile) exit_on_error("Please supply a phenotypefile argument.\n"); if (!checkfileexists(phenofile)) exit_on_error("Phenotypefile not found on your filesystem.\n"); debug_trace ("Phenotypefile = %s\n",phenofile); if (!genofile) exit_on_error("Please supply a genofile argument.\n"); if (!checkfileexists(genofile)) exit_on_error("Genotypefile not found on your filesystem.\n"); debug_trace ("Genotypefile = %s\n",genofile); if (!markerfile) exit_on_error("Please supply a markerfile argument.\n"); if (!checkfileexists(genofile)) exit_on_error("Markerfile not found on your filesystem.\n"); debug_trace ("Markerfile = %s\n",markerfile); if (!settingsfile) exit_on_error("Please supply a settingsfile argument.\n"); if (!checkfileexists(settingsfile)) exit_on_error("settingsfile not found on your filesystem.\n"); debug_trace ("settingsfile = %s\n",settingsfile); //Optional files if (!coffile) { if (!checkfileexists(coffile)) { debug_trace("Cofactorfile not found on your filesystem.\n"); } else { debug_trace("Cofactorfile = %s\n",coffile); } } //Warn people for non-existing options for (index = optind; index < argc; index++) { debug_trace("Non-option argument %s\n", argv[index]); } //Read in settingsfile mqmalgorithmsettings = loadmqmsetting(settingsfile,mqmalgorithmsettings,verbose); //Create large datastructures double **QTL; ivector chr = newivector(mqmalgorithmsettings.nmark); cvector cofactor = newcvector(mqmalgorithmsettings.nmark); vector mapdistance = newvector(mqmalgorithmsettings.nmark); vector pos = newvector(mqmalgorithmsettings.nmark); matrix pheno_value = newmatrix(mqmalgorithmsettings.npheno,mqmalgorithmsettings.nind); MQMMarkerMatrix markers= newMQMMarkerMatrix(mqmalgorithmsettings.nmark,mqmalgorithmsettings.nind); ivector INDlist= newivector(mqmalgorithmsettings.nind); //Some additional variables int set_cofactors=0; //Markers set as cofactors int backwards=0; //Backward elimination ? MQMCrossType crosstype = CUNKNOWN; if(mqmalgorithmsettings.suggestedcross=='F'){ crosstype = CF2; //Crosstype } if(mqmalgorithmsettings.suggestedcross=='B'){ crosstype = CBC; //Crosstype } if(mqmalgorithmsettings.suggestedcross=='R'){ crosstype = CRIL; //Crosstype } //Here we know what we need so we can start reading in files with the new loader functions markers = readgenotype(genofile,mqmalgorithmsettings.nind,mqmalgorithmsettings.nmark,verbose); debug_trace("Genotypefile done\n"); pheno_value = readphenotype(phenofile,mqmalgorithmsettings.nind,mqmalgorithmsettings.npheno,verbose); debug_trace("Phenotypefile done \n"); mqmmarkersinfo = readmarkerfile(markerfile,mqmalgorithmsettings.nmark,verbose); chr = mqmmarkersinfo.markerchr; pos = mqmmarkersinfo.markerdistance; debug_trace("Markerposition file done\n"); //Determine how many chromosomes we have int max_chr=0; for (unsigned int m=0; m < (unsigned int) mqmalgorithmsettings.nmark; m++) { if (max_chr 0) { backwards = 1; } //Initialize an empty individuals list for (unsigned int i=0; i< mqmalgorithmsettings.nind; i++) { INDlist[i] = i; } int nind = mqmalgorithmsettings.nind; int augmentednind = mqmalgorithmsettings.nind; // if(mqmalgorithmsettings.max_totalaugment <= mqmalgorithmsettings.nind) exit_on_error_gracefull("Augmentation parameter conflict max_augmentation <= individuals"); //int testje = calculate_augmentation(mqmalgorithmsettings.nind,mqmalgorithmsettings.nmark,markers,crosstype); if(selectivelygenotyped(markers,chr,mqmalgorithmsettings.nind,mqmalgorithmsettings.nmark)){ fprintf(fout,"Warning: Selective genotyped set, only including most likely (neglect_unlikely set to 1)\n"); mqmalgorithmsettings.neglect_unlikely = 1; } mqmaugmentfull(&markers,&nind,&augmentednind,&INDlist,mqmalgorithmsettings.neglect_unlikely, mqmalgorithmsettings.max_totalaugment, mqmalgorithmsettings.max_indaugment,&pheno_value,mqmalgorithmsettings.nmark,chr,mapdistance,1,crosstype,verbose); // Start scanning for QTLs double logL = analyseF2(augmentednind, &mqmalgorithmsettings.nmark, &cofactor, (MQMMarkerMatrix)markers, pheno_value[phenotype], backwards,QTL, &mapdistance,&chr,0,0,mqmalgorithmsettings.windowsize, mqmalgorithmsettings.stepsize,mqmalgorithmsettings.stepmin,mqmalgorithmsettings.stepmax,mqmalgorithmsettings.alpha,mqmalgorithmsettings.maxiter,nind,&INDlist,mqmalgorithmsettings.estmap,crosstype,false,verbose); // Write final QTL profile (screen and file) if (!isinf(logL) && !isnan(logL)) { for (int q=0; q test/t11out.txt mqm.exe -v -pTest/std/phenotypes1.txt -gTest/std/genotypes1.txt -mTest/std/markers1.txt -sTest/std/settings1.txt -cTest/t12/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --neglect=1 > test/t12out.txt echo "Dataset 2 BC=~ Hyper (genotypes NA -> 0)" mqm.exe -v -pTest/std/phenotypes2.txt -gTest/std/genotypes2.txt -mTest/std/markers2.txt -sTest/std/settings2.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --neglect=1 > test/t21out.txt mqm.exe -v -pTest/std/phenotypes2.txt -gTest/std/genotypes2.txt -mTest/std/markers2.txt -sTest/std/settings2.txt -cTest/t22/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --neglect=1 > test/t22out.txt mqm.exe -v -pTest/std/phenotypes2.txt -gTest/std/genotypes2.txt -mTest/std/markers2.txt -sTest/std/settings2.txt -cTest/t23/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --neglect=1 > test/t23out.txt mqm.exe -v -pTest/std/phenotypes2.txt -gTest/std/genotypes2m.txt -mTest/std/markers2.txt -sTest/std/settings2.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=y --maugment=10000 --miaugment=250 --neglect=1 > test/t24out.txt mqm.exe -v -pTest/std/phenotypes2.txt -gTest/std/genotypes2m.txt -mTest/std/markers2.txt -sTest/std/settings2.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=y --maugment=10000 --miaugment=250 --neglect=1 > test/t25out.txt echo "Dataset 3 F2=~ Listeria" mqm.exe -v -pTest/std/phenotypes3.txt -gTest/std/genotypes3.txt -mTest/std/markers3.txt -sTest/std/settings3.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --neglect=1> test/t31out.txt mqm.exe -v -pTest/std/phenotypes3.txt -gTest/std/genotypes3.txt -mTest/std/markers3m.txt -sTest/std/settings3.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=y --maugment=10000 --miaugment=250 --neglect=1 > test/t32out.txt mqm.exe -v -pTest/std/phenotypes3.txt -gTest/std/genotypes3.txt -mTest/std/markers3.txt -sTest/std/settings3.txt -cTest/t33/cofactors.txt --smin=0 --smax=200 --sstep=2 --alpha=0.02 --window=10 --maxiter=1000 --estmap=n --maugment=10000 --miaugment=250 --neglect=1 > test/t33out.txt echo "Output done"qtl/inst/contrib/bin/test/0000755000175100001440000000000012422233634015235 5ustar hornikusersqtl/inst/contrib/bin/test/chridhyper.txt0000644000175100001440000000064212422233634020141 0ustar hornikusers1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 13 13 13 13 13 14 14 14 14 14 15 15 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 17 17 17 18 18 18 18 19 19 19 19 X X X Xqtl/inst/contrib/bin/test/filledgenohyper.txt0000644000175100001440000025172712422233634021174 0ustar hornikusers2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 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0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 9 0 9 0 9 9 0 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 0 9 0 9 1 1 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 1 1 9 9 1 9 9 1 9 1 9 1 1 9 9 1 9 9 9 9 9 0 0 9 9 9 9 0 9 9 9 9 9 1 9 9 9 1 9 9 1 1 9 1 9 9 9 9 1 1 1 9 1 9 1 9 9 9 9 9 9 9 0 9 0 9 9 9 9 1 9 1 9 0 9 9 0 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 1 1 1 1 9 1 9 9 1 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 1 9 1 9 1 1 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 1 1 9 9 1 9 9 1 0 0 9 0 0 9 9 0 9 9 9 9 9 0 1 9 9 9 9 0 9 9 9 9 9 0 9 9 9 0 9 9 0 0 9 0 9 9 9 9 0 0 0 9 0 9 0 9 9 9 9 9 9 9 0 9 0 9 9 9 9 1 9 1 9 1 9 1 1 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 9 1 9 1 9 1 9 9 1 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 1 9 1 9 1 1 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 1 1 9 9 1 9 9 1 9 1 9 1 1 9 9 1 9 9 9 9 9 0 0 9 9 9 9 0 9 9 9 9 9 0 0 0 0 0 0 0 0 0 9 0 1 1 1 1 1 1 1 9 0 9 0 9 9 9 9 9 9 9 1 9 1 9 9 9 9 0 9 0 9 0 9 9 0 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 0 9 0 9 0 9 9 0 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 0 9 0 9 0 0 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 1 1 9 9 1 9 9 1 1 0 9 0 0 9 9 0 9 9 9 9 9 0 0 9 9 9 9 1 9 9 9 9 9 0 1 1 1 1 9 9 1 1 9 1 9 9 9 9 1 1 1 9 1 9 0 9 9 9 9 9 9 9 0 0 1 9 9 9 9 0 9 0 9 0 9 9 0 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 0 9 0 9 0 9 9 0 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 1 0 0 9 0 0 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 0 0 9 9 0 9 9 0 9 0 9 0 0 9 9 0 9 9 9 9 9 1 1 9 9 9 9 1 9 9 9 9 9 0 9 9 9 0 9 9 0 0 9 0 9 9 9 9 0 0 0 9 1 9 0 9 9 9 9 9 9 9 0 9 0 9 9 9 9 1 9 0 9 1 9 1 0 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 9 1 9 9 1 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 1 1 1 9 1 1 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 0 0 9 9 0 9 9 0 9 0 9 0 0 9 9 0 9 9 9 9 9 0 0 9 9 9 9 1 9 9 9 9 9 1 9 9 9 1 9 9 1 1 9 1 9 9 9 9 1 1 1 9 1 9 0 9 9 9 9 9 9 9 1 9 1 9 9 9 9 1 9 1 9 1 9 9 1 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 1 1 1 9 1 9 9 1 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 0 9 0 9 0 0 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 0 0 9 9 0 9 9 0 9 0 9 0 0 0 0 1 9 9 9 9 9 1 1 9 9 9 9 0 9 9 9 9 9 1 9 9 9 1 9 9 1 1 9 1 9 9 9 9 1 1 1 9 0 9 0 9 9 9 9 9 9 9 1 9 1 9 9 9 9 0 9 0 9 0 9 9 0 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 0 9 0 9 0 9 9 0 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 0 9 0 9 0 0 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 9 1 1 9 9 1 9 9 0 9 0 9 0 0 9 9 0 9 9 9 9 9 0 0 9 9 9 9 1 9 9 9 9 9 1 9 9 9 1 0 0 0 0 9 0 9 9 9 9 0 1 1 9 0 0 1 9 9 9 9 9 9 9 1 9 1 9 9 9 9 0 9 0 9 0 9 9 0 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 0 9 0 9 0 9 9 0 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 1 9 1 9 1 1 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9qtl/inst/contrib/bin/test/t11/0000755000175100001440000000000012422233634015642 5ustar hornikusersqtl/inst/contrib/bin/test/t11/cofactors.txt0000644000175100001440000000152212422233634020366 0ustar hornikusers0 0 0 0 0 0 1 0 0 0 00 0 0 1 0 0 0 0 0 0 01 0 0 0 0 0 0 0 0 0 00 1 0 0 0 0 0 0 1 0 00 0 0 0 0 0 0 0 0 0 00 0 0 0 1 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 qtl/inst/contrib/bin/test/regression/0000755000175100001440000000000012566656321017430 5ustar hornikusersqtl/inst/contrib/bin/test/regression/t25out.txt0000644000175100001440000023476512422233634021341 0ustar hornikusersINFO: Augmentation routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:B: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=0.500000 INFO: Individual 92 moved to second augmentation round INFO: Individual 93 moved to second augmentation round INFO: Individual 94 moved to second augmentation round INFO: Individual 95 moved to second augmentation round INFO: Individual 96 moved to second augmentation round INFO: Individual 97 moved to second augmentation round INFO: Individual 98 moved to second augmentation round INFO: Individual 99 moved to second augmentation round INFO: Individual 100 moved to second augmentation round INFO: Individual 101 moved to second augmentation round INFO: Individual 102 moved to second augmentation round INFO: Individual 103 moved to second augmentation round INFO: Individual 105 moved to second augmentation round INFO: Individual 106 moved to second augmentation round INFO: Individual 108 moved to second augmentation round INFO: Individual 109 moved to second augmentation round INFO: Individual 110 moved to second augmentation round INFO: Individual 111 moved to second augmentation round INFO: Individual 112 moved to second augmentation round INFO: Individual 113 moved to second augmentation round INFO: Individual 114 moved to second augmentation round INFO: Individual 115 moved to second augmentation round INFO: Individual 116 moved to second augmentation round INFO: Individual 118 moved to second augmentation round INFO: Individual 119 moved to second augmentation round INFO: Individual 120 moved to second augmentation round INFO: Individual 121 moved to second augmentation round INFO: Individual 122 moved to second augmentation round INFO: Individual 123 moved to second augmentation round INFO: Individual 125 moved to second augmentation round INFO: Individual 126 moved to second augmentation round INFO: Individual 127 moved to second augmentation round INFO: Individual 128 moved to second augmentation round INFO: Individual 129 moved to second augmentation round INFO: Individual 130 moved to second augmentation round INFO: Individual 132 moved to second augmentation round INFO: Individual 133 moved to second augmentation round INFO: Individual 135 moved to second augmentation round INFO: Individual 140 moved to second augmentation round INFO: Individual 141 moved to second augmentation round INFO: Individual 142 moved to second augmentation round INFO: Individual 144 moved to second augmentation round INFO: Individual 146 moved to second augmentation round INFO: Individual 147 moved to second augmentation round INFO: Individual 148 moved to second augmentation round INFO: Individual 149 moved to second augmentation round INFO: Individual 150 moved to second augmentation round INFO: Individual 153 moved to second augmentation round INFO: Individual 154 moved to second augmentation round INFO: Individual 155 moved to second augmentation round INFO: Individual 156 moved to second augmentation round INFO: Individual 157 moved to second augmentation round INFO: Individual 159 moved to second augmentation round INFO: Individual 160 moved to second augmentation round INFO: Individual 162 moved to second augmentation round INFO: Individual 163 moved to second augmentation round INFO: Individual 166 moved to second augmentation round INFO: Individual 167 moved to second augmentation round INFO: Individual 168 moved to second augmentation round INFO: Individual 170 moved to second augmentation round INFO: Individual 171 moved to second augmentation round INFO: Individual 173 moved to second augmentation round INFO: Individual 174 moved to second augmentation round INFO: Individual 175 moved to second augmentation round INFO: Individual 176 moved to second augmentation round INFO: Individual 177 moved to second augmentation round INFO: Individual 178 moved to second augmentation round INFO: Individual 179 moved to second augmentation round INFO: Individual 180 moved to second augmentation round INFO: Individual 182 moved to second augmentation round INFO: Individual 184 moved to second augmentation round INFO: Individual 185 moved to second augmentation round INFO: Individual 186 moved to second augmentation round INFO: Individual 187 moved to second augmentation round INFO: Individual 188 moved to second augmentation round INFO: Individual 189 moved to second augmentation round INFO: Individual 190 moved to second augmentation round INFO: Individual 191 moved to second augmentation round INFO: Individual 192 moved to second augmentation round INFO: Individual 193 moved to second augmentation round INFO: Individual 194 moved to second augmentation round INFO: Individual 195 moved to second augmentation round INFO: Individual 196 moved to second augmentation round INFO: Individual 199 moved to second augmentation round INFO: Individual 200 moved to second augmentation round INFO: Individual 201 moved to second augmentation round INFO: Individual 202 moved to second augmentation round INFO: Individual 203 moved to second augmentation round INFO: Individual 205 moved to second augmentation round INFO: Individual 206 moved to second augmentation round INFO: Individual 208 moved to second augmentation round INFO: Individual 209 moved to second augmentation round INFO: Individual 210 moved to second augmentation round INFO: Individual 211 moved to second augmentation round INFO: Individual 212 moved to second augmentation round INFO: Individual 213 moved to second augmentation round INFO: Individual 216 moved to second augmentation round INFO: Individual 217 moved to second augmentation round INFO: Individual 218 moved to second augmentation round INFO: Individual 219 moved to second augmentation round INFO: Individual 220 moved to second augmentation round INFO: Individual 221 moved to second augmentation round INFO: Individual 222 moved to second augmentation round INFO: Individual 223 moved to second augmentation round INFO: Individual 224 moved to second augmentation round INFO: Individual 226 moved to second augmentation round INFO: Individual 227 moved to second augmentation round INFO: Individual 229 moved to second augmentation round INFO: Individual 230 moved to second augmentation round INFO: Individual 231 moved to second augmentation round INFO: Individual 233 moved to second augmentation round INFO: Individual 235 moved to second augmentation round INFO: Individual 236 moved to second augmentation round INFO: Individual 237 moved to second augmentation round INFO: Individual 239 moved to second augmentation round INFO: Individual 241 moved to second augmentation round INFO: Individual 243 moved to second augmentation round INFO: Individual 244 moved to second augmentation round INFO: Individual 246 moved to second augmentation round INFO: Individual 247 moved to second augmentation round INFO: Individual 248 moved to second augmentation round INFO: Individual 249 moved to second augmentation round INFO: Step 2: Unaugmented individuals INFO: Done with: 128/250 individuals still need to do 122 INFO: Crosstype determined by the algorithm:B: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=1.000000 INFO: Augmentation step 2 returned most likely for 122 individuals INFO: Done with augmentation INFO: Marker 6 at chr 1 is dropped INFO: Marker 15 at chr 1 is dropped INFO: Marker 16 at chr 1 is dropped INFO: Marker 17 at chr 1 is dropped INFO: Marker 42 at chr 4 is dropped INFO: Marker 48 at chr 4 is dropped INFO: Marker 105 at chr 11 is dropped INFO: Marker 107 at chr 11 is dropped INFO: Marker 111 at chr 11 is dropped INFO: Marker 133 at chr 15 is dropped INFO: Marker 137 at chr 15 is dropped INFO: Marker 139 at chr 15 is dropped INFO: Marker 148 at chr 16 is dropped INFO: Marker 150 at chr 17 is dropped INFO: Marker 151 at chr 17 is dropped INFO: Marker 154 at chr 17 is dropped INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.019 Alfa=0.020000 INFO: dimX:1 nInd:250 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,249)=0.020000 INFO: F(4.003,2,249)=0.020000 INFO: Log-likelihood of full model= -11907.215 INFO: Residual variance= 70.959 INFO: Trait mean= 101.611; Trait variation= 70.959 INFO: Number of output datapoints: 2020 0 0.551 1 0.551 2 0.559 3 0.577 4 0.587 5 0.588 6 0.579 7 0.560 8 0.532 9 0.497 10 0.487 11 0.664 12 0.861 13 1.066 14 1.264 15 1.446 16 1.605 17 1.668 18 2.221 19 2.900 20 2.988 21 3.141 22 3.206 23 3.298 24 3.378 25 3.286 26 2.843 27 2.233 28 2.115 29 2.193 30 2.205 31 2.153 32 2.047 33 2.712 34 3.265 35 3.137 36 3.288 37 3.117 38 3.147 39 3.238 40 3.069 41 2.690 42 2.218 43 1.582 44 1.464 45 1.433 46 1.387 47 1.322 48 1.266 49 1.200 50 1.122 51 1.035 52 0.941 53 0.843 54 0.743 55 0.644 56 0.550 57 0.462 58 0.390 59 0.389 60 0.388 61 0.388 62 0.387 63 0.386 64 0.385 65 0.384 66 0.383 67 0.381 68 0.380 69 0.378 70 0.376 71 0.374 72 0.372 73 0.369 74 0.367 75 0.364 76 0.360 77 0.357 78 0.353 79 0.349 80 0.344 81 0.340 82 0.334 83 0.329 84 0.323 85 0.317 86 0.311 87 0.305 88 0.298 89 0.291 90 0.284 91 0.277 92 0.269 93 0.262 94 0.254 95 0.247 96 0.239 97 0.232 98 0.224 99 0.216 100 0.209 101 0.207 102 0.207 103 0.208 104 0.208 105 0.218 106 0.287 107 0.356 108 0.416 109 0.461 110 0.498 111 0.557 112 0.612 113 0.662 114 0.703 115 0.735 116 0.776 117 0.869 118 0.962 119 1.049 120 1.126 121 1.189 122 1.234 123 1.259 124 1.262 125 1.246 126 1.364 127 1.568 128 1.490 129 1.453 130 1.470 131 1.448 132 1.386 133 1.291 134 1.171 135 0.996 136 0.798 137 0.608 138 0.431 139 0.278 140 0.154 141 0.066 142 0.015 143 0.000 144 0.005 145 0.006 146 0.007 147 0.008 148 0.009 149 0.010 150 0.011 151 0.011 152 0.011 153 0.011 154 0.011 155 0.011 156 0.011 157 0.011 158 0.011 159 0.011 160 0.011 161 0.011 162 0.011 163 0.011 164 0.011 165 0.011 166 0.011 167 0.011 168 0.011 169 0.011 170 0.011 171 0.011 172 0.011 173 0.011 174 0.011 175 0.011 176 0.011 177 0.011 178 0.011 179 0.011 180 0.010 181 0.010 182 0.010 183 0.010 184 0.010 185 0.010 186 0.010 187 0.010 188 0.010 189 0.010 190 0.010 191 0.010 192 0.009 193 0.009 194 0.009 195 0.009 196 0.009 197 0.009 198 0.008 199 0.008 200 0.008 201 0.008 202 0.063 203 0.063 204 0.020 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305 3.465 306 3.870 307 4.239 308 4.566 309 4.848 310 5.080 311 5.234 312 6.153 313 6.308 314 6.370 315 5.843 316 5.912 317 5.798 318 7.058 319 4.862 320 4.477 321 3.534 322 3.415 323 3.278 324 3.123 325 2.949 326 2.757 327 2.750 328 2.866 329 2.863 330 2.733 331 2.489 332 2.451 333 2.571 334 2.665 335 2.728 336 2.760 337 2.760 338 2.728 339 2.666 340 2.578 341 2.558 342 2.551 343 2.543 344 2.533 345 2.521 346 2.507 347 2.491 348 2.473 349 2.451 350 2.427 351 2.400 352 2.370 353 2.337 354 2.301 355 2.263 356 2.222 357 2.178 358 2.132 359 2.084 360 2.035 361 1.984 362 1.931 363 1.878 364 1.824 365 1.770 366 1.715 367 1.660 368 1.605 369 1.551 370 1.497 371 1.444 372 1.391 373 1.340 374 1.289 375 1.239 376 1.190 377 1.143 378 1.097 379 1.052 380 1.008 381 0.965 382 0.924 383 0.884 384 0.846 385 0.809 386 0.773 387 0.738 388 0.705 389 0.673 390 0.642 391 0.612 392 0.584 393 0.556 394 0.530 395 0.505 396 0.481 397 0.458 398 0.436 399 0.415 400 0.395 401 0.375 402 0.357 403 0.339 404 0.370 405 0.330 406 0.246 407 0.163 408 0.118 409 0.079 410 0.079 411 0.108 412 0.194 413 0.295 414 0.250 415 0.160 416 0.085 417 0.031 418 0.003 419 0.002 420 0.025 421 0.049 422 0.067 423 0.088 424 0.112 425 0.139 426 0.167 427 0.196 428 0.225 429 0.254 430 0.284 431 0.322 432 0.358 433 0.394 434 0.427 435 0.789 436 1.161 437 1.378 438 1.430 439 1.425 440 1.381 441 1.351 442 1.339 443 1.289 444 1.299 445 1.224 446 1.218 447 1.211 448 1.204 449 1.196 450 1.188 451 1.179 452 1.169 453 1.158 454 1.147 455 1.134 456 1.121 457 1.108 458 1.093 459 1.077 460 1.061 461 1.044 462 1.026 463 1.007 464 0.988 465 0.968 466 0.947 467 0.926 468 0.905 469 0.883 470 0.860 471 0.838 472 0.815 473 0.792 474 0.769 475 0.745 476 0.722 477 0.699 478 0.677 479 0.654 480 0.632 481 0.609 482 0.588 483 0.566 484 0.545 485 0.525 486 0.505 487 0.485 488 0.466 489 0.447 490 0.429 491 0.412 492 0.394 493 0.378 494 0.362 495 0.346 496 0.331 497 0.317 498 0.303 499 0.289 500 0.276 501 0.264 502 0.252 503 0.240 504 0.229 505 0.108 506 0.227 507 0.389 508 0.558 509 0.692 510 0.780 511 0.958 512 1.140 513 1.316 514 1.479 515 1.625 516 1.750 517 1.788 518 1.625 519 1.261 520 0.961 521 1.061 522 1.136 523 1.175 524 1.172 525 1.128 526 1.143 527 1.142 528 1.096 529 1.009 530 0.891 531 0.820 532 0.851 533 0.825 534 0.903 535 1.009 536 0.993 537 0.920 538 0.697 539 0.652 540 0.652 541 0.652 542 0.652 543 0.652 544 0.651 545 0.651 546 0.650 547 0.649 548 0.648 549 0.646 550 0.645 551 0.642 552 0.640 553 0.636 554 0.632 555 0.628 556 0.623 557 0.617 558 0.610 559 0.603 560 0.595 561 0.586 562 0.577 563 0.566 564 0.556 565 0.544 566 0.532 567 0.520 568 0.507 569 0.494 570 0.480 571 0.467 572 0.453 573 0.439 574 0.425 575 0.411 576 0.397 577 0.383 578 0.369 579 0.356 580 0.342 581 0.329 582 0.316 583 0.304 584 0.292 585 0.280 586 0.268 587 0.256 588 0.245 589 0.235 590 0.224 591 0.214 592 0.205 593 0.195 594 0.186 595 0.178 596 0.169 597 0.161 598 0.154 599 0.146 600 0.139 601 0.133 602 0.126 603 0.120 604 0.114 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hornikusersINFO: Augmentation routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:B: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=1.000000 INFO: Done with augmentation INFO: Marker 6 at chr 1 is dropped INFO: Marker 15 at chr 1 is dropped INFO: Marker 16 at chr 1 is dropped INFO: Marker 17 at chr 1 is dropped INFO: Marker 42 at chr 4 is dropped INFO: Marker 48 at chr 4 is dropped INFO: Marker 105 at chr 11 is dropped INFO: Marker 107 at chr 11 is dropped INFO: Marker 111 at chr 11 is dropped INFO: Marker 133 at chr 15 is dropped INFO: Marker 137 at chr 15 is dropped INFO: Marker 139 at chr 15 is dropped INFO: Marker 148 at chr 16 is dropped INFO: Marker 150 at chr 17 is dropped INFO: Marker 151 at chr 17 is dropped INFO: Marker 154 at chr 17 is dropped INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.019 Alfa=0.020000 INFO: dimX:1 nInd:250 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,249)=0.020000 INFO: F(4.003,2,249)=0.020000 INFO: Log-likelihood of full model= -11981.145 INFO: Residual variance= 70.959 INFO: Trait mean= 101.611; Trait variation= 70.959 INFO: Number of output datapoints: 2020 0 0.551 1 0.551 2 0.559 3 0.577 4 0.587 5 0.588 6 0.579 7 0.560 8 0.532 9 0.497 10 0.487 11 0.664 12 0.861 13 1.066 14 1.264 15 1.446 16 1.605 17 1.668 18 2.221 19 2.900 20 2.988 21 3.141 22 3.206 23 3.298 24 3.378 25 3.286 26 2.843 27 2.233 28 2.115 29 2.193 30 2.205 31 2.153 32 2.047 33 2.712 34 3.265 35 3.137 36 3.288 37 3.117 38 3.147 39 3.238 40 3.069 41 2.690 42 2.218 43 1.582 44 1.464 45 1.433 46 1.387 47 1.322 48 1.266 49 1.200 50 1.122 51 1.035 52 0.941 53 0.843 54 0.743 55 0.644 56 0.550 57 0.462 58 0.390 59 0.389 60 0.388 61 0.388 62 0.387 63 0.386 64 0.385 65 0.384 66 0.383 67 0.381 68 0.380 69 0.378 70 0.376 71 0.374 72 0.372 73 0.369 74 0.367 75 0.364 76 0.360 77 0.357 78 0.353 79 0.349 80 0.344 81 0.340 82 0.334 83 0.329 84 0.323 85 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0.078 1898 0.075 1899 0.071 1900 0.068 1901 0.065 1902 0.062 1903 0.059 1904 0.056 1905 0.053 1906 0.051 1907 0.048 1908 0.046 1909 0.044 1910 0.041 1911 0.039 1912 0.037 1913 0.035 1914 0.034 1915 0.032 1916 0.030 1917 0.029 1918 0.027 1919 0.074 1920 0.096 1921 0.160 1922 0.245 1923 0.351 1924 0.472 1925 0.602 1926 0.733 1927 0.857 1928 0.970 1929 1.068 1930 1.334 1931 1.667 1932 1.818 1933 1.734 1934 1.592 1935 1.811 1936 1.976 1937 2.071 1938 2.093 1939 2.044 1940 1.931 1941 1.798 1942 1.798 1943 1.796 1944 1.793 1945 1.787 1946 1.779 1947 1.768 1948 1.753 1949 1.736 1950 1.714 1951 1.690 1952 1.662 1953 1.631 1954 1.597 1955 1.561 1956 1.522 1957 1.482 1958 1.441 1959 1.399 1960 1.356 1961 1.313 1962 1.269 1963 1.226 1964 1.183 1965 1.140 1966 1.098 1967 1.057 1968 1.016 1969 0.977 1970 0.938 1971 0.900 1972 0.863 1973 0.827 1974 0.792 1975 0.759 1976 0.726 1977 0.694 1978 0.664 1979 0.635 1980 0.606 1981 0.579 1982 0.553 1983 0.528 1984 0.503 1985 0.480 1986 0.458 1987 0.436 1988 0.416 1989 0.396 1990 0.377 1991 0.359 1992 0.342 1993 0.325 1994 0.309 1995 0.294 1996 0.280 1997 0.266 1998 0.253 1999 0.240 2000 0.228 2001 0.217 2002 0.206 2003 0.196 2004 0.186 2005 0.176 2006 0.167 2007 0.159 2008 0.151 2009 0.143 2010 0.136 2011 0.129 2012 0.122 2013 0.116 2014 0.110 2015 0.104 2016 0.099 2017 0.093 2018 0.088 2019 0.084 2020 0.968 2021 0.987 2022 0.996 2023 0.987 2024 0.979 2025 0.972 2026 0.971 2027 0.975 2028 0.983 2029 0.991 2030 0.998 2031 0.985 2032 0.974 2033 0.971 2034 0.976 2035 0.986 2036 0.995 2037 0.978 2038 0.984 2039 0.997 2040 0.995 2041 0.992 2042 0.998 2043 0.990 2044 0.995 2045 0.997 2046 0.991 2047 0.995 2048 0.978 2049 0.950 2050 0.937 2051 0.962 2052 0.992 2053 0.990 2054 0.995 2055 0.997 2056 0.965 2057 0.993 2058 0.986 2059 0.947 2060 0.954 2061 1.000 2062 0.980 2063 0.995 2064 0.997 2065 0.995 2066 0.996 2067 1.000 2068 0.991 2069 0.984 2070 0.978 2071 0.973 2072 0.969 2073 0.966 2074 0.968 2075 0.975 2076 0.983 2077 0.991 2078 0.998 2079 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0.987 2535 0.993 2536 0.999 2537 0.995 2538 0.994 2539 0.988 2540 0.993 2541 0.969 2542 0.947 2543 0.950 2544 0.971 2545 0.995 2546 0.963 2547 0.919 2548 0.889 2549 0.922 2550 0.967 2551 0.996 2552 0.986 2553 0.994 2554 0.980 2555 0.957 2556 0.977 2557 0.987 2558 0.983 2559 0.987 2560 0.968 2561 0.949 2562 0.932 2563 0.915 2564 0.898 2565 0.883 2566 0.868 2567 0.853 2568 0.840 2569 0.826 2570 0.813 2571 0.801 2572 0.790 2573 0.778 2574 0.768 2575 0.757 2576 0.748 2577 0.738 2578 0.730 2579 0.722 2580 0.714 2581 0.707 2582 0.700 2583 0.693 2584 0.687 2585 0.682 2586 0.677 2587 0.672 2588 0.667 2589 0.662 2590 0.658 2591 0.654 2592 0.650 2593 0.646 2594 0.643 2595 0.640 2596 0.637 2597 0.634 2598 0.631 2599 0.629 2600 0.626 2601 0.624 2602 0.622 2603 0.620 2604 0.618 2605 0.616 2606 0.614 2607 0.612 2608 0.610 2609 0.608 2610 0.607 2611 0.605 2612 0.603 2613 0.602 2614 0.600 2615 0.599 2616 0.597 2617 0.596 2618 0.595 2619 0.593 2620 0.592 2621 0.590 2622 0.589 2623 0.588 2624 0.586 2625 0.585 2626 0.989 2627 0.996 2628 0.987 2629 0.980 2630 0.979 2631 0.986 2632 0.995 2633 0.995 2634 0.986 2635 0.979 2636 0.976 2637 0.982 2638 0.990 2639 0.999 2640 0.993 2641 0.996 2642 0.993 2643 0.993 2644 0.997 2645 0.994 2646 0.982 2647 0.971 2648 0.960 2649 0.956 2650 0.967 2651 0.978 2652 0.990 2653 0.993 2654 0.996 2655 0.976 2656 0.957 2657 0.939 2658 0.922 2659 0.906 2660 0.890 2661 0.874 2662 0.860 2663 0.846 2664 0.832 2665 0.819 2666 0.806 2667 0.794 2668 0.783 2669 0.772 2670 0.761 2671 0.751 2672 0.741 2673 0.732 2674 0.722 2675 0.714 2676 0.705 2677 0.697 2678 0.690 2679 0.682 2680 0.675 2681 0.668 2682 0.662 2683 0.655 2684 0.649 2685 0.644 2686 0.638 2687 0.633 2688 0.628 2689 0.623 2690 0.619 2691 0.615 2692 0.611 2693 0.607 2694 0.603 2695 0.600 2696 0.597 2697 0.594 2698 0.591 2699 0.588 2700 0.586 2701 0.583 2702 0.581 2703 0.579 2704 0.577 2705 0.576 2706 0.574 2707 0.572 2708 0.571 2709 0.570 2710 0.569 2711 0.568 2712 0.566 2713 0.565 2714 0.564 2715 0.563 2716 0.562 2717 0.561 2718 0.560 2719 0.559 2720 0.558 2721 0.557 2722 0.556 2723 0.555 2724 0.555 2725 0.554 2726 0.553 2727 0.938 2728 0.956 2729 0.974 2730 0.994 2731 0.993 2732 0.985 2733 0.977 2734 0.976 2735 0.984 2736 0.992 2737 0.998 2738 0.990 2739 0.983 2740 0.977 2741 0.978 2742 0.985 2743 0.992 2744 0.999 2745 0.989 2746 0.988 2747 0.998 2748 0.992 2749 0.984 2750 0.976 2751 0.972 2752 0.976 2753 0.979 2754 0.984 2755 0.990 2756 0.996 2757 0.994 2758 0.985 2759 0.978 2760 0.976 2761 0.975 2762 0.974 2763 0.982 2764 0.992 2765 0.994 2766 0.974 2767 0.955 2768 0.938 2769 0.920 2770 0.904 2771 0.888 2772 0.873 2773 0.858 2774 0.844 2775 0.831 2776 0.819 2777 0.807 2778 0.796 2779 0.785 2780 0.775 2781 0.766 2782 0.758 2783 0.750 2784 0.742 2785 0.735 2786 0.728 2787 0.721 2788 0.715 2789 0.709 2790 0.703 2791 0.698 2792 0.692 2793 0.687 2794 0.683 2795 0.678 2796 0.673 2797 0.669 2798 0.665 2799 0.661 2800 0.657 2801 0.653 2802 0.650 2803 0.646 2804 0.643 2805 0.640 2806 0.637 2807 0.634 2808 0.631 2809 0.628 2810 0.626 2811 0.623 2812 0.621 2813 0.618 2814 0.616 2815 0.614 2816 0.612 2817 0.610 2818 0.608 2819 0.606 2820 0.604 2821 0.602 2822 0.600 2823 0.598 2824 0.597 2825 0.595 2826 0.593 2827 0.592 2828 0.893 2829 0.909 2830 0.926 2831 0.943 2832 0.961 2833 0.980 2834 1.000 2835 0.995 2836 0.991 2837 0.989 2838 0.991 2839 0.995 2840 1.000 2841 0.992 2842 0.985 2843 0.978 2844 0.976 2845 0.978 2846 0.984 2847 0.990 2848 0.998 2849 0.990 2850 0.980 2851 0.970 2852 0.965 2853 0.967 2854 0.974 2855 0.984 2856 0.995 2857 0.989 2858 0.973 2859 0.967 2860 0.969 2861 0.979 2862 0.993 2863 0.989 2864 0.969 2865 0.951 2866 0.933 2867 0.917 2868 0.900 2869 0.885 2870 0.870 2871 0.856 2872 0.842 2873 0.829 2874 0.817 2875 0.805 2876 0.794 2877 0.784 2878 0.774 2879 0.765 2880 0.756 2881 0.748 2882 0.741 2883 0.734 2884 0.727 2885 0.720 2886 0.714 2887 0.708 2888 0.702 2889 0.697 2890 0.692 2891 0.687 2892 0.682 2893 0.677 2894 0.673 2895 0.669 2896 0.665 2897 0.661 2898 0.657 2899 0.654 2900 0.650 2901 0.647 2902 0.644 2903 0.641 2904 0.638 2905 0.635 2906 0.633 2907 0.630 2908 0.628 2909 0.625 2910 0.623 2911 0.621 2912 0.618 2913 0.616 2914 0.614 2915 0.612 2916 0.610 2917 0.608 2918 0.607 2919 0.605 2920 0.603 2921 0.602 2922 0.600 2923 0.598 2924 0.597 2925 0.595 2926 0.594 2927 0.592 2928 0.591 2929 0.978 2930 0.998 2931 0.991 2932 0.983 2933 0.976 2934 0.978 2935 0.985 2936 0.994 2937 0.995 2938 0.984 2939 0.973 2940 0.963 2941 0.953 2942 0.945 2943 0.936 2944 0.929 2945 0.922 2946 0.923 2947 0.930 2948 0.938 2949 0.946 2950 0.955 2951 0.965 2952 0.975 2953 0.986 2954 0.998 2955 0.992 2956 0.984 2957 0.977 2958 0.971 2959 0.966 2960 0.970 2961 0.976 2962 0.983 2963 0.991 2964 0.999 2965 0.988 2966 0.992 2967 0.994 2968 0.974 2969 0.956 2970 0.938 2971 0.920 2972 0.904 2973 0.888 2974 0.873 2975 0.858 2976 0.844 2977 0.831 2978 0.818 2979 0.805 2980 0.793 2981 0.782 2982 0.771 2983 0.760 2984 0.750 2985 0.740 2986 0.731 2987 0.721 2988 0.713 2989 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-9774.932 INFO: Marker 89 is dropped, resulting in reduced model logL = -9775.098 INFO: Marker 90 is dropped, resulting in reduced model logL = -9775.218 INFO: Marker 27 is dropped, resulting in reduced model logL = -9775.746 INFO: Marker 28 is dropped, resulting in reduced model logL = -9775.884 INFO: Marker 31 is dropped, resulting in reduced model logL = -9776.425 INFO: Marker 47 is dropped, resulting in reduced model logL = -9777.281 INFO: Marker 8 is dropped, resulting in reduced model logL = -9778.645 INFO: Number of output datapoints: 2020 0 0.131 1 0.162 2 0.132 3 0.102 4 0.074 5 0.048 6 0.027 7 0.011 8 0.002 9 0.000 10 0.003 11 0.011 12 0.023 13 0.029 14 0.028 15 0.027 16 0.025 17 0.022 18 0.019 19 0.016 20 0.013 21 0.020 22 0.030 23 0.041 24 0.052 25 0.060 26 0.085 27 0.118 28 0.165 29 0.219 30 0.279 31 0.342 32 0.406 33 0.466 34 0.521 35 0.569 36 0.765 37 1.095 38 1.438 39 1.748 40 1.998 41 2.093 42 1.993 43 1.914 44 1.912 45 1.862 46 1.765 47 1.604 48 1.589 49 1.574 50 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0.092 1324 0.096 1325 0.099 1326 0.098 1327 0.095 1328 0.088 1329 0.079 1330 0.060 1331 0.036 1332 0.016 1333 0.003 1334 0.000 1335 0.005 1336 0.015 1337 0.015 1338 0.015 1339 0.015 1340 0.015 1341 0.015 1342 0.015 1343 0.015 1344 0.015 1345 0.014 1346 0.014 1347 0.014 1348 0.014 1349 0.014 1350 0.014 1351 0.013 1352 0.013 1353 0.013 1354 0.013 1355 0.013 1356 0.012 1357 0.012 1358 0.012 1359 0.012 1360 0.011 1361 0.011 1362 0.011 1363 0.010 1364 0.010 1365 0.010 1366 0.009 1367 0.009 1368 0.009 1369 0.008 1370 0.008 1371 0.008 1372 0.007 1373 0.007 1374 0.007 1375 0.006 1376 0.006 1377 0.006 1378 0.005 1379 0.005 1380 0.005 1381 0.005 1382 0.004 1383 0.004 1384 0.004 1385 0.003 1386 0.003 1387 0.003 1388 0.003 1389 0.003 1390 0.002 1391 0.002 1392 0.002 1393 0.002 1394 0.002 1395 0.002 1396 0.001 1397 0.001 1398 0.001 1399 0.001 1400 0.001 1401 0.001 1402 0.001 1403 0.001 1404 0.001 1405 0.000 1406 0.000 1407 0.000 1408 0.000 1409 0.000 1410 0.000 1411 0.000 1412 0.000 1413 0.000 1414 0.023 1415 0.007 1416 0.000 1417 0.004 1418 0.022 1419 0.049 1420 0.083 1421 0.125 1422 0.185 1423 0.245 1424 0.418 1425 0.559 1426 0.700 1427 0.750 1428 0.642 1429 0.519 1430 0.432 1431 0.408 1432 0.378 1433 0.343 1434 0.304 1435 0.266 1436 0.246 1437 0.245 1438 0.244 1439 0.243 1440 0.241 1441 0.240 1442 0.238 1443 0.236 1444 0.235 1445 0.232 1446 0.230 1447 0.228 1448 0.225 1449 0.223 1450 0.220 1451 0.216 1452 0.213 1453 0.210 1454 0.206 1455 0.202 1456 0.198 1457 0.194 1458 0.189 1459 0.185 1460 0.180 1461 0.176 1462 0.171 1463 0.166 1464 0.161 1465 0.155 1466 0.150 1467 0.145 1468 0.140 1469 0.135 1470 0.130 1471 0.125 1472 0.120 1473 0.115 1474 0.110 1475 0.105 1476 0.100 1477 0.096 1478 0.091 1479 0.087 1480 0.083 1481 0.079 1482 0.075 1483 0.071 1484 0.067 1485 0.063 1486 0.060 1487 0.057 1488 0.054 1489 0.051 1490 0.048 1491 0.045 1492 0.042 1493 0.040 1494 0.037 1495 0.035 1496 0.033 1497 0.031 1498 0.029 1499 0.027 1500 0.025 1501 0.024 1502 0.022 1503 0.021 1504 0.019 1505 0.018 1506 0.017 1507 0.016 1508 0.015 1509 0.014 1510 0.013 1511 0.012 1512 0.011 1513 0.010 1514 0.009 1515 0.571 1516 0.609 1517 0.641 1518 0.664 1519 0.675 1520 0.673 1521 0.657 1522 0.631 1523 0.594 1524 0.656 1525 0.777 1526 0.882 1527 0.963 1528 1.017 1529 1.032 1530 1.026 1531 0.997 1532 0.946 1533 0.873 1534 0.782 1535 0.680 1536 0.586 1537 0.582 1538 0.578 1539 0.573 1540 0.568 1541 0.562 1542 0.556 1543 0.550 1544 0.543 1545 0.536 1546 0.528 1547 0.520 1548 0.511 1549 0.502 1550 0.493 1551 0.482 1552 0.472 1553 0.461 1554 0.450 1555 0.438 1556 0.427 1557 0.415 1558 0.403 1559 0.390 1560 0.378 1561 0.365 1562 0.353 1563 0.341 1564 0.328 1565 0.316 1566 0.304 1567 0.292 1568 0.280 1569 0.269 1570 0.257 1571 0.246 1572 0.235 1573 0.225 1574 0.215 1575 0.205 1576 0.195 1577 0.186 1578 0.177 1579 0.168 1580 0.159 1581 0.151 1582 0.144 1583 0.136 1584 0.129 1585 0.122 1586 0.115 1587 0.109 1588 0.103 1589 0.097 1590 0.092 1591 0.087 1592 0.082 1593 0.077 1594 0.072 1595 0.068 1596 0.064 1597 0.060 1598 0.056 1599 0.053 1600 0.050 1601 0.047 1602 0.044 1603 0.041 1604 0.038 1605 0.036 1606 0.034 1607 0.031 1608 0.029 1609 0.027 1610 0.025 1611 0.024 1612 0.022 1613 0.021 1614 0.019 1615 0.018 1616 0.063 1617 0.072 1618 0.081 1619 0.087 1620 0.090 1621 0.091 1622 0.090 1623 0.093 1624 0.093 1625 0.083 1626 0.058 1627 0.035 1628 0.017 1629 0.005 1630 0.000 1631 0.002 1632 0.012 1633 0.027 1634 0.046 1635 0.067 1636 0.076 1637 0.076 1638 0.075 1639 0.075 1640 0.074 1641 0.074 1642 0.074 1643 0.073 1644 0.073 1645 0.072 1646 0.071 1647 0.071 1648 0.070 1649 0.069 1650 0.069 1651 0.068 1652 0.067 1653 0.066 1654 0.065 1655 0.064 1656 0.063 1657 0.062 1658 0.060 1659 0.059 1660 0.058 1661 0.056 1662 0.055 1663 0.053 1664 0.052 1665 0.050 1666 0.049 1667 0.047 1668 0.046 1669 0.044 1670 0.042 1671 0.041 1672 0.039 1673 0.038 1674 0.036 1675 0.034 1676 0.033 1677 0.031 1678 0.030 1679 0.028 1680 0.027 1681 0.026 1682 0.024 1683 0.023 1684 0.022 1685 0.020 1686 0.019 1687 0.018 1688 0.017 1689 0.016 1690 0.015 1691 0.014 1692 0.013 1693 0.012 1694 0.012 1695 0.011 1696 0.010 1697 0.009 1698 0.009 1699 0.008 1700 0.008 1701 0.007 1702 0.007 1703 0.006 1704 0.006 1705 0.005 1706 0.005 1707 0.004 1708 0.004 1709 0.004 1710 0.003 1711 0.003 1712 0.003 1713 0.003 1714 0.002 1715 0.002 1716 0.002 1717 0.502 1718 0.445 1719 0.439 1720 0.425 1721 0.403 1722 0.373 1723 0.338 1724 0.298 1725 0.258 1726 0.158 1727 0.042 1728 0.014 1729 0.014 1730 0.014 1731 0.014 1732 0.014 1733 0.014 1734 0.014 1735 0.013 1736 0.013 1737 0.013 1738 0.013 1739 0.013 1740 0.013 1741 0.013 1742 0.013 1743 0.013 1744 0.012 1745 0.012 1746 0.012 1747 0.012 1748 0.012 1749 0.012 1750 0.011 1751 0.011 1752 0.011 1753 0.011 1754 0.011 1755 0.010 1756 0.010 1757 0.010 1758 0.009 1759 0.009 1760 0.009 1761 0.009 1762 0.008 1763 0.008 1764 0.008 1765 0.007 1766 0.007 1767 0.007 1768 0.007 1769 0.006 1770 0.006 1771 0.006 1772 0.005 1773 0.005 1774 0.005 1775 0.005 1776 0.004 1777 0.004 1778 0.004 1779 0.004 1780 0.003 1781 0.003 1782 0.003 1783 0.003 1784 0.002 1785 0.002 1786 0.002 1787 0.002 1788 0.002 1789 0.002 1790 0.001 1791 0.001 1792 0.001 1793 0.001 1794 0.001 1795 0.001 1796 0.001 1797 0.001 1798 0.001 1799 0.001 1800 0.000 1801 0.000 1802 0.000 1803 0.000 1804 0.000 1805 0.000 1806 0.000 1807 0.000 1808 0.000 1809 0.000 1810 0.000 1811 0.000 1812 0.000 1813 0.000 1814 0.000 1815 0.000 1816 0.000 1817 0.000 1818 0.002 1819 0.000 1820 0.000 1821 0.001 1822 0.005 1823 0.011 1824 0.019 1825 0.028 1826 0.037 1827 0.033 1828 0.026 1829 0.019 1830 0.012 1831 0.007 1832 0.003 1833 0.000 1834 0.000 1835 0.000 1836 0.000 1837 0.000 1838 0.000 1839 0.001 1840 0.001 1841 0.001 1842 0.001 1843 0.001 1844 0.001 1845 0.001 1846 0.001 1847 0.001 1848 0.001 1849 0.001 1850 0.001 1851 0.001 1852 0.001 1853 0.001 1854 0.001 1855 0.001 1856 0.001 1857 0.001 1858 0.001 1859 0.001 1860 0.001 1861 0.001 1862 0.001 1863 0.001 1864 0.001 1865 0.001 1866 0.001 1867 0.001 1868 0.001 1869 0.001 1870 0.001 1871 0.001 1872 0.001 1873 0.001 1874 0.001 1875 0.001 1876 0.001 1877 0.001 1878 0.001 1879 0.000 1880 0.000 1881 0.000 1882 0.000 1883 0.000 1884 0.000 1885 0.000 1886 0.000 1887 0.000 1888 0.000 1889 0.000 1890 0.000 1891 0.000 1892 0.000 1893 0.000 1894 0.000 1895 0.000 1896 0.000 1897 0.000 1898 0.000 1899 0.000 1900 0.000 1901 0.000 1902 0.000 1903 0.000 1904 0.000 1905 0.000 1906 0.000 1907 0.000 1908 0.000 1909 0.000 1910 0.000 1911 0.000 1912 0.000 1913 0.000 1914 0.000 1915 0.000 1916 0.000 1917 0.000 1918 0.000 1919 0.489 1920 0.482 1921 0.473 1922 0.462 1923 0.448 1924 0.431 1925 0.412 1926 0.390 1927 0.366 1928 0.340 1929 0.312 1930 0.284 1931 0.255 1932 0.226 1933 0.198 1934 0.172 1935 0.147 1936 0.125 1937 0.104 1938 0.087 1939 0.071 1940 0.058 1941 0.056 1942 0.055 1943 0.055 1944 0.054 1945 0.053 1946 0.053 1947 0.052 1948 0.051 1949 0.051 1950 0.050 1951 0.049 1952 0.048 1953 0.047 1954 0.046 1955 0.045 1956 0.044 1957 0.043 1958 0.042 1959 0.041 1960 0.040 1961 0.038 1962 0.037 1963 0.036 1964 0.035 1965 0.033 1966 0.032 1967 0.031 1968 0.030 1969 0.028 1970 0.027 1971 0.026 1972 0.025 1973 0.024 1974 0.022 1975 0.021 1976 0.020 1977 0.019 1978 0.018 1979 0.017 1980 0.016 1981 0.015 1982 0.014 1983 0.014 1984 0.013 1985 0.012 1986 0.011 1987 0.010 1988 0.010 1989 0.009 1990 0.009 1991 0.008 1992 0.007 1993 0.007 1994 0.006 1995 0.006 1996 0.005 1997 0.005 1998 0.005 1999 0.004 2000 0.004 2001 0.004 2002 0.003 2003 0.003 2004 0.003 2005 0.003 2006 0.002 2007 0.002 2008 0.002 2009 0.002 2010 0.002 2011 0.001 2012 0.001 2013 0.001 2014 0.001 2015 0.001 2016 0.001 2017 0.001 2018 0.001 2019 0.001 2020 1.000 2021 0.978 2022 0.937 2023 0.898 2024 0.861 2025 0.829 2026 0.800 2027 0.800 2028 0.830 2029 0.863 2030 0.900 2031 0.939 2032 0.981 2033 0.972 2034 0.927 2035 0.884 2036 0.848 2037 0.861 2038 0.900 2039 0.943 2040 0.990 2041 0.965 2042 0.924 2043 0.917 2044 0.956 2045 0.999 2046 0.980 2047 0.973 2048 0.931 2049 0.891 2050 0.854 2051 0.845 2052 0.879 2053 0.916 2054 0.956 2055 0.997 2056 0.971 2057 0.928 2058 0.921 2059 0.951 2060 0.988 2061 0.985 2062 0.976 2063 0.977 2064 0.938 2065 0.943 2066 0.984 2067 0.992 2068 0.955 2069 0.919 2070 0.886 2071 0.856 2072 0.827 2073 0.801 2074 0.776 2075 0.753 2076 0.731 2077 0.711 2078 0.693 2079 0.676 2080 0.660 2081 0.645 2082 0.633 2083 0.621 2084 0.610 2085 0.600 2086 0.591 2087 0.582 2088 0.574 2089 0.566 2090 0.560 2091 0.554 2092 0.549 2093 0.544 2094 0.539 2095 0.535 2096 0.531 2097 0.528 2098 0.525 2099 0.522 2100 0.520 2101 0.517 2102 0.515 2103 0.513 2104 0.511 2105 0.509 2106 0.508 2107 0.506 2108 0.505 2109 0.504 2110 0.504 2111 0.503 2112 0.503 2113 0.502 2114 0.502 2115 0.502 2116 0.501 2117 0.501 2118 0.501 2119 0.501 2120 0.501 2121 1.000 2122 0.956 2123 0.915 2124 0.877 2125 0.842 2126 0.813 2127 0.787 2128 0.774 2129 0.783 2130 0.810 2131 0.843 2132 0.879 2133 0.917 2134 0.957 2135 0.998 2136 0.954 2137 0.913 2138 0.875 2139 0.841 2140 0.826 2141 0.853 2142 0.890 2143 0.931 2144 0.974 2145 0.980 2146 0.939 2147 0.900 2148 0.864 2149 0.832 2150 0.827 2151 0.858 2152 0.893 2153 0.931 2154 0.972 2155 0.984 2156 0.943 2157 0.909 2158 0.931 2159 0.970 2160 0.985 2161 0.939 2162 0.899 2163 0.873 2164 0.896 2165 0.936 2166 0.980 2167 0.977 2168 0.940 2169 0.906 2170 0.873 2171 0.843 2172 0.815 2173 0.789 2174 0.765 2175 0.742 2176 0.721 2177 0.701 2178 0.683 2179 0.665 2180 0.649 2181 0.634 2182 0.620 2183 0.608 2184 0.596 2185 0.586 2186 0.577 2187 0.569 2188 0.562 2189 0.555 2190 0.549 2191 0.543 2192 0.539 2193 0.535 2194 0.531 2195 0.528 2196 0.525 2197 0.522 2198 0.519 2199 0.517 2200 0.515 2201 0.513 2202 0.512 2203 0.510 2204 0.509 2205 0.507 2206 0.506 2207 0.505 2208 0.504 2209 0.504 2210 0.503 2211 0.503 2212 0.502 2213 0.502 2214 0.502 2215 0.501 2216 0.501 2217 0.501 2218 0.501 2219 0.501 2220 0.501 2221 0.500 2222 1.000 2223 0.958 2224 0.920 2225 0.884 2226 0.850 2227 0.819 2228 0.791 2229 0.765 2230 0.747 2231 0.756 2232 0.781 2233 0.808 2234 0.840 2235 0.874 2236 0.910 2237 0.948 2238 0.989 2239 0.962 2240 0.916 2241 0.881 2242 0.910 2243 0.954 2244 0.998 2245 0.957 2246 0.918 2247 0.883 2248 0.887 2249 0.924 2250 0.965 2251 0.990 2252 0.947 2253 0.973 2254 0.979 2255 0.932 2256 0.931 2257 0.979 2258 0.977 2259 0.940 2260 0.905 2261 0.873 2262 0.843 2263 0.815 2264 0.789 2265 0.765 2266 0.742 2267 0.721 2268 0.701 2269 0.682 2270 0.665 2271 0.649 2272 0.634 2273 0.620 2274 0.606 2275 0.594 2276 0.582 2277 0.571 2278 0.561 2279 0.552 2280 0.543 2281 0.534 2282 0.527 2283 0.520 2284 0.515 2285 0.511 2286 0.508 2287 0.506 2288 0.505 2289 0.504 2290 0.504 2291 0.504 2292 0.503 2293 0.503 2294 0.503 2295 0.502 2296 0.502 2297 0.502 2298 0.502 2299 0.502 2300 0.501 2301 0.501 2302 0.501 2303 0.501 2304 0.501 2305 0.501 2306 0.501 2307 0.501 2308 0.500 2309 0.500 2310 0.500 2311 0.500 2312 0.500 2313 0.500 2314 0.500 2315 0.500 2316 0.500 2317 0.500 2318 0.500 2319 0.500 2320 0.500 2321 0.500 2322 0.500 2323 1.000 2324 0.953 2325 0.910 2326 0.868 2327 0.830 2328 0.814 2329 0.846 2330 0.885 2331 0.927 2332 0.972 2333 0.982 2334 0.942 2335 0.905 2336 0.869 2337 0.860 2338 0.895 2339 0.933 2340 0.972 2341 0.985 2342 0.943 2343 0.904 2344 0.868 2345 0.833 2346 0.801 2347 0.776 2348 0.753 2349 0.745 2350 0.763 2351 0.787 2352 0.812 2353 0.844 2354 0.879 2355 0.915 2356 0.955 2357 0.997 2358 0.963 2359 0.927 2360 0.893 2361 0.862 2362 0.832 2363 0.805 2364 0.779 2365 0.755 2366 0.733 2367 0.712 2368 0.693 2369 0.675 2370 0.657 2371 0.641 2372 0.627 2373 0.613 2374 0.600 2375 0.588 2376 0.576 2377 0.566 2378 0.556 2379 0.548 2380 0.540 2381 0.534 2382 0.529 2383 0.524 2384 0.520 2385 0.516 2386 0.513 2387 0.511 2388 0.509 2389 0.507 2390 0.506 2391 0.504 2392 0.503 2393 0.503 2394 0.502 2395 0.501 2396 0.501 2397 0.500 2398 0.500 2399 0.500 2400 0.500 2401 0.500 2402 0.500 2403 0.500 2404 0.500 2405 0.500 2406 0.500 2407 0.500 2408 0.500 2409 0.500 2410 0.500 2411 0.500 2412 0.500 2413 0.500 2414 0.499 2415 0.499 2416 0.499 2417 0.499 2418 0.499 2419 0.499 2420 0.499 2421 0.499 2422 0.499 2423 0.499 2424 1.000 2425 0.963 2426 0.959 2427 0.997 2428 0.958 2429 0.917 2430 0.885 2431 0.898 2432 0.933 2433 0.973 2434 0.991 2435 0.965 2436 0.990 2437 0.989 2438 0.960 2439 0.979 2440 0.984 2441 0.978 2442 0.960 2443 0.998 2444 0.965 2445 0.957 2446 0.999 2447 0.961 2448 0.948 2449 0.981 2450 0.977 2451 0.935 2452 0.900 2453 0.919 2454 0.958 2455 0.997 2456 0.959 2457 0.923 2458 0.889 2459 0.858 2460 0.828 2461 0.801 2462 0.775 2463 0.751 2464 0.729 2465 0.708 2466 0.688 2467 0.669 2468 0.652 2469 0.636 2470 0.621 2471 0.606 2472 0.593 2473 0.581 2474 0.569 2475 0.559 2476 0.550 2477 0.542 2478 0.535 2479 0.528 2480 0.523 2481 0.518 2482 0.513 2483 0.510 2484 0.507 2485 0.504 2486 0.502 2487 0.501 2488 0.499 2489 0.498 2490 0.498 2491 0.498 2492 0.497 2493 0.497 2494 0.497 2495 0.497 2496 0.497 2497 0.497 2498 0.498 2499 0.498 2500 0.498 2501 0.498 2502 0.498 2503 0.498 2504 0.498 2505 0.498 2506 0.498 2507 0.498 2508 0.498 2509 0.498 2510 0.499 2511 0.499 2512 0.499 2513 0.499 2514 0.499 2515 0.499 2516 0.499 2517 0.499 2518 0.499 2519 0.499 2520 0.499 2521 0.499 2522 0.499 2523 0.499 2524 0.499 2525 0.832 2526 0.861 2527 0.892 2528 0.925 2529 0.961 2530 0.999 2531 0.957 2532 0.926 2533 0.953 2534 0.995 2535 0.968 2536 0.957 2537 0.997 2538 0.953 2539 0.937 2540 0.977 2541 0.980 2542 0.939 2543 0.904 2544 0.921 2545 0.960 2546 0.995 2547 0.976 2548 0.983 2549 0.990 2550 0.973 2551 0.992 2552 0.972 2553 0.987 2554 0.980 2555 0.985 2556 0.975 2557 0.938 2558 0.904 2559 0.872 2560 0.843 2561 0.815 2562 0.789 2563 0.765 2564 0.743 2565 0.722 2566 0.703 2567 0.685 2568 0.669 2569 0.654 2570 0.639 2571 0.626 2572 0.614 2573 0.603 2574 0.594 2575 0.586 2576 0.578 2577 0.570 2578 0.564 2579 0.557 2580 0.551 2581 0.546 2582 0.541 2583 0.536 2584 0.532 2585 0.529 2586 0.525 2587 0.522 2588 0.519 2589 0.516 2590 0.514 2591 0.512 2592 0.510 2593 0.508 2594 0.507 2595 0.506 2596 0.505 2597 0.504 2598 0.503 2599 0.502 2600 0.502 2601 0.501 2602 0.501 2603 0.501 2604 0.501 2605 0.500 2606 0.500 2607 0.500 2608 0.500 2609 0.500 2610 0.500 2611 0.500 2612 0.500 2613 0.500 2614 0.500 2615 0.500 2616 0.500 2617 0.500 2618 0.500 2619 0.500 2620 0.500 2621 0.500 2622 0.500 2623 0.500 2624 0.500 2625 0.500 2626 1.000 2627 0.955 2628 0.913 2629 0.873 2630 0.838 2631 0.826 2632 0.860 2633 0.898 2634 0.939 2635 0.982 2636 0.973 2637 0.933 2638 0.896 2639 0.866 2640 0.867 2641 0.899 2642 0.938 2643 0.980 2644 0.979 2645 0.947 2646 0.980 2647 0.979 2648 0.937 2649 0.897 2650 0.861 2651 0.828 2652 0.845 2653 0.880 2654 0.917 2655 0.956 2656 0.997 2657 0.959 2658 0.920 2659 0.890 2660 0.916 2661 0.955 2662 0.998 2663 0.962 2664 0.926 2665 0.893 2666 0.861 2667 0.832 2668 0.805 2669 0.779 2670 0.756 2671 0.733 2672 0.713 2673 0.693 2674 0.675 2675 0.658 2676 0.642 2677 0.628 2678 0.614 2679 0.602 2680 0.591 2681 0.582 2682 0.573 2683 0.566 2684 0.558 2685 0.552 2686 0.546 2687 0.540 2688 0.536 2689 0.532 2690 0.528 2691 0.525 2692 0.522 2693 0.519 2694 0.517 2695 0.514 2696 0.512 2697 0.510 2698 0.509 2699 0.507 2700 0.506 2701 0.505 2702 0.504 2703 0.504 2704 0.503 2705 0.503 2706 0.502 2707 0.502 2708 0.501 2709 0.501 2710 0.501 2711 0.501 2712 0.500 2713 0.500 2714 0.500 2715 0.500 2716 0.500 2717 0.500 2718 0.500 2719 0.500 2720 0.499 2721 0.499 2722 0.499 2723 0.499 2724 0.499 2725 0.499 2726 0.499 2727 1.000 2728 0.986 2729 0.946 2730 0.909 2731 0.932 2732 0.971 2733 0.988 2734 0.948 2735 0.912 2736 0.880 2737 0.872 2738 0.902 2739 0.938 2740 0.976 2741 0.980 2742 0.936 2743 0.977 2744 0.977 2745 0.935 2746 0.896 2747 0.860 2748 0.831 2749 0.863 2750 0.899 2751 0.938 2752 0.980 2753 0.977 2754 0.940 2755 0.905 2756 0.873 2757 0.843 2758 0.815 2759 0.789 2760 0.764 2761 0.741 2762 0.720 2763 0.700 2764 0.681 2765 0.664 2766 0.647 2767 0.632 2768 0.618 2769 0.604 2770 0.591 2771 0.580 2772 0.568 2773 0.558 2774 0.548 2775 0.539 2776 0.531 2777 0.523 2778 0.516 2779 0.512 2780 0.508 2781 0.506 2782 0.504 2783 0.502 2784 0.502 2785 0.501 2786 0.501 2787 0.501 2788 0.500 2789 0.500 2790 0.500 2791 0.500 2792 0.500 2793 0.500 2794 0.500 2795 0.500 2796 0.500 2797 0.500 2798 0.500 2799 0.500 2800 0.500 2801 0.500 2802 0.500 2803 0.500 2804 0.500 2805 0.500 2806 0.500 2807 0.500 2808 0.500 2809 0.500 2810 0.500 2811 0.500 2812 0.500 2813 0.500 2814 0.500 2815 0.500 2816 0.500 2817 0.500 2818 0.500 2819 0.500 2820 0.500 2821 0.500 2822 0.500 2823 0.500 2824 0.500 2825 0.500 2826 0.500 2827 0.500 2828 1.000 2829 0.948 2830 0.993 2831 0.960 2832 0.918 2833 0.900 2834 0.940 2835 0.983 2836 0.972 2837 0.931 2838 0.893 2839 0.890 2840 0.928 2841 0.970 2842 0.986 2843 0.946 2844 0.980 2845 0.978 2846 0.938 2847 0.902 2848 0.897 2849 0.932 2850 0.972 2851 0.986 2852 0.947 2853 0.950 2854 0.989 2855 0.970 2856 0.934 2857 0.899 2858 0.867 2859 0.837 2860 0.809 2861 0.783 2862 0.759 2863 0.736 2864 0.715 2865 0.695 2866 0.676 2867 0.658 2868 0.642 2869 0.626 2870 0.612 2871 0.598 2872 0.585 2873 0.573 2874 0.562 2875 0.552 2876 0.542 2877 0.532 2878 0.523 2879 0.515 2880 0.507 2881 0.500 2882 0.496 2883 0.495 2884 0.496 2885 0.496 2886 0.496 2887 0.496 2888 0.497 2889 0.497 2890 0.497 2891 0.497 2892 0.497 2893 0.498 2894 0.498 2895 0.498 2896 0.498 2897 0.498 2898 0.498 2899 0.498 2900 0.498 2901 0.499 2902 0.499 2903 0.499 2904 0.499 2905 0.499 2906 0.499 2907 0.499 2908 0.499 2909 0.499 2910 0.499 2911 0.499 2912 0.499 2913 0.499 2914 0.499 2915 0.499 2916 0.499 2917 0.499 2918 0.499 2919 0.499 2920 0.499 2921 0.499 2922 0.499 2923 0.499 2924 0.499 2925 0.499 2926 0.499 2927 0.499 2928 0.499 2929 1.000 2930 0.958 2931 0.919 2932 0.882 2933 0.847 2934 0.816 2935 0.785 2936 0.804 2937 0.835 2938 0.869 2939 0.905 2940 0.943 2941 0.984 2942 0.973 2943 0.932 2944 0.895 2945 0.863 2946 0.872 2947 0.905 2948 0.943 2949 0.984 2950 0.968 2951 0.923 2952 0.934 2953 0.981 2954 0.971 2955 0.929 2956 0.892 2957 0.900 2958 0.936 2959 0.977 2960 0.981 2961 0.944 2962 0.909 2963 0.876 2964 0.846 2965 0.818 2966 0.792 2967 0.767 2968 0.744 2969 0.723 2970 0.703 2971 0.684 2972 0.667 2973 0.650 2974 0.635 2975 0.621 2976 0.608 2977 0.596 2978 0.585 2979 0.576 2980 0.569 2981 0.562 2982 0.555 2983 0.549 2984 0.544 2985 0.539 2986 0.535 2987 0.531 2988 0.527 2989 0.524 2990 0.521 2991 0.518 2992 0.516 2993 0.514 2994 0.512 2995 0.510 2996 0.509 2997 0.508 2998 0.507 2999 0.506 3000 0.505 3001 0.504 3002 0.503 3003 0.503 3004 0.502 3005 0.502 3006 0.501 3007 0.501 3008 0.501 3009 0.501 3010 0.501 3011 0.500 3012 0.500 3013 0.500 3014 0.500 3015 0.500 3016 0.500 3017 0.500 3018 0.499 3019 0.499 3020 0.499 3021 0.499 3022 0.499 3023 0.499 3024 0.499 3025 0.499 3026 0.499 3027 0.499 3028 0.499 3029 0.499 3030 1.000 3031 0.957 3032 0.917 3033 0.881 3034 0.860 3035 0.895 3036 0.933 3037 0.975 3038 0.980 3039 0.937 3040 0.895 3041 0.904 3042 0.946 3043 0.990 3044 0.966 3045 0.926 3046 0.891 3047 0.909 3048 0.947 3049 0.988 3050 0.968 3051 0.996 3052 0.960 3053 0.919 3054 0.881 3055 0.846 3056 0.816 3057 0.809 3058 0.838 3059 0.872 3060 0.909 3061 0.949 3062 0.992 3063 0.967 3064 0.931 3065 0.897 3066 0.865 3067 0.835 3068 0.808 3069 0.782 3070 0.757 3071 0.735 3072 0.714 3073 0.694 3074 0.675 3075 0.658 3076 0.642 3077 0.626 3078 0.612 3079 0.599 3080 0.586 3081 0.574 3082 0.563 3083 0.553 3084 0.543 3085 0.534 3086 0.525 3087 0.517 3088 0.510 3089 0.505 3090 0.501 3091 0.499 3092 0.498 3093 0.498 3094 0.498 3095 0.498 3096 0.498 3097 0.498 3098 0.499 3099 0.499 3100 0.499 3101 0.499 3102 0.499 3103 0.499 3104 0.499 3105 0.499 3106 0.499 3107 0.499 3108 0.499 3109 0.499 3110 0.499 3111 0.499 3112 0.499 3113 0.499 3114 0.499 3115 0.499 3116 0.499 3117 0.499 3118 0.499 3119 0.499 3120 0.499 3121 0.499 3122 0.499 3123 0.499 3124 0.499 3125 0.499 3126 0.499 3127 0.499 3128 0.499 3129 0.499 3130 0.499 3131 1.000 3132 0.962 3133 0.959 3134 0.996 3135 0.960 3136 0.920 3137 0.882 3138 0.852 3139 0.886 3140 0.924 3141 0.965 3142 0.991 3143 0.951 3144 0.938 3145 0.977 3146 0.978 3147 0.935 3148 0.896 3149 0.884 3150 0.917 3151 0.959 3152 0.995 3153 0.954 3154 0.916 3155 0.884 3156 0.912 3157 0.950 3158 0.990 3159 0.969 3160 0.933 3161 0.899 3162 0.867 3163 0.837 3164 0.809 3165 0.783 3166 0.758 3167 0.736 3168 0.714 3169 0.694 3170 0.676 3171 0.658 3172 0.642 3173 0.626 3174 0.612 3175 0.599 3176 0.587 3177 0.576 3178 0.565 3179 0.556 3180 0.548 3181 0.541 3182 0.535 3183 0.530 3184 0.526 3185 0.522 3186 0.518 3187 0.515 3188 0.513 3189 0.511 3190 0.509 3191 0.507 3192 0.505 3193 0.504 3194 0.503 3195 0.502 3196 0.501 3197 0.500 3198 0.500 3199 0.500 3200 0.499 3201 0.499 3202 0.499 3203 0.499 3204 0.499 3205 0.499 3206 0.499 3207 0.499 3208 0.499 3209 0.499 3210 0.499 3211 0.499 3212 0.499 3213 0.499 3214 0.499 3215 0.499 3216 0.499 3217 0.499 3218 0.499 3219 0.499 3220 0.499 3221 0.499 3222 0.499 3223 0.499 3224 0.499 3225 0.499 3226 0.499 3227 0.499 3228 0.499 3229 0.499 3230 0.499 3231 0.499 3232 1.000 3233 0.956 3234 0.911 3235 0.911 3236 0.947 3237 0.991 3238 0.971 3239 0.975 3240 0.942 3241 0.979 3242 0.982 3243 0.979 3244 0.982 3245 0.996 3246 0.989 3247 0.966 3248 0.927 3249 0.954 3250 0.999 3251 0.961 3252 0.925 3253 0.891 3254 0.860 3255 0.830 3256 0.803 3257 0.777 3258 0.753 3259 0.731 3260 0.710 3261 0.690 3262 0.672 3263 0.655 3264 0.639 3265 0.624 3266 0.610 3267 0.596 3268 0.584 3269 0.572 3270 0.561 3271 0.551 3272 0.542 3273 0.533 3274 0.525 3275 0.519 3276 0.514 3277 0.509 3278 0.505 3279 0.503 3280 0.501 3281 0.500 3282 0.499 3283 0.499 3284 0.498 3285 0.498 3286 0.498 3287 0.499 3288 0.499 3289 0.499 3290 0.499 3291 0.499 3292 0.499 3293 0.499 3294 0.499 3295 0.499 3296 0.499 3297 0.499 3298 0.499 3299 0.499 3300 0.499 3301 0.499 3302 0.499 3303 0.499 3304 0.499 3305 0.499 3306 0.499 3307 0.499 3308 0.499 3309 0.499 3310 0.499 3311 0.499 3312 0.499 3313 0.499 3314 0.499 3315 0.499 3316 0.499 3317 0.499 3318 0.499 3319 0.499 3320 0.499 3321 0.499 3322 0.499 3323 0.499 3324 0.499 3325 0.499 3326 0.499 3327 0.499 3328 0.499 3329 0.499 3330 0.499 3331 0.499 3332 0.499 3333 1.000 3334 0.958 3335 0.919 3336 0.882 3337 0.848 3338 0.819 3339 0.798 3340 0.822 3341 0.851 3342 0.884 3343 0.921 3344 0.960 3345 0.998 3346 0.958 3347 0.922 3348 0.945 3349 0.984 3350 0.974 3351 0.934 3352 0.897 3353 0.891 3354 0.927 3355 0.967 3356 0.991 3357 0.953 3358 0.917 3359 0.884 3360 0.854 3361 0.825 3362 0.799 3363 0.774 3364 0.751 3365 0.729 3366 0.709 3367 0.690 3368 0.673 3369 0.657 3370 0.641 3371 0.627 3372 0.614 3373 0.601 3374 0.590 3375 0.579 3376 0.569 3377 0.559 3378 0.550 3379 0.542 3380 0.534 3381 0.528 3382 0.522 3383 0.518 3384 0.514 3385 0.512 3386 0.510 3387 0.509 3388 0.508 3389 0.507 3390 0.506 3391 0.506 3392 0.505 3393 0.505 3394 0.505 3395 0.504 3396 0.504 3397 0.503 3398 0.503 3399 0.503 3400 0.503 3401 0.502 3402 0.502 3403 0.502 3404 0.502 3405 0.502 3406 0.501 3407 0.501 3408 0.501 3409 0.501 3410 0.501 3411 0.501 3412 0.501 3413 0.501 3414 0.501 3415 0.500 3416 0.500 3417 0.500 3418 0.500 3419 0.500 3420 0.500 3421 0.500 3422 0.500 3423 0.500 3424 0.500 3425 0.500 3426 0.500 3427 0.500 3428 0.500 3429 0.500 3430 0.500 3431 0.500 3432 0.500 3433 0.500 3434 1.000 3435 0.956 3436 0.914 3437 0.874 3438 0.885 3439 0.926 3440 0.968 3441 0.988 3442 0.946 3443 0.983 3444 0.985 3445 0.961 3446 0.998 3447 0.981 3448 0.942 3449 0.971 3450 0.984 3451 0.944 3452 0.905 3453 0.899 3454 0.937 3455 0.979 3456 0.979 3457 0.942 3458 0.907 3459 0.875 3460 0.844 3461 0.816 3462 0.789 3463 0.765 3464 0.741 3465 0.720 3466 0.699 3467 0.680 3468 0.662 3469 0.646 3470 0.630 3471 0.615 3472 0.602 3473 0.589 3474 0.577 3475 0.566 3476 0.557 3477 0.550 3478 0.543 3479 0.537 3480 0.531 3481 0.526 3482 0.521 3483 0.517 3484 0.514 3485 0.512 3486 0.510 3487 0.508 3488 0.506 3489 0.504 3490 0.503 3491 0.502 3492 0.501 3493 0.500 3494 0.500 3495 0.500 3496 0.499 3497 0.499 3498 0.499 3499 0.499 3500 0.499 3501 0.499 3502 0.499 3503 0.499 3504 0.499 3505 0.499 3506 0.499 3507 0.499 3508 0.499 3509 0.499 3510 0.499 3511 0.499 3512 0.499 3513 0.499 3514 0.499 3515 0.499 3516 0.499 3517 0.499 3518 0.499 3519 0.499 3520 0.499 3521 0.499 3522 0.499 3523 0.499 3524 0.499 3525 0.499 3526 0.499 3527 0.499 3528 0.499 3529 0.499 3530 0.499 3531 0.499 3532 0.499 3533 0.499 3534 0.499 3535 1.000 3536 0.956 3537 0.915 3538 0.878 3539 0.853 3540 0.867 3541 0.900 3542 0.940 3543 0.982 3544 0.977 3545 0.945 3546 0.938 3547 0.963 3548 0.996 3549 0.963 3550 0.925 3551 0.890 3552 0.871 3553 0.885 3554 0.921 3555 0.961 3556 0.996 3557 0.957 3558 0.922 3559 0.888 3560 0.857 3561 0.828 3562 0.801 3563 0.776 3564 0.753 3565 0.731 3566 0.710 3567 0.691 3568 0.673 3569 0.657 3570 0.641 3571 0.626 3572 0.613 3573 0.601 3574 0.590 3575 0.580 3576 0.572 3577 0.564 3578 0.556 3579 0.550 3580 0.544 3581 0.539 3582 0.535 3583 0.530 3584 0.527 3585 0.523 3586 0.520 3587 0.518 3588 0.515 3589 0.513 3590 0.511 3591 0.509 3592 0.508 3593 0.507 3594 0.506 3595 0.505 3596 0.504 3597 0.503 3598 0.503 3599 0.503 3600 0.502 3601 0.502 3602 0.502 3603 0.502 3604 0.501 3605 0.501 3606 0.501 3607 0.501 3608 0.501 3609 0.501 3610 0.501 3611 0.500 3612 0.500 3613 0.500 3614 0.500 3615 0.500 3616 0.500 3617 0.500 3618 0.499 3619 0.499 3620 0.499 3621 0.499 3622 0.499 3623 0.499 3624 0.499 3625 0.499 3626 0.499 3627 0.499 3628 0.499 3629 0.499 3630 0.499 3631 0.499 3632 0.499 3633 0.499 3634 0.499 3635 0.499 3636 1.000 3637 0.954 3638 0.913 3639 0.887 3640 0.918 3641 0.960 3642 0.993 3643 0.943 3644 0.966 3645 0.985 3646 0.943 3647 0.904 3648 0.867 3649 0.832 3650 0.799 3651 0.829 3652 0.863 3653 0.900 3654 0.940 3655 0.981 3656 0.977 3657 0.940 3658 0.905 3659 0.873 3660 0.843 3661 0.815 3662 0.788 3663 0.764 3664 0.741 3665 0.719 3666 0.699 3667 0.681 3668 0.663 3669 0.647 3670 0.631 3671 0.617 3672 0.603 3673 0.591 3674 0.579 3675 0.568 3676 0.558 3677 0.548 3678 0.540 3679 0.533 3680 0.527 3681 0.522 3682 0.517 3683 0.514 3684 0.511 3685 0.508 3686 0.506 3687 0.504 3688 0.503 3689 0.502 3690 0.502 3691 0.501 3692 0.501 3693 0.500 3694 0.500 3695 0.500 3696 0.500 3697 0.500 3698 0.500 3699 0.500 3700 0.499 3701 0.499 3702 0.499 3703 0.499 3704 0.499 3705 0.499 3706 0.499 3707 0.499 3708 0.499 3709 0.499 3710 0.499 3711 0.499 3712 0.499 3713 0.499 3714 0.499 3715 0.499 3716 0.499 3717 0.499 3718 0.499 3719 0.499 3720 0.499 3721 0.499 3722 0.499 3723 0.499 3724 0.499 3725 0.499 3726 0.499 3727 0.499 3728 0.499 3729 0.499 3730 0.499 3731 0.499 3732 0.499 3733 0.499 3734 0.499 3735 0.499 3736 0.499 3737 1.000 3738 0.971 3739 0.929 3740 0.891 3741 0.858 3742 0.863 3743 0.896 3744 0.935 3745 0.978 3746 0.976 3747 0.977 3748 0.978 3749 0.941 3750 0.906 3751 0.873 3752 0.843 3753 0.815 3754 0.788 3755 0.763 3756 0.740 3757 0.718 3758 0.698 3759 0.679 3760 0.661 3761 0.644 3762 0.628 3763 0.614 3764 0.600 3765 0.587 3766 0.575 3767 0.563 3768 0.552 3769 0.542 3770 0.533 3771 0.524 3772 0.515 3773 0.508 3774 0.502 3775 0.499 3776 0.496 3777 0.495 3778 0.495 3779 0.495 3780 0.495 3781 0.495 3782 0.496 3783 0.496 3784 0.496 3785 0.497 3786 0.497 3787 0.497 3788 0.497 3789 0.497 3790 0.498 3791 0.498 3792 0.498 3793 0.498 3794 0.498 3795 0.498 3796 0.498 3797 0.498 3798 0.498 3799 0.499 3800 0.499 3801 0.499 3802 0.499 3803 0.499 3804 0.499 3805 0.499 3806 0.499 3807 0.499 3808 0.499 3809 0.499 3810 0.499 3811 0.499 3812 0.499 3813 0.499 3814 0.499 3815 0.499 3816 0.499 3817 0.499 3818 0.499 3819 0.499 3820 0.499 3821 0.499 3822 0.499 3823 0.499 3824 0.499 3825 0.499 3826 0.499 3827 0.499 3828 0.499 3829 0.499 3830 0.499 3831 0.499 3832 0.499 3833 0.499 3834 0.499 3835 0.499 3836 0.499 3837 0.499 3838 1.000 3839 0.957 3840 0.917 3841 0.881 3842 0.850 3843 0.876 3844 0.911 3845 0.950 3846 0.992 3847 0.964 3848 0.923 3849 0.885 3850 0.853 3851 0.866 3852 0.900 3853 0.939 3854 0.981 3855 0.976 3856 0.938 3857 0.903 3858 0.915 3859 0.951 3860 0.990 3861 0.970 3862 0.934 3863 0.899 3864 0.867 3865 0.838 3866 0.810 3867 0.784 3868 0.760 3869 0.737 3870 0.716 3871 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model= -8847.557 INFO: Residual variance= 28972781.109 INFO: Trait mean= 4327.088; Trait variation= 28972781.109 INFO: Number of output datapoints: 245 0 0.862 1 0.883 2 0.889 3 0.885 4 0.875 5 0.779 6 0.300 7 0.513 8 0.596 9 0.456 10 0.184 11 0.143 12 0.101 13 0.081 14 0.062 15 0.018 16 0.003 17 0.008 18 0.086 19 0.280 20 0.142 21 0.019 22 0.000 23 0.125 24 0.188 25 0.282 26 0.369 27 0.081 28 0.598 29 0.069 30 0.032 31 0.032 32 0.032 33 0.031 34 0.031 35 0.030 36 0.029 37 0.029 38 0.028 39 0.026 40 0.025 41 0.024 42 0.022 43 0.021 44 0.019 45 0.018 46 0.016 47 0.015 48 0.013 49 0.598 50 0.603 51 0.600 52 0.593 53 0.584 54 0.446 55 0.539 56 0.568 57 0.037 58 0.095 59 0.599 60 1.592 61 2.307 62 1.649 63 2.282 64 0.963 65 0.238 66 0.100 67 0.052 68 0.066 69 0.021 70 0.015 71 0.015 72 0.016 73 0.016 74 0.016 75 0.016 76 0.016 77 0.016 78 0.017 79 0.017 80 0.017 81 0.017 82 0.016 83 0.016 84 0.015 85 0.014 86 0.012 87 0.011 88 0.010 89 0.009 90 0.008 91 0.007 92 0.006 93 0.005 94 0.004 95 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695 0.000 696 0.000 697 0.000 698 0.000 699 0.000 700 0.000 701 0.000 702 0.000 703 0.000 704 0.000 705 0.000 706 0.000 707 0.000 708 0.000 709 0.000 710 0.000 711 0.000 712 0.000 713 0.000 714 0.000 715 0.000 716 0.000 717 0.000 718 0.000 719 0.000 qtl/inst/contrib/bin/test/regression/t32out.txt0000644000175100001440000021566612422233634021336 0ustar hornikusersINFO: Augmentation routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:F: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=0.500000 INFO: Done with augmentation INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.021 Alfa=0.020000 INFO: dimX:1 nInd:120 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.566,1,119)=0.020000 INFO: F(4.003,2,119)=0.020000 INFO: Log-likelihood of full model= -9356.840 INFO: Residual variance= 6631.434 INFO: Trait mean= 148.703; Trait variation= 6631.434 INFO: Number of output datapoints: 2020 0 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1647 0.112 1648 0.112 1649 0.111 1650 0.111 1651 0.110 1652 0.110 1653 0.109 1654 0.108 1655 0.107 1656 0.106 1657 0.105 1658 0.104 1659 0.103 1660 0.102 1661 0.101 1662 0.099 1663 0.098 1664 0.096 1665 0.094 1666 0.092 1667 0.090 1668 0.088 1669 0.086 1670 0.084 1671 0.082 1672 0.080 1673 0.078 1674 0.075 1675 0.073 1676 0.071 1677 0.068 1678 0.066 1679 0.064 1680 0.061 1681 0.059 1682 0.057 1683 0.055 1684 0.052 1685 0.050 1686 0.048 1687 0.046 1688 0.044 1689 0.042 1690 0.040 1691 0.039 1692 0.037 1693 0.035 1694 0.033 1695 0.032 1696 0.030 1697 0.029 1698 0.027 1699 0.026 1700 0.025 1701 0.023 1702 0.022 1703 0.021 1704 0.020 1705 0.019 1706 0.018 1707 0.017 1708 0.016 1709 0.015 1710 0.014 1711 0.013 1712 0.013 1713 0.012 1714 0.011 1715 0.011 1716 0.010 1717 0.513 1718 0.442 1719 0.431 1720 0.411 1721 0.382 1722 0.346 1723 0.305 1724 0.262 1725 0.219 1726 0.157 1727 0.082 1728 0.056 1729 0.056 1730 0.055 1731 0.055 1732 0.055 1733 0.055 1734 0.055 1735 0.055 1736 0.055 1737 0.055 1738 0.055 1739 0.055 1740 0.055 1741 0.055 1742 0.054 1743 0.054 1744 0.054 1745 0.054 1746 0.054 1747 0.053 1748 0.053 1749 0.053 1750 0.053 1751 0.052 1752 0.052 1753 0.051 1754 0.051 1755 0.050 1756 0.050 1757 0.049 1758 0.049 1759 0.048 1760 0.047 1761 0.047 1762 0.046 1763 0.045 1764 0.044 1765 0.043 1766 0.042 1767 0.041 1768 0.040 1769 0.039 1770 0.038 1771 0.037 1772 0.036 1773 0.034 1774 0.033 1775 0.032 1776 0.031 1777 0.030 1778 0.029 1779 0.028 1780 0.027 1781 0.025 1782 0.024 1783 0.023 1784 0.022 1785 0.021 1786 0.020 1787 0.019 1788 0.018 1789 0.018 1790 0.017 1791 0.016 1792 0.015 1793 0.014 1794 0.014 1795 0.013 1796 0.012 1797 0.012 1798 0.011 1799 0.010 1800 0.010 1801 0.009 1802 0.009 1803 0.008 1804 0.008 1805 0.007 1806 0.007 1807 0.006 1808 0.006 1809 0.006 1810 0.005 1811 0.005 1812 0.005 1813 0.004 1814 0.004 1815 0.004 1816 0.003 1817 0.003 1818 0.045 1819 0.071 1820 0.104 1821 0.142 1822 0.185 1823 0.229 1824 0.271 1825 0.309 1826 0.341 1827 0.293 1828 0.226 1829 0.159 1830 0.097 1831 0.048 1832 0.016 1833 0.001 1834 0.002 1835 0.005 1836 0.005 1837 0.005 1838 0.004 1839 0.003 1840 0.002 1841 0.002 1842 0.002 1843 0.002 1844 0.002 1845 0.002 1846 0.002 1847 0.002 1848 0.002 1849 0.002 1850 0.002 1851 0.002 1852 0.002 1853 0.002 1854 0.002 1855 0.002 1856 0.002 1857 0.002 1858 0.002 1859 0.002 1860 0.002 1861 0.002 1862 0.002 1863 0.002 1864 0.002 1865 0.002 1866 0.002 1867 0.002 1868 0.002 1869 0.002 1870 0.002 1871 0.002 1872 0.002 1873 0.002 1874 0.002 1875 0.001 1876 0.001 1877 0.001 1878 0.001 1879 0.001 1880 0.001 1881 0.001 1882 0.001 1883 0.001 1884 0.001 1885 0.001 1886 0.001 1887 0.001 1888 0.001 1889 0.001 1890 0.001 1891 0.001 1892 0.001 1893 0.001 1894 0.001 1895 0.001 1896 0.000 1897 0.000 1898 0.000 1899 0.000 1900 0.000 1901 0.000 1902 0.000 1903 0.000 1904 0.000 1905 0.000 1906 0.000 1907 0.000 1908 0.000 1909 0.000 1910 0.000 1911 0.000 1912 0.000 1913 0.000 1914 0.000 1915 0.000 1916 0.000 1917 0.000 1918 0.000 1919 0.602 1920 0.572 1921 0.538 1922 0.500 1923 0.458 1924 0.413 1925 0.365 1926 0.316 1927 0.266 1928 0.216 1929 0.169 1930 0.126 1931 0.089 1932 0.057 1933 0.033 1934 0.015 1935 0.005 1936 0.000 1937 0.000 1938 0.005 1939 0.012 1940 0.022 1941 0.024 1942 0.024 1943 0.024 1944 0.024 1945 0.023 1946 0.023 1947 0.023 1948 0.023 1949 0.023 1950 0.023 1951 0.023 1952 0.023 1953 0.023 1954 0.022 1955 0.022 1956 0.022 1957 0.022 1958 0.022 1959 0.021 1960 0.021 1961 0.021 1962 0.021 1963 0.020 1964 0.020 1965 0.020 1966 0.019 1967 0.019 1968 0.019 1969 0.018 1970 0.018 1971 0.017 1972 0.017 1973 0.016 1974 0.016 1975 0.016 1976 0.015 1977 0.015 1978 0.014 1979 0.014 1980 0.013 1981 0.013 1982 0.012 1983 0.012 1984 0.011 1985 0.011 1986 0.010 1987 0.010 1988 0.010 1989 0.009 1990 0.009 1991 0.008 1992 0.008 1993 0.007 1994 0.007 1995 0.007 1996 0.006 1997 0.006 1998 0.006 1999 0.005 2000 0.005 2001 0.005 2002 0.004 2003 0.004 2004 0.004 2005 0.004 2006 0.003 2007 0.003 2008 0.003 2009 0.003 2010 0.003 2011 0.002 2012 0.002 2013 0.002 2014 0.002 2015 0.002 2016 0.002 2017 0.002 2018 0.001 2019 0.001 2020 1.000 2021 0.978 2022 0.937 2023 0.898 2024 0.862 2025 0.829 2026 0.799 2027 0.789 2028 0.813 2029 0.845 2030 0.882 2031 0.921 2032 0.961 2033 0.959 2034 0.926 2035 0.896 2036 0.872 2037 0.883 2038 0.914 2039 0.948 2040 0.986 2041 0.961 2042 0.921 2043 0.915 2044 0.956 2045 0.999 2046 0.980 2047 0.973 2048 0.931 2049 0.892 2050 0.854 2051 0.844 2052 0.878 2053 0.916 2054 0.955 2055 0.997 2056 0.971 2057 0.927 2058 0.922 2059 0.951 2060 0.988 2061 0.988 2062 0.979 2063 0.977 2064 0.938 2065 0.942 2066 0.984 2067 0.992 2068 0.954 2069 0.919 2070 0.886 2071 0.855 2072 0.827 2073 0.800 2074 0.775 2075 0.752 2076 0.730 2077 0.710 2078 0.692 2079 0.675 2080 0.660 2081 0.647 2082 0.635 2083 0.623 2084 0.613 2085 0.604 2086 0.596 2087 0.588 2088 0.581 2089 0.575 2090 0.569 2091 0.563 2092 0.557 2093 0.552 2094 0.547 2095 0.543 2096 0.538 2097 0.534 2098 0.531 2099 0.528 2100 0.525 2101 0.522 2102 0.520 2103 0.518 2104 0.516 2105 0.514 2106 0.512 2107 0.510 2108 0.509 2109 0.508 2110 0.507 2111 0.507 2112 0.506 2113 0.505 2114 0.504 2115 0.504 2116 0.503 2117 0.502 2118 0.502 2119 0.501 2120 0.500 2121 0.991 2122 0.950 2123 0.912 2124 0.877 2125 0.845 2126 0.817 2127 0.793 2128 0.779 2129 0.792 2130 0.818 2131 0.849 2132 0.884 2133 0.920 2134 0.959 2135 0.998 2136 0.955 2137 0.914 2138 0.877 2139 0.844 2140 0.830 2141 0.856 2142 0.890 2143 0.928 2144 0.969 2145 0.972 2146 0.931 2147 0.893 2148 0.858 2149 0.830 2150 0.826 2151 0.844 2152 0.869 2153 0.898 2154 0.933 2155 0.949 2156 0.928 2157 0.912 2158 0.935 2159 0.971 2160 0.983 2161 0.939 2162 0.902 2163 0.884 2164 0.903 2165 0.941 2166 0.981 2167 0.977 2168 0.940 2169 0.906 2170 0.873 2171 0.843 2172 0.815 2173 0.789 2174 0.765 2175 0.742 2176 0.721 2177 0.701 2178 0.683 2179 0.666 2180 0.650 2181 0.636 2182 0.623 2183 0.612 2184 0.602 2185 0.593 2186 0.585 2187 0.578 2188 0.572 2189 0.566 2190 0.561 2191 0.556 2192 0.551 2193 0.546 2194 0.542 2195 0.538 2196 0.534 2197 0.531 2198 0.528 2199 0.525 2200 0.522 2201 0.520 2202 0.518 2203 0.516 2204 0.514 2205 0.512 2206 0.511 2207 0.510 2208 0.508 2209 0.508 2210 0.507 2211 0.506 2212 0.505 2213 0.504 2214 0.504 2215 0.503 2216 0.502 2217 0.502 2218 0.501 2219 0.500 2220 0.500 2221 0.499 2222 1.000 2223 0.957 2224 0.918 2225 0.881 2226 0.846 2227 0.813 2228 0.783 2229 0.756 2230 0.739 2231 0.743 2232 0.769 2233 0.798 2234 0.829 2235 0.862 2236 0.897 2237 0.934 2238 0.973 2239 0.958 2240 0.926 2241 0.901 2242 0.927 2243 0.958 2244 0.988 2245 0.956 2246 0.927 2247 0.902 2248 0.906 2249 0.936 2250 0.970 2251 0.990 2252 0.948 2253 0.969 2254 0.978 2255 0.950 2256 0.952 2257 0.985 2258 0.977 2259 0.940 2260 0.905 2261 0.873 2262 0.843 2263 0.815 2264 0.789 2265 0.765 2266 0.742 2267 0.721 2268 0.702 2269 0.683 2270 0.666 2271 0.650 2272 0.635 2273 0.621 2274 0.608 2275 0.596 2276 0.584 2277 0.575 2278 0.566 2279 0.558 2280 0.552 2281 0.546 2282 0.541 2283 0.537 2284 0.532 2285 0.528 2286 0.525 2287 0.521 2288 0.518 2289 0.515 2290 0.513 2291 0.510 2292 0.509 2293 0.507 2294 0.506 2295 0.505 2296 0.505 2297 0.504 2298 0.504 2299 0.503 2300 0.503 2301 0.502 2302 0.502 2303 0.502 2304 0.501 2305 0.501 2306 0.501 2307 0.501 2308 0.501 2309 0.500 2310 0.500 2311 0.500 2312 0.500 2313 0.500 2314 0.500 2315 0.500 2316 0.500 2317 0.500 2318 0.500 2319 0.500 2320 0.500 2321 0.500 2322 0.500 2323 0.953 2324 0.920 2325 0.889 2326 0.860 2327 0.833 2328 0.821 2329 0.832 2330 0.849 2331 0.872 2332 0.900 2333 0.908 2334 0.885 2335 0.865 2336 0.846 2337 0.844 2338 0.872 2339 0.901 2340 0.933 2341 0.944 2342 0.912 2343 0.883 2344 0.856 2345 0.831 2346 0.808 2347 0.791 2348 0.774 2349 0.766 2350 0.786 2351 0.808 2352 0.831 2353 0.860 2354 0.891 2355 0.924 2356 0.959 2357 0.997 2358 0.963 2359 0.927 2360 0.893 2361 0.861 2362 0.832 2363 0.805 2364 0.779 2365 0.755 2366 0.733 2367 0.712 2368 0.693 2369 0.674 2370 0.657 2371 0.641 2372 0.626 2373 0.612 2374 0.599 2375 0.587 2376 0.575 2377 0.564 2378 0.554 2379 0.545 2380 0.537 2381 0.530 2382 0.525 2383 0.522 2384 0.518 2385 0.515 2386 0.512 2387 0.509 2388 0.507 2389 0.505 2390 0.504 2391 0.503 2392 0.503 2393 0.502 2394 0.501 2395 0.501 2396 0.500 2397 0.500 2398 0.500 2399 0.500 2400 0.500 2401 0.500 2402 0.500 2403 0.500 2404 0.500 2405 0.500 2406 0.500 2407 0.500 2408 0.500 2409 0.500 2410 0.500 2411 0.500 2412 0.500 2413 0.500 2414 0.500 2415 0.500 2416 0.500 2417 0.500 2418 0.500 2419 0.500 2420 0.500 2421 0.499 2422 0.499 2423 0.499 2424 1.000 2425 0.961 2426 0.960 2427 0.997 2428 0.952 2429 0.912 2430 0.897 2431 0.907 2432 0.935 2433 0.974 2434 0.989 2435 0.965 2436 0.991 2437 0.989 2438 0.959 2439 0.979 2440 0.981 2441 0.978 2442 0.958 2443 0.998 2444 0.964 2445 0.954 2446 0.999 2447 0.964 2448 0.948 2449 0.952 2450 0.938 2451 0.904 2452 0.894 2453 0.915 2454 0.954 2455 0.997 2456 0.959 2457 0.923 2458 0.890 2459 0.858 2460 0.829 2461 0.802 2462 0.777 2463 0.753 2464 0.731 2465 0.711 2466 0.692 2467 0.675 2468 0.661 2469 0.647 2470 0.636 2471 0.626 2472 0.616 2473 0.607 2474 0.599 2475 0.592 2476 0.585 2477 0.578 2478 0.572 2479 0.565 2480 0.560 2481 0.554 2482 0.550 2483 0.545 2484 0.541 2485 0.537 2486 0.534 2487 0.531 2488 0.528 2489 0.525 2490 0.523 2491 0.520 2492 0.519 2493 0.517 2494 0.515 2495 0.514 2496 0.512 2497 0.511 2498 0.510 2499 0.509 2500 0.508 2501 0.507 2502 0.506 2503 0.505 2504 0.504 2505 0.503 2506 0.503 2507 0.502 2508 0.501 2509 0.500 2510 0.500 2511 0.499 2512 0.498 2513 0.498 2514 0.498 2515 0.498 2516 0.498 2517 0.498 2518 0.498 2519 0.498 2520 0.498 2521 0.498 2522 0.498 2523 0.498 2524 0.498 2525 0.832 2526 0.861 2527 0.892 2528 0.925 2529 0.961 2530 0.999 2531 0.955 2532 0.924 2533 0.935 2534 0.981 2535 0.965 2536 0.962 2537 0.997 2538 0.951 2539 0.940 2540 0.978 2541 0.980 2542 0.939 2543 0.906 2544 0.920 2545 0.960 2546 0.995 2547 0.973 2548 0.973 2549 0.956 2550 0.971 2551 0.992 2552 0.973 2553 0.970 2554 0.978 2555 0.986 2556 0.975 2557 0.939 2558 0.904 2559 0.873 2560 0.843 2561 0.816 2562 0.790 2563 0.767 2564 0.745 2565 0.725 2566 0.706 2567 0.690 2568 0.675 2569 0.662 2570 0.649 2571 0.638 2572 0.628 2573 0.619 2574 0.611 2575 0.603 2576 0.596 2577 0.589 2578 0.583 2579 0.577 2580 0.571 2581 0.566 2582 0.561 2583 0.556 2584 0.551 2585 0.547 2586 0.543 2587 0.540 2588 0.536 2589 0.533 2590 0.530 2591 0.528 2592 0.525 2593 0.523 2594 0.520 2595 0.518 2596 0.516 2597 0.515 2598 0.513 2599 0.512 2600 0.510 2601 0.509 2602 0.508 2603 0.507 2604 0.506 2605 0.505 2606 0.504 2607 0.503 2608 0.502 2609 0.501 2610 0.500 2611 0.499 2612 0.499 2613 0.499 2614 0.499 2615 0.499 2616 0.499 2617 0.499 2618 0.499 2619 0.499 2620 0.499 2621 0.499 2622 0.499 2623 0.499 2624 0.499 2625 0.499 2626 0.997 2627 0.963 2628 0.931 2629 0.901 2630 0.872 2631 0.860 2632 0.886 2633 0.914 2634 0.943 2635 0.976 2636 0.964 2637 0.925 2638 0.889 2639 0.862 2640 0.866 2641 0.898 2642 0.937 2643 0.979 2644 0.981 2645 0.956 2646 0.983 2647 0.978 2648 0.935 2649 0.895 2650 0.857 2651 0.821 2652 0.837 2653 0.873 2654 0.912 2655 0.954 2656 0.997 2657 0.972 2658 0.945 2659 0.923 2660 0.943 2661 0.970 2662 0.998 2663 0.962 2664 0.926 2665 0.893 2666 0.861 2667 0.832 2668 0.805 2669 0.779 2670 0.755 2671 0.733 2672 0.712 2673 0.693 2674 0.675 2675 0.658 2676 0.642 2677 0.627 2678 0.613 2679 0.600 2680 0.588 2681 0.577 2682 0.568 2683 0.560 2684 0.553 2685 0.547 2686 0.542 2687 0.537 2688 0.533 2689 0.529 2690 0.526 2691 0.522 2692 0.519 2693 0.517 2694 0.514 2695 0.512 2696 0.511 2697 0.509 2698 0.508 2699 0.507 2700 0.506 2701 0.505 2702 0.504 2703 0.504 2704 0.503 2705 0.502 2706 0.502 2707 0.501 2708 0.500 2709 0.500 2710 0.500 2711 0.500 2712 0.500 2713 0.500 2714 0.500 2715 0.500 2716 0.499 2717 0.499 2718 0.499 2719 0.499 2720 0.499 2721 0.499 2722 0.499 2723 0.499 2724 0.499 2725 0.499 2726 0.499 2727 1.000 2728 0.986 2729 0.947 2730 0.910 2731 0.928 2732 0.967 2733 0.984 2734 0.941 2735 0.902 2736 0.867 2737 0.856 2738 0.891 2739 0.930 2740 0.972 2741 0.983 2742 0.951 2743 0.982 2744 0.980 2745 0.943 2746 0.908 2747 0.878 2748 0.858 2749 0.882 2750 0.912 2751 0.946 2752 0.983 2753 0.977 2754 0.940 2755 0.905 2756 0.873 2757 0.843 2758 0.815 2759 0.789 2760 0.764 2761 0.741 2762 0.720 2763 0.700 2764 0.681 2765 0.664 2766 0.647 2767 0.632 2768 0.618 2769 0.604 2770 0.592 2771 0.580 2772 0.571 2773 0.562 2774 0.555 2775 0.549 2776 0.543 2777 0.539 2778 0.535 2779 0.531 2780 0.527 2781 0.523 2782 0.520 2783 0.517 2784 0.515 2785 0.512 2786 0.510 2787 0.509 2788 0.507 2789 0.506 2790 0.505 2791 0.505 2792 0.504 2793 0.503 2794 0.502 2795 0.502 2796 0.501 2797 0.500 2798 0.500 2799 0.500 2800 0.500 2801 0.500 2802 0.500 2803 0.500 2804 0.500 2805 0.499 2806 0.499 2807 0.499 2808 0.499 2809 0.499 2810 0.499 2811 0.499 2812 0.499 2813 0.499 2814 0.499 2815 0.499 2816 0.499 2817 0.499 2818 0.499 2819 0.499 2820 0.499 2821 0.499 2822 0.499 2823 0.499 2824 0.499 2825 0.499 2826 0.499 2827 0.499 2828 1.000 2829 0.957 2830 0.994 2831 0.960 2832 0.919 2833 0.902 2834 0.940 2835 0.983 2836 0.971 2837 0.930 2838 0.893 2839 0.884 2840 0.921 2841 0.962 2842 0.983 2843 0.962 2844 0.981 2845 0.974 2846 0.942 2847 0.915 2848 0.908 2849 0.940 2850 0.975 2851 0.988 2852 0.956 2853 0.959 2854 0.991 2855 0.970 2856 0.934 2857 0.899 2858 0.867 2859 0.837 2860 0.809 2861 0.783 2862 0.758 2863 0.735 2864 0.714 2865 0.694 2866 0.675 2867 0.657 2868 0.640 2869 0.625 2870 0.610 2871 0.596 2872 0.583 2873 0.571 2874 0.560 2875 0.549 2876 0.539 2877 0.531 2878 0.523 2879 0.517 2880 0.513 2881 0.509 2882 0.506 2883 0.503 2884 0.501 2885 0.500 2886 0.498 2887 0.498 2888 0.497 2889 0.497 2890 0.496 2891 0.496 2892 0.496 2893 0.496 2894 0.496 2895 0.497 2896 0.497 2897 0.497 2898 0.497 2899 0.497 2900 0.497 2901 0.498 2902 0.498 2903 0.498 2904 0.498 2905 0.498 2906 0.498 2907 0.498 2908 0.498 2909 0.498 2910 0.499 2911 0.499 2912 0.499 2913 0.499 2914 0.499 2915 0.499 2916 0.499 2917 0.499 2918 0.499 2919 0.499 2920 0.499 2921 0.499 2922 0.499 2923 0.499 2924 0.499 2925 0.499 2926 0.499 2927 0.499 2928 0.499 2929 1.000 2930 0.956 2931 0.914 2932 0.876 2933 0.839 2934 0.805 2935 0.786 2936 0.786 2937 0.809 2938 0.840 2939 0.875 2940 0.911 2941 0.950 2942 0.944 2943 0.914 2944 0.890 2945 0.875 2946 0.885 2947 0.915 2948 0.949 2949 0.986 2950 0.971 2951 0.933 2952 0.940 2953 0.980 2954 0.968 2955 0.927 2956 0.890 2957 0.901 2958 0.937 2959 0.977 2960 0.981 2961 0.944 2962 0.909 2963 0.876 2964 0.846 2965 0.818 2966 0.792 2967 0.767 2968 0.744 2969 0.723 2970 0.703 2971 0.684 2972 0.667 2973 0.651 2974 0.635 2975 0.623 2976 0.611 2977 0.600 2978 0.591 2979 0.583 2980 0.577 2981 0.571 2982 0.565 2983 0.560 2984 0.556 2985 0.551 2986 0.547 2987 0.542 2988 0.539 2989 0.535 2990 0.532 2991 0.529 2992 0.526 2993 0.523 2994 0.521 2995 0.519 2996 0.517 2997 0.515 2998 0.514 2999 0.512 3000 0.511 3001 0.510 3002 0.509 3003 0.508 3004 0.507 3005 0.506 3006 0.505 3007 0.504 3008 0.503 3009 0.502 3010 0.502 3011 0.501 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0.000670 99.500000 29.000000 0.000580 99.500000 29.500000 0.000500 99.500000 30.000000 0.000430 99.500000 30.500000 0.000370 99.500000 31.000000 0.000310 99.500000 31.500000 0.000270 99.500000 32.000000 0.000230 99.500000 32.500000 0.000190 99.500000 33.000000 0.000160 99.500000 33.500000 0.000140 99.500000 34.000000 0.000110 99.500000 34.500000 0.000100 99.500000 35.000000 0.000080 99.500000 35.500000 0.000070 99.500000 36.000000 0.000050 99.500000 36.500000 0.000040 99.500000 37.000000 0.000040 99.500000 37.500000 0.000030 99.500000 38.000000 0.000020 99.500000 38.500000 0.000020 99.500000 39.000000 0.000010 99.500000 39.500000 0.000010 99.500000 40.000000 0.000010 99.500000 40.500000 0.000010 qtl/inst/contrib/bin/test/regression/t34out.txt0000644000175100001440000000012212422233634021313 0ustar hornikusersINFO: EXIT ERROR: Augmentation parameter conflict max_augmentation <= individuals qtl/inst/contrib/bin/test/regression/t12out.txt0000644000175100001440000004564412422233634021331 0ustar hornikusersINFO: Augmentation routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:F: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=1.000000 INFO: Done with augmentation INFO: Prob=0.021 Alfa=0.020000 INFO: Prob=0.020 Alfa=0.020000 INFO: dimX:12 nInd:180 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,168)=0.020000 INFO: F(4.003,2,168)=0.020000 INFO: Log-likelihood of full model= -8833.613 INFO: Residual variance= 26378204.403 INFO: Trait mean= 4327.088; Trait variation= 28972781.109 INFO: Marker 15 is dropped, resulting in reduced model logL = -8833.669 INFO: Marker 42 is dropped, resulting in reduced model logL = -8833.883 INFO: Marker 23 is dropped, resulting in reduced model logL = -8834.653 INFO: Marker 54 is dropped, resulting in reduced model logL = -8835.353 INFO: Marker 48 is dropped, resulting in reduced model logL = -8835.779 INFO: Marker 36 is dropped, resulting in reduced model logL = -8836.631 INFO: Marker 35 is dropped, resulting in reduced model logL = -8836.787 INFO: Marker 7 is dropped, resulting in reduced model logL = -8837.772 INFO: Marker 60 is dropped, resulting in reduced model logL = -8840.226 INFO: Marker 66 is dropped, resulting in reduced model logL = -8843.804 INFO: Number of output datapoints: 505 0 0.794 1 0.853 2 0.816 3 0.699 4 0.572 5 0.422 6 0.348 7 0.511 8 0.769 9 0.840 10 0.851 11 0.816 12 0.719 13 0.463 14 0.254 15 0.246 16 0.231 17 0.247 18 0.254 19 0.248 20 0.230 21 0.209 22 0.213 23 0.210 24 0.190 25 0.158 26 0.121 27 0.070 28 0.027 29 0.003 30 0.004 31 0.050 32 0.031 33 0.003 34 0.006 35 0.071 36 0.184 37 0.248 38 0.215 39 0.157 40 0.089 41 0.029 42 0.029 43 0.024 44 0.000 45 0.005 46 0.048 47 0.124 48 0.205 49 0.277 50 0.318 51 0.321 52 0.457 53 0.605 54 0.767 55 0.800 56 0.655 57 0.356 58 0.107 59 1.199 60 0.740 61 0.352 62 0.183 63 0.056 64 0.053 65 0.053 66 0.053 67 0.054 68 0.054 69 0.055 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Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=0.500000 INFO: Done with augmentation INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.021 Alfa=0.020000 INFO: dimX:1 nInd:120 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.566,1,119)=0.020000 INFO: F(4.003,2,119)=0.020000 INFO: Log-likelihood of full model= -9793.248 INFO: Residual variance= 6631.434 INFO: Trait mean= 148.703; Trait variation= 6631.434 INFO: Number of output datapoints: 2020 0 0.218 1 0.361 2 0.331 3 0.296 4 0.258 5 0.216 6 0.174 7 0.134 8 0.097 9 0.066 10 0.040 11 0.022 12 0.010 13 0.006 14 0.006 15 0.005 16 0.005 17 0.004 18 0.003 19 0.002 20 0.002 21 0.001 22 0.018 23 0.052 24 0.099 25 0.149 26 0.102 27 0.103 28 0.142 29 0.187 30 0.237 31 0.290 32 0.341 33 0.389 34 0.432 35 0.469 36 0.766 37 1.099 38 1.451 39 1.776 40 2.041 41 2.074 42 1.757 43 1.572 44 1.575 45 1.547 46 1.488 47 1.396 48 1.389 49 1.382 50 1.374 51 1.365 52 1.355 53 1.343 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1872 0.000 1873 0.000 1874 0.000 1875 0.000 1876 0.000 1877 0.000 1878 0.000 1879 0.000 1880 0.000 1881 0.000 1882 0.000 1883 0.000 1884 0.000 1885 0.000 1886 0.000 1887 0.000 1888 0.000 1889 0.000 1890 0.000 1891 0.000 1892 0.000 1893 0.000 1894 0.000 1895 0.000 1896 0.000 1897 0.000 1898 0.000 1899 0.000 1900 0.000 1901 0.000 1902 0.000 1903 0.000 1904 0.000 1905 0.000 1906 0.000 1907 0.000 1908 0.000 1909 0.000 1910 0.000 1911 0.000 1912 0.000 1913 0.000 1914 0.000 1915 0.000 1916 0.000 1917 0.000 1918 0.000 1919 0.602 1920 0.586 1921 0.566 1922 0.542 1923 0.514 1924 0.482 1925 0.446 1926 0.407 1927 0.365 1928 0.321 1929 0.277 1930 0.233 1931 0.191 1932 0.152 1933 0.117 1934 0.087 1935 0.062 1936 0.042 1937 0.027 1938 0.015 1939 0.008 1940 0.003 1941 0.002 1942 0.002 1943 0.002 1944 0.002 1945 0.002 1946 0.002 1947 0.002 1948 0.002 1949 0.002 1950 0.002 1951 0.002 1952 0.002 1953 0.002 1954 0.002 1955 0.002 1956 0.002 1957 0.002 1958 0.002 1959 0.002 1960 0.002 1961 0.002 1962 0.002 1963 0.002 1964 0.002 1965 0.002 1966 0.002 1967 0.002 1968 0.002 1969 0.002 1970 0.002 1971 0.002 1972 0.002 1973 0.002 1974 0.002 1975 0.002 1976 0.001 1977 0.001 1978 0.001 1979 0.001 1980 0.001 1981 0.001 1982 0.001 1983 0.001 1984 0.001 1985 0.001 1986 0.001 1987 0.001 1988 0.001 1989 0.001 1990 0.001 1991 0.001 1992 0.001 1993 0.001 1994 0.001 1995 0.001 1996 0.001 1997 0.000 1998 0.000 1999 0.000 2000 0.000 2001 0.000 2002 0.000 2003 0.000 2004 0.000 2005 0.000 2006 0.000 2007 0.000 2008 0.000 2009 0.000 2010 0.000 2011 0.000 2012 0.000 2013 0.000 2014 0.000 2015 0.000 2016 0.000 2017 0.000 2018 0.000 2019 0.000 2020 1.000 2021 0.978 2022 0.937 2023 0.898 2024 0.862 2025 0.830 2026 0.802 2027 0.800 2028 0.829 2029 0.862 2030 0.899 2031 0.939 2032 0.981 2033 0.972 2034 0.927 2035 0.884 2036 0.848 2037 0.861 2038 0.900 2039 0.943 2040 0.990 2041 0.965 2042 0.924 2043 0.917 2044 0.957 2045 0.999 2046 0.980 2047 0.973 2048 0.931 2049 0.892 2050 0.854 2051 0.844 2052 0.878 2053 0.916 2054 0.955 2055 0.997 2056 0.971 2057 0.927 2058 0.922 2059 0.951 2060 0.988 2061 0.986 2062 0.974 2063 0.977 2064 0.937 2065 0.943 2066 0.984 2067 0.992 2068 0.954 2069 0.919 2070 0.886 2071 0.855 2072 0.827 2073 0.800 2074 0.775 2075 0.752 2076 0.730 2077 0.710 2078 0.692 2079 0.675 2080 0.660 2081 0.647 2082 0.635 2083 0.623 2084 0.613 2085 0.604 2086 0.596 2087 0.588 2088 0.581 2089 0.575 2090 0.569 2091 0.563 2092 0.557 2093 0.552 2094 0.547 2095 0.543 2096 0.538 2097 0.534 2098 0.531 2099 0.528 2100 0.525 2101 0.522 2102 0.520 2103 0.518 2104 0.516 2105 0.514 2106 0.512 2107 0.510 2108 0.509 2109 0.508 2110 0.507 2111 0.507 2112 0.506 2113 0.505 2114 0.504 2115 0.504 2116 0.503 2117 0.502 2118 0.502 2119 0.501 2120 0.500 2121 1.000 2122 0.956 2123 0.915 2124 0.877 2125 0.842 2126 0.812 2127 0.786 2128 0.773 2129 0.783 2130 0.810 2131 0.843 2132 0.879 2133 0.917 2134 0.957 2135 0.998 2136 0.954 2137 0.913 2138 0.875 2139 0.841 2140 0.827 2141 0.853 2142 0.890 2143 0.931 2144 0.974 2145 0.980 2146 0.939 2147 0.900 2148 0.864 2149 0.831 2150 0.827 2151 0.858 2152 0.894 2153 0.932 2154 0.972 2155 0.984 2156 0.943 2157 0.908 2158 0.931 2159 0.970 2160 0.985 2161 0.938 2162 0.899 2163 0.877 2164 0.896 2165 0.936 2166 0.980 2167 0.977 2168 0.940 2169 0.906 2170 0.873 2171 0.843 2172 0.815 2173 0.789 2174 0.765 2175 0.742 2176 0.721 2177 0.701 2178 0.683 2179 0.666 2180 0.650 2181 0.636 2182 0.623 2183 0.612 2184 0.602 2185 0.593 2186 0.585 2187 0.578 2188 0.572 2189 0.566 2190 0.561 2191 0.556 2192 0.551 2193 0.546 2194 0.542 2195 0.538 2196 0.534 2197 0.531 2198 0.528 2199 0.525 2200 0.522 2201 0.520 2202 0.518 2203 0.516 2204 0.514 2205 0.512 2206 0.511 2207 0.510 2208 0.508 2209 0.508 2210 0.507 2211 0.506 2212 0.505 2213 0.504 2214 0.504 2215 0.503 2216 0.502 2217 0.502 2218 0.501 2219 0.500 2220 0.500 2221 0.499 2222 1.000 2223 0.959 2224 0.920 2225 0.884 2226 0.851 2227 0.820 2228 0.791 2229 0.765 2230 0.746 2231 0.755 2232 0.781 2233 0.809 2234 0.840 2235 0.874 2236 0.910 2237 0.949 2238 0.989 2239 0.963 2240 0.916 2241 0.879 2242 0.911 2243 0.955 2244 0.998 2245 0.956 2246 0.918 2247 0.883 2248 0.887 2249 0.923 2250 0.965 2251 0.990 2252 0.947 2253 0.973 2254 0.979 2255 0.931 2256 0.931 2257 0.979 2258 0.977 2259 0.940 2260 0.905 2261 0.873 2262 0.843 2263 0.815 2264 0.789 2265 0.765 2266 0.742 2267 0.721 2268 0.701 2269 0.682 2270 0.665 2271 0.649 2272 0.634 2273 0.620 2274 0.606 2275 0.594 2276 0.582 2277 0.571 2278 0.562 2279 0.553 2280 0.546 2281 0.540 2282 0.535 2283 0.530 2284 0.526 2285 0.522 2286 0.519 2287 0.515 2288 0.513 2289 0.510 2290 0.508 2291 0.506 2292 0.505 2293 0.504 2294 0.504 2295 0.503 2296 0.503 2297 0.502 2298 0.502 2299 0.502 2300 0.501 2301 0.501 2302 0.501 2303 0.501 2304 0.501 2305 0.501 2306 0.500 2307 0.500 2308 0.500 2309 0.500 2310 0.500 2311 0.500 2312 0.500 2313 0.500 2314 0.500 2315 0.500 2316 0.500 2317 0.500 2318 0.500 2319 0.500 2320 0.500 2321 0.500 2322 0.500 2323 1.000 2324 0.953 2325 0.909 2326 0.868 2327 0.830 2328 0.816 2329 0.846 2330 0.885 2331 0.927 2332 0.972 2333 0.982 2334 0.943 2335 0.905 2336 0.870 2337 0.859 2338 0.895 2339 0.932 2340 0.972 2341 0.985 2342 0.943 2343 0.904 2344 0.868 2345 0.834 2346 0.802 2347 0.777 2348 0.754 2349 0.740 2350 0.762 2351 0.786 2352 0.812 2353 0.844 2354 0.879 2355 0.915 2356 0.955 2357 0.997 2358 0.963 2359 0.927 2360 0.893 2361 0.861 2362 0.832 2363 0.805 2364 0.779 2365 0.755 2366 0.733 2367 0.712 2368 0.693 2369 0.674 2370 0.657 2371 0.641 2372 0.626 2373 0.612 2374 0.599 2375 0.587 2376 0.575 2377 0.564 2378 0.554 2379 0.545 2380 0.537 2381 0.530 2382 0.525 2383 0.522 2384 0.518 2385 0.515 2386 0.512 2387 0.509 2388 0.507 2389 0.505 2390 0.504 2391 0.503 2392 0.503 2393 0.502 2394 0.501 2395 0.501 2396 0.500 2397 0.500 2398 0.500 2399 0.500 2400 0.500 2401 0.500 2402 0.500 2403 0.500 2404 0.500 2405 0.500 2406 0.500 2407 0.500 2408 0.500 2409 0.500 2410 0.500 2411 0.500 2412 0.500 2413 0.500 2414 0.500 2415 0.500 2416 0.500 2417 0.500 2418 0.500 2419 0.500 2420 0.500 2421 0.499 2422 0.499 2423 0.499 2424 1.000 2425 0.961 2426 0.960 2427 0.997 2428 0.952 2429 0.912 2430 0.897 2431 0.907 2432 0.935 2433 0.974 2434 0.989 2435 0.965 2436 0.991 2437 0.989 2438 0.959 2439 0.979 2440 0.985 2441 0.978 2442 0.958 2443 0.998 2444 0.964 2445 0.954 2446 0.999 2447 0.959 2448 0.951 2449 0.980 2450 0.977 2451 0.936 2452 0.910 2453 0.920 2454 0.955 2455 0.997 2456 0.959 2457 0.923 2458 0.890 2459 0.858 2460 0.829 2461 0.802 2462 0.777 2463 0.753 2464 0.731 2465 0.711 2466 0.692 2467 0.675 2468 0.661 2469 0.647 2470 0.636 2471 0.626 2472 0.616 2473 0.607 2474 0.599 2475 0.592 2476 0.585 2477 0.578 2478 0.572 2479 0.565 2480 0.560 2481 0.554 2482 0.550 2483 0.545 2484 0.541 2485 0.537 2486 0.534 2487 0.531 2488 0.528 2489 0.525 2490 0.523 2491 0.520 2492 0.519 2493 0.517 2494 0.515 2495 0.514 2496 0.512 2497 0.511 2498 0.510 2499 0.509 2500 0.508 2501 0.507 2502 0.506 2503 0.505 2504 0.504 2505 0.503 2506 0.503 2507 0.502 2508 0.501 2509 0.500 2510 0.500 2511 0.499 2512 0.498 2513 0.498 2514 0.498 2515 0.498 2516 0.498 2517 0.498 2518 0.498 2519 0.498 2520 0.498 2521 0.498 2522 0.498 2523 0.498 2524 0.498 2525 0.832 2526 0.861 2527 0.892 2528 0.925 2529 0.961 2530 0.999 2531 0.957 2532 0.930 2533 0.952 2534 0.995 2535 0.968 2536 0.957 2537 0.997 2538 0.951 2539 0.940 2540 0.978 2541 0.980 2542 0.939 2543 0.906 2544 0.920 2545 0.960 2546 0.995 2547 0.976 2548 0.983 2549 0.990 2550 0.973 2551 0.991 2552 0.973 2553 0.986 2554 0.980 2555 0.986 2556 0.975 2557 0.939 2558 0.904 2559 0.873 2560 0.843 2561 0.816 2562 0.790 2563 0.767 2564 0.745 2565 0.725 2566 0.706 2567 0.690 2568 0.675 2569 0.662 2570 0.649 2571 0.638 2572 0.628 2573 0.619 2574 0.611 2575 0.603 2576 0.596 2577 0.589 2578 0.583 2579 0.577 2580 0.571 2581 0.566 2582 0.561 2583 0.556 2584 0.551 2585 0.547 2586 0.543 2587 0.540 2588 0.536 2589 0.533 2590 0.530 2591 0.528 2592 0.525 2593 0.523 2594 0.520 2595 0.518 2596 0.516 2597 0.515 2598 0.513 2599 0.512 2600 0.510 2601 0.509 2602 0.508 2603 0.507 2604 0.506 2605 0.505 2606 0.504 2607 0.503 2608 0.502 2609 0.501 2610 0.500 2611 0.499 2612 0.499 2613 0.499 2614 0.499 2615 0.499 2616 0.499 2617 0.499 2618 0.499 2619 0.499 2620 0.499 2621 0.499 2622 0.499 2623 0.499 2624 0.499 2625 0.499 2626 1.000 2627 0.955 2628 0.914 2629 0.874 2630 0.839 2631 0.825 2632 0.859 2633 0.897 2634 0.938 2635 0.982 2636 0.973 2637 0.933 2638 0.895 2639 0.865 2640 0.868 2641 0.900 2642 0.938 2643 0.980 2644 0.979 2645 0.948 2646 0.980 2647 0.979 2648 0.938 2649 0.899 2650 0.862 2651 0.828 2652 0.843 2653 0.878 2654 0.915 2655 0.955 2656 0.997 2657 0.959 2658 0.920 2659 0.887 2660 0.916 2661 0.955 2662 0.998 2663 0.962 2664 0.926 2665 0.893 2666 0.861 2667 0.832 2668 0.804 2669 0.779 2670 0.755 2671 0.733 2672 0.712 2673 0.692 2674 0.674 2675 0.657 2676 0.641 2677 0.626 2678 0.612 2679 0.599 2680 0.586 2681 0.575 2682 0.564 2683 0.554 2684 0.546 2685 0.539 2686 0.534 2687 0.529 2688 0.525 2689 0.521 2690 0.518 2691 0.515 2692 0.512 2693 0.510 2694 0.508 2695 0.507 2696 0.505 2697 0.505 2698 0.504 2699 0.503 2700 0.502 2701 0.502 2702 0.501 2703 0.500 2704 0.500 2705 0.500 2706 0.500 2707 0.500 2708 0.500 2709 0.500 2710 0.499 2711 0.499 2712 0.499 2713 0.499 2714 0.499 2715 0.499 2716 0.499 2717 0.499 2718 0.499 2719 0.499 2720 0.499 2721 0.499 2722 0.499 2723 0.499 2724 0.499 2725 0.499 2726 0.499 2727 1.000 2728 0.986 2729 0.948 2730 0.911 2731 0.930 2732 0.970 2733 0.988 2734 0.948 2735 0.912 2736 0.880 2737 0.871 2738 0.901 2739 0.937 2740 0.976 2741 0.980 2742 0.936 2743 0.977 2744 0.977 2745 0.935 2746 0.895 2747 0.860 2748 0.833 2749 0.863 2750 0.899 2751 0.938 2752 0.980 2753 0.977 2754 0.940 2755 0.905 2756 0.873 2757 0.843 2758 0.815 2759 0.789 2760 0.764 2761 0.741 2762 0.720 2763 0.700 2764 0.681 2765 0.664 2766 0.647 2767 0.632 2768 0.618 2769 0.604 2770 0.591 2771 0.580 2772 0.569 2773 0.558 2774 0.549 2775 0.541 2776 0.534 2777 0.529 2778 0.525 2779 0.521 2780 0.518 2781 0.514 2782 0.511 2783 0.509 2784 0.507 2785 0.505 2786 0.504 2787 0.503 2788 0.503 2789 0.502 2790 0.501 2791 0.500 2792 0.500 2793 0.500 2794 0.500 2795 0.500 2796 0.500 2797 0.500 2798 0.500 2799 0.500 2800 0.500 2801 0.500 2802 0.500 2803 0.500 2804 0.500 2805 0.500 2806 0.500 2807 0.500 2808 0.500 2809 0.500 2810 0.500 2811 0.500 2812 0.500 2813 0.500 2814 0.500 2815 0.500 2816 0.500 2817 0.500 2818 0.500 2819 0.500 2820 0.499 2821 0.499 2822 0.499 2823 0.499 2824 0.499 2825 0.499 2826 0.499 2827 0.499 2828 1.000 2829 0.949 2830 0.993 2831 0.960 2832 0.919 2833 0.902 2834 0.940 2835 0.983 2836 0.972 2837 0.931 2838 0.895 2839 0.888 2840 0.927 2841 0.970 2842 0.986 2843 0.947 2844 0.980 2845 0.978 2846 0.938 2847 0.902 2848 0.896 2849 0.932 2850 0.972 2851 0.986 2852 0.947 2853 0.950 2854 0.989 2855 0.970 2856 0.934 2857 0.899 2858 0.867 2859 0.837 2860 0.809 2861 0.783 2862 0.759 2863 0.736 2864 0.715 2865 0.695 2866 0.676 2867 0.658 2868 0.642 2869 0.626 2870 0.612 2871 0.598 2872 0.585 2873 0.573 2874 0.562 2875 0.552 2876 0.542 2877 0.532 2878 0.524 2879 0.516 2880 0.511 2881 0.507 2882 0.503 2883 0.501 2884 0.499 2885 0.498 2886 0.497 2887 0.497 2888 0.497 2889 0.497 2890 0.497 2891 0.497 2892 0.497 2893 0.498 2894 0.498 2895 0.498 2896 0.498 2897 0.498 2898 0.498 2899 0.498 2900 0.498 2901 0.498 2902 0.499 2903 0.499 2904 0.499 2905 0.499 2906 0.499 2907 0.499 2908 0.499 2909 0.499 2910 0.499 2911 0.499 2912 0.499 2913 0.499 2914 0.499 2915 0.499 2916 0.499 2917 0.499 2918 0.499 2919 0.499 2920 0.499 2921 0.499 2922 0.499 2923 0.499 2924 0.499 2925 0.499 2926 0.499 2927 0.499 2928 0.499 2929 1.000 2930 0.957 2931 0.917 2932 0.880 2933 0.845 2934 0.813 2935 0.800 2936 0.809 2937 0.837 2938 0.871 2939 0.906 2940 0.944 2941 0.984 2942 0.973 2943 0.932 2944 0.896 2945 0.868 2946 0.871 2947 0.904 2948 0.942 2949 0.984 2950 0.968 2951 0.924 2952 0.933 2953 0.981 2954 0.971 2955 0.929 2956 0.891 2957 0.900 2958 0.936 2959 0.977 2960 0.981 2961 0.944 2962 0.909 2963 0.876 2964 0.846 2965 0.818 2966 0.792 2967 0.767 2968 0.744 2969 0.723 2970 0.703 2971 0.684 2972 0.667 2973 0.651 2974 0.635 2975 0.623 2976 0.611 2977 0.600 2978 0.591 2979 0.583 2980 0.577 2981 0.571 2982 0.565 2983 0.560 2984 0.556 2985 0.551 2986 0.547 2987 0.542 2988 0.539 2989 0.535 2990 0.532 2991 0.529 2992 0.526 2993 0.523 2994 0.521 2995 0.519 2996 0.517 2997 0.515 2998 0.514 2999 0.512 3000 0.511 3001 0.510 3002 0.509 3003 0.508 3004 0.507 3005 0.506 3006 0.505 3007 0.504 3008 0.503 3009 0.502 3010 0.502 3011 0.501 3012 0.500 3013 0.500 3014 0.500 3015 0.500 3016 0.500 3017 0.500 3018 0.499 3019 0.499 3020 0.499 3021 0.499 3022 0.499 3023 0.499 3024 0.499 3025 0.499 3026 0.499 3027 0.499 3028 0.499 3029 0.499 3030 1.000 3031 0.958 3032 0.918 3033 0.881 3034 0.859 3035 0.894 3036 0.933 3037 0.974 3038 0.980 3039 0.937 3040 0.895 3041 0.904 3042 0.946 3043 0.990 3044 0.966 3045 0.926 3046 0.890 3047 0.909 3048 0.947 3049 0.988 3050 0.968 3051 0.996 3052 0.960 3053 0.919 3054 0.881 3055 0.846 3056 0.816 3057 0.808 3058 0.838 3059 0.872 3060 0.909 3061 0.949 3062 0.992 3063 0.967 3064 0.931 3065 0.897 3066 0.865 3067 0.835 3068 0.808 3069 0.782 3070 0.757 3071 0.735 3072 0.714 3073 0.694 3074 0.675 3075 0.658 3076 0.642 3077 0.626 3078 0.612 3079 0.599 3080 0.586 3081 0.574 3082 0.563 3083 0.553 3084 0.543 3085 0.534 3086 0.527 3087 0.521 3088 0.516 3089 0.512 3090 0.509 3091 0.507 3092 0.504 3093 0.502 3094 0.501 3095 0.501 3096 0.500 3097 0.499 3098 0.499 3099 0.499 3100 0.499 3101 0.499 3102 0.499 3103 0.499 3104 0.499 3105 0.499 3106 0.499 3107 0.499 3108 0.499 3109 0.499 3110 0.499 3111 0.499 3112 0.499 3113 0.499 3114 0.499 3115 0.499 3116 0.499 3117 0.499 3118 0.499 3119 0.499 3120 0.499 3121 0.499 3122 0.499 3123 0.499 3124 0.499 3125 0.499 3126 0.499 3127 0.499 3128 0.499 3129 0.499 3130 0.499 3131 1.000 3132 0.962 3133 0.959 3134 0.996 3135 0.960 3136 0.920 3137 0.882 3138 0.852 3139 0.886 3140 0.924 3141 0.965 3142 0.991 3143 0.951 3144 0.938 3145 0.977 3146 0.978 3147 0.934 3148 0.896 3149 0.884 3150 0.918 3151 0.959 3152 0.995 3153 0.955 3154 0.917 3155 0.883 3156 0.911 3157 0.950 3158 0.990 3159 0.969 3160 0.933 3161 0.899 3162 0.867 3163 0.837 3164 0.809 3165 0.783 3166 0.758 3167 0.735 3168 0.714 3169 0.694 3170 0.675 3171 0.658 3172 0.641 3173 0.626 3174 0.611 3175 0.597 3176 0.585 3177 0.573 3178 0.561 3179 0.551 3180 0.541 3181 0.534 3182 0.528 3183 0.523 3184 0.518 3185 0.515 3186 0.511 3187 0.508 3188 0.506 3189 0.504 3190 0.502 3191 0.501 3192 0.501 3193 0.500 3194 0.499 3195 0.499 3196 0.498 3197 0.497 3198 0.498 3199 0.498 3200 0.498 3201 0.498 3202 0.498 3203 0.498 3204 0.498 3205 0.498 3206 0.498 3207 0.498 3208 0.498 3209 0.498 3210 0.499 3211 0.499 3212 0.499 3213 0.499 3214 0.499 3215 0.499 3216 0.499 3217 0.499 3218 0.499 3219 0.499 3220 0.499 3221 0.499 3222 0.499 3223 0.499 3224 0.499 3225 0.499 3226 0.499 3227 0.499 3228 0.499 3229 0.499 3230 0.499 3231 0.499 3232 1.000 3233 0.956 3234 0.910 3235 0.912 3236 0.946 3237 0.991 3238 0.971 3239 0.975 3240 0.944 3241 0.978 3242 0.982 3243 0.979 3244 0.982 3245 0.996 3246 0.989 3247 0.968 3248 0.933 3249 0.951 3250 0.999 3251 0.961 3252 0.925 3253 0.891 3254 0.860 3255 0.830 3256 0.803 3257 0.778 3258 0.754 3259 0.731 3260 0.711 3261 0.691 3262 0.673 3263 0.656 3264 0.640 3265 0.626 3266 0.614 3267 0.603 3268 0.593 3269 0.585 3270 0.577 3271 0.570 3272 0.563 3273 0.558 3274 0.553 3275 0.548 3276 0.544 3277 0.539 3278 0.535 3279 0.532 3280 0.528 3281 0.524 3282 0.521 3283 0.518 3284 0.515 3285 0.513 3286 0.510 3287 0.509 3288 0.507 3289 0.506 3290 0.505 3291 0.504 3292 0.503 3293 0.503 3294 0.502 3295 0.502 3296 0.501 3297 0.501 3298 0.500 3299 0.500 3300 0.499 3301 0.499 3302 0.499 3303 0.499 3304 0.499 3305 0.499 3306 0.499 3307 0.499 3308 0.499 3309 0.499 3310 0.499 3311 0.499 3312 0.499 3313 0.499 3314 0.499 3315 0.499 3316 0.499 3317 0.499 3318 0.499 3319 0.499 3320 0.499 3321 0.499 3322 0.499 3323 0.499 3324 0.499 3325 0.499 3326 0.499 3327 0.499 3328 0.499 3329 0.499 3330 0.499 3331 0.499 3332 0.499 3333 1.000 3334 0.958 3335 0.919 3336 0.882 3337 0.848 3338 0.820 3339 0.798 3340 0.822 3341 0.850 3342 0.884 3343 0.921 3344 0.960 3345 0.998 3346 0.958 3347 0.921 3348 0.946 3349 0.984 3350 0.974 3351 0.934 3352 0.897 3353 0.891 3354 0.927 3355 0.967 3356 0.991 3357 0.953 3358 0.917 3359 0.884 3360 0.854 3361 0.825 3362 0.799 3363 0.774 3364 0.751 3365 0.729 3366 0.709 3367 0.690 3368 0.673 3369 0.657 3370 0.641 3371 0.627 3372 0.614 3373 0.601 3374 0.590 3375 0.579 3376 0.569 3377 0.559 3378 0.550 3379 0.542 3380 0.534 3381 0.526 3382 0.520 3383 0.516 3384 0.512 3385 0.510 3386 0.509 3387 0.508 3388 0.508 3389 0.507 3390 0.506 3391 0.506 3392 0.505 3393 0.505 3394 0.505 3395 0.504 3396 0.504 3397 0.503 3398 0.503 3399 0.503 3400 0.503 3401 0.502 3402 0.502 3403 0.502 3404 0.502 3405 0.502 3406 0.501 3407 0.501 3408 0.501 3409 0.501 3410 0.501 3411 0.501 3412 0.501 3413 0.501 3414 0.501 3415 0.500 3416 0.500 3417 0.500 3418 0.500 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5967 0.000 5968 0.000 5969 0.000 5970 0.000 5971 0.000 5972 0.000 5973 0.000 5974 0.000 5975 0.000 5976 0.000 5977 0.000 5978 0.000 5979 0.000 5980 0.000 5981 0.000 5982 0.000 5983 0.000 5984 0.000 5985 0.000 5986 0.000 5987 0.000 5988 0.000 5989 0.000 5990 0.000 5991 0.000 5992 0.000 5993 0.000 5994 0.000 5995 0.000 5996 0.000 5997 0.000 5998 0.000 5999 0.000 qtl/inst/contrib/bin/test/regression/t21out.txt0000644000175100001440000021700612422233634021322 0ustar hornikusersINFO: Augmentation routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:B: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=1.000000 INFO: Done with augmentation INFO: Marker 6 at chr 1 is dropped INFO: Marker 15 at chr 1 is dropped INFO: Marker 16 at chr 1 is dropped INFO: Marker 17 at chr 1 is dropped INFO: Marker 42 at chr 4 is dropped INFO: Marker 48 at chr 4 is dropped INFO: Marker 105 at chr 11 is dropped INFO: Marker 107 at chr 11 is dropped INFO: Marker 111 at chr 11 is dropped INFO: Marker 133 at chr 15 is dropped INFO: Marker 137 at chr 15 is dropped INFO: Marker 139 at chr 15 is dropped INFO: Marker 148 at chr 16 is dropped INFO: Marker 150 at chr 17 is dropped INFO: Marker 151 at chr 17 is dropped INFO: Marker 154 at chr 17 is dropped INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.019 Alfa=0.020000 INFO: dimX:1 nInd:250 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,249)=0.020000 INFO: F(4.003,2,249)=0.020000 INFO: Log-likelihood of full model= -14731.340 INFO: Residual variance= 70.959 INFO: Trait mean= 101.611; Trait variation= 70.959 INFO: Number of output datapoints: 2020 0 0.091 1 0.091 2 0.101 3 0.129 4 0.161 5 0.195 6 0.227 7 0.257 8 0.281 9 0.300 10 0.333 11 0.509 12 0.719 13 0.949 14 1.181 15 1.399 16 1.593 17 1.668 18 2.221 19 2.900 20 2.988 21 3.073 22 3.188 23 3.360 24 3.425 25 3.352 26 3.043 27 2.511 28 2.357 29 2.368 30 2.313 31 2.203 32 2.048 33 2.719 34 3.248 35 2.930 36 3.157 37 3.098 38 3.102 39 3.294 40 3.243 41 2.980 42 2.387 43 1.603 44 1.428 45 1.344 46 1.235 47 1.105 48 1.033 49 0.950 50 0.857 51 0.759 52 0.656 53 0.553 54 0.454 55 0.362 56 0.280 57 0.209 58 0.155 59 0.155 60 0.155 61 0.155 62 0.154 63 0.154 64 0.154 65 0.154 66 0.153 67 0.153 68 0.152 69 0.152 70 0.151 71 0.151 72 0.150 73 0.150 74 0.149 75 0.148 76 0.147 77 0.146 78 0.145 79 0.144 80 0.143 81 0.141 82 0.140 83 0.138 84 0.137 85 0.135 86 0.133 87 0.131 88 0.129 89 0.127 90 0.125 91 0.122 92 0.120 93 0.117 94 0.114 95 0.112 96 0.109 97 0.106 98 0.103 99 0.100 100 0.097 101 0.420 102 0.421 103 0.422 104 0.422 105 0.437 106 0.531 107 0.609 108 0.657 109 0.670 110 0.681 111 0.769 112 0.851 113 0.922 114 0.979 115 1.019 116 1.064 117 1.163 118 1.255 119 1.333 120 1.390 121 1.422 122 1.426 123 1.402 124 1.351 125 1.278 126 1.364 127 1.568 128 1.490 129 1.438 130 1.427 131 1.368 132 1.263 133 1.124 134 0.967 135 0.786 136 0.596 137 0.416 138 0.258 139 0.131 140 0.045 141 0.004 142 0.005 143 0.043 144 0.082 145 0.086 146 0.088 147 0.089 148 0.089 149 0.087 150 0.084 151 0.084 152 0.084 153 0.084 154 0.084 155 0.084 156 0.084 157 0.083 158 0.083 159 0.083 160 0.083 161 0.083 162 0.083 163 0.083 164 0.083 165 0.083 166 0.082 167 0.082 168 0.082 169 0.082 170 0.082 171 0.081 172 0.081 173 0.081 174 0.080 175 0.080 176 0.079 177 0.079 178 0.078 179 0.077 180 0.076 181 0.076 182 0.075 183 0.074 184 0.073 185 0.071 186 0.070 187 0.069 188 0.068 189 0.066 190 0.065 191 0.063 192 0.062 193 0.060 194 0.058 195 0.057 196 0.055 197 0.053 198 0.052 199 0.050 200 0.048 201 0.046 202 0.006 203 0.006 204 0.029 205 0.083 206 0.170 207 0.278 208 0.388 209 0.481 210 0.548 211 0.589 212 0.612 213 0.632 214 0.647 215 0.657 216 0.661 217 0.658 218 0.650 219 0.636 220 0.618 221 0.449 222 0.129 223 0.000 224 0.099 225 0.180 226 0.169 227 0.156 228 0.143 229 0.130 230 0.117 231 0.103 232 0.084 233 0.063 234 0.043 235 0.026 236 0.021 237 0.021 238 0.021 239 0.021 240 0.021 241 0.021 242 0.021 243 0.021 244 0.021 245 0.021 246 0.021 247 0.021 248 0.021 249 0.021 250 0.021 251 0.021 252 0.021 253 0.020 254 0.020 255 0.020 256 0.020 257 0.020 258 0.020 259 0.020 260 0.020 261 0.020 262 0.020 263 0.020 264 0.020 265 0.020 266 0.020 267 0.020 268 0.019 269 0.019 270 0.019 271 0.019 272 0.019 273 0.018 274 0.018 275 0.018 276 0.018 277 0.017 278 0.017 279 0.017 280 0.017 281 0.016 282 0.016 283 0.015 284 0.015 285 0.015 286 0.014 287 0.014 288 0.013 289 0.013 290 0.013 291 0.012 292 0.012 293 0.011 294 0.011 295 0.010 296 0.010 297 0.010 298 0.009 299 0.009 300 0.008 301 0.008 302 0.008 303 1.752 304 2.243 305 2.770 306 3.303 307 3.815 308 4.289 309 4.710 310 5.069 311 5.234 312 6.070 313 6.174 314 6.370 315 5.843 316 5.912 317 5.798 318 7.058 319 4.862 320 4.477 321 3.534 322 3.415 323 3.278 324 3.123 325 2.949 326 2.757 327 2.750 328 2.866 329 2.863 330 2.733 331 2.489 332 2.398 333 2.419 334 2.400 335 2.339 336 2.239 337 2.104 338 1.942 339 1.763 340 1.577 341 1.546 342 1.543 343 1.539 344 1.534 345 1.528 346 1.522 347 1.514 348 1.506 349 1.496 350 1.485 351 1.473 352 1.460 353 1.444 354 1.428 355 1.409 356 1.389 357 1.368 358 1.345 359 1.321 360 1.295 361 1.268 362 1.240 363 1.211 364 1.182 365 1.151 366 1.120 367 1.088 368 1.056 369 1.024 370 0.992 371 0.960 372 0.928 373 0.896 374 0.865 375 0.834 376 0.803 377 0.773 378 0.744 379 0.715 380 0.687 381 0.659 382 0.632 383 0.606 384 0.581 385 0.556 386 0.532 387 0.509 388 0.487 389 0.465 390 0.444 391 0.424 392 0.405 393 0.387 394 0.369 395 0.352 396 0.335 397 0.319 398 0.304 399 0.290 400 0.276 401 0.262 402 0.250 403 0.237 404 0.370 405 0.378 406 0.333 407 0.249 408 0.159 409 0.086 410 0.077 411 0.111 412 0.290 413 0.525 414 0.464 415 0.299 416 0.158 417 0.057 418 0.005 419 0.006 420 0.051 421 0.088 422 0.105 423 0.124 424 0.143 425 0.162 426 0.180 427 0.197 428 0.213 429 0.226 430 0.242 431 0.266 432 0.288 433 0.306 434 0.319 435 0.655 436 1.073 437 1.460 438 1.582 439 1.533 440 1.425 441 1.351 442 1.339 443 1.281 444 1.250 445 1.133 446 1.127 447 1.121 448 1.115 449 1.108 450 1.100 451 1.092 452 1.083 453 1.073 454 1.063 455 1.052 456 1.040 457 1.028 458 1.015 459 1.001 460 0.986 461 0.970 462 0.954 463 0.937 464 0.920 465 0.901 466 0.883 467 0.863 468 0.844 469 0.824 470 0.803 471 0.782 472 0.761 473 0.740 474 0.719 475 0.698 476 0.676 477 0.655 478 0.634 479 0.613 480 0.592 481 0.572 482 0.552 483 0.532 484 0.512 485 0.493 486 0.475 487 0.456 488 0.438 489 0.421 490 0.404 491 0.388 492 0.372 493 0.356 494 0.341 495 0.326 496 0.312 497 0.299 498 0.286 499 0.273 500 0.261 501 0.249 502 0.238 503 0.227 504 0.216 505 0.070 506 0.180 507 0.346 508 0.532 509 0.686 510 0.790 511 0.949 512 1.105 513 1.246 514 1.362 515 1.451 516 1.522 517 1.709 518 1.676 519 1.399 520 1.124 521 1.199 522 1.242 523 1.246 524 1.209 525 1.133 526 1.163 527 1.193 528 1.182 529 1.129 530 1.042 531 0.977 532 0.956 533 0.856 534 0.887 535 0.966 536 0.932 537 0.787 538 0.544 539 0.652 540 0.652 541 0.652 542 0.652 543 0.652 544 0.651 545 0.651 546 0.650 547 0.649 548 0.648 549 0.646 550 0.645 551 0.642 552 0.640 553 0.636 554 0.632 555 0.628 556 0.623 557 0.617 558 0.610 559 0.603 560 0.595 561 0.586 562 0.577 563 0.566 564 0.556 565 0.544 566 0.532 567 0.520 568 0.507 569 0.494 570 0.480 571 0.467 572 0.453 573 0.439 574 0.425 575 0.411 576 0.397 577 0.383 578 0.369 579 0.356 580 0.342 581 0.329 582 0.316 583 0.304 584 0.292 585 0.280 586 0.268 587 0.256 588 0.245 589 0.235 590 0.224 591 0.214 592 0.205 593 0.195 594 0.186 595 0.178 596 0.169 597 0.161 598 0.154 599 0.146 600 0.139 601 0.133 602 0.126 603 0.120 604 0.114 605 0.108 606 0.088 607 0.097 608 0.119 609 0.139 610 0.156 611 0.168 612 0.174 613 0.191 614 0.229 615 0.267 616 0.302 617 0.333 618 0.357 619 0.373 620 0.201 621 0.123 622 0.082 623 0.048 624 0.023 625 0.009 626 0.002 627 0.000 628 0.002 629 0.011 630 0.025 631 0.042 632 0.061 633 0.109 634 0.258 635 0.258 636 0.258 637 0.258 638 0.257 639 0.257 640 0.257 641 0.257 642 0.256 643 0.256 644 0.255 645 0.255 646 0.254 647 0.253 648 0.253 649 0.252 650 0.250 651 0.249 652 0.248 653 0.246 654 0.244 655 0.242 656 0.240 657 0.238 658 0.235 659 0.232 660 0.229 661 0.226 662 0.222 663 0.219 664 0.214 665 0.210 666 0.206 667 0.201 668 0.197 669 0.192 670 0.187 671 0.182 672 0.176 673 0.171 674 0.166 675 0.161 676 0.155 677 0.150 678 0.145 679 0.140 680 0.135 681 0.130 682 0.125 683 0.120 684 0.115 685 0.110 686 0.106 687 0.101 688 0.097 689 0.093 690 0.089 691 0.085 692 0.081 693 0.078 694 0.074 695 0.071 696 0.067 697 0.064 698 0.061 699 0.058 700 0.055 701 0.053 702 0.050 703 0.047 704 0.045 705 0.043 706 0.041 707 0.103 708 0.103 709 0.104 710 0.104 711 0.094 712 0.080 713 0.065 714 0.051 715 0.037 716 0.026 717 0.017 718 0.014 719 0.010 720 0.007 721 0.004 722 0.002 723 0.001 724 0.000 725 0.005 726 0.016 727 0.032 728 0.014 729 0.000 730 0.004 731 0.026 732 0.067 733 0.126 734 0.200 735 0.283 736 0.372 737 0.422 738 0.427 739 0.425 740 0.414 741 0.395 742 0.369 743 0.337 744 0.302 745 0.276 746 0.277 747 0.277 748 0.277 749 0.277 750 0.277 751 0.277 752 0.277 753 0.277 754 0.276 755 0.276 756 0.276 757 0.276 758 0.276 759 0.275 760 0.275 761 0.274 762 0.273 763 0.272 764 0.271 765 0.269 766 0.268 767 0.266 768 0.263 769 0.261 770 0.258 771 0.255 772 0.251 773 0.248 774 0.244 775 0.239 776 0.235 777 0.230 778 0.225 779 0.219 780 0.214 781 0.208 782 0.203 783 0.197 784 0.191 785 0.185 786 0.179 787 0.173 788 0.167 789 0.161 790 0.155 791 0.149 792 0.144 793 0.138 794 0.133 795 0.127 796 0.122 797 0.117 798 0.112 799 0.107 800 0.102 801 0.098 802 0.093 803 0.089 804 0.085 805 0.081 806 0.077 807 0.073 808 0.074 809 0.074 810 0.074 811 0.075 812 0.075 813 0.075 814 0.075 815 0.060 816 0.047 817 0.034 818 0.023 819 0.013 820 0.007 821 0.009 822 0.012 823 0.015 824 0.018 825 0.021 826 0.024 827 0.027 828 0.029 829 0.035 830 0.042 831 0.049 832 0.056 833 0.063 834 0.068 835 0.072 836 0.075 837 0.112 838 0.193 839 0.290 840 0.391 841 0.479 842 0.545 843 0.567 844 0.566 845 0.564 846 0.563 847 0.562 848 0.560 849 0.559 850 0.557 851 0.555 852 0.552 853 0.550 854 0.547 855 0.543 856 0.540 857 0.536 858 0.531 859 0.526 860 0.521 861 0.515 862 0.509 863 0.502 864 0.495 865 0.488 866 0.479 867 0.471 868 0.462 869 0.453 870 0.443 871 0.433 872 0.422 873 0.412 874 0.401 875 0.390 876 0.379 877 0.368 878 0.357 879 0.345 880 0.334 881 0.323 882 0.312 883 0.301 884 0.290 885 0.280 886 0.269 887 0.259 888 0.249 889 0.239 890 0.229 891 0.220 892 0.211 893 0.202 894 0.193 895 0.185 896 0.177 897 0.169 898 0.161 899 0.154 900 0.147 901 0.140 902 0.134 903 0.128 904 0.122 905 0.116 906 0.110 907 0.105 908 0.100 909 0.009 910 0.009 911 0.014 912 0.020 913 0.027 914 0.034 915 0.042 916 0.048 917 0.049 918 0.040 919 0.031 920 0.023 921 0.015 922 0.009 923 0.004 924 0.000 925 0.000 926 0.001 927 0.004 928 0.010 929 0.017 930 0.025 931 0.034 932 0.043 933 0.053 934 0.063 935 0.056 936 0.045 937 0.035 938 0.026 939 0.017 940 0.010 941 0.005 942 0.001 943 0.000 944 0.000 945 0.006 946 0.019 947 0.030 948 0.030 949 0.030 950 0.030 951 0.030 952 0.030 953 0.030 954 0.030 955 0.030 956 0.030 957 0.030 958 0.030 959 0.030 960 0.030 961 0.030 962 0.030 963 0.030 964 0.030 965 0.030 966 0.030 967 0.030 968 0.030 969 0.030 970 0.030 971 0.030 972 0.029 973 0.029 974 0.029 975 0.029 976 0.029 977 0.029 978 0.029 979 0.028 980 0.028 981 0.028 982 0.028 983 0.027 984 0.027 985 0.027 986 0.026 987 0.026 988 0.025 989 0.025 990 0.024 991 0.024 992 0.023 993 0.023 994 0.022 995 0.022 996 0.021 997 0.020 998 0.020 999 0.019 1000 0.018 1001 0.018 1002 0.017 1003 0.017 1004 0.016 1005 0.015 1006 0.015 1007 0.014 1008 0.013 1009 0.013 1010 0.170 1011 0.170 1012 0.175 1013 0.029 1014 0.018 1015 0.118 1016 0.250 1017 0.348 1018 0.364 1019 0.371 1020 0.396 1021 0.428 1022 0.419 1023 0.426 1024 0.496 1025 0.565 1026 0.631 1027 0.686 1028 0.728 1029 0.753 1030 0.761 1031 0.754 1032 0.728 1033 0.654 1034 0.576 1035 0.496 1036 0.415 1037 0.337 1038 0.264 1039 0.199 1040 0.144 1041 0.111 1042 0.078 1043 0.049 1044 0.025 1045 0.008 1046 0.000 1047 0.002 1048 0.012 1049 0.029 1050 0.050 1051 0.060 1052 0.060 1053 0.060 1054 0.060 1055 0.060 1056 0.060 1057 0.060 1058 0.060 1059 0.060 1060 0.060 1061 0.059 1062 0.059 1063 0.059 1064 0.059 1065 0.059 1066 0.059 1067 0.059 1068 0.058 1069 0.058 1070 0.058 1071 0.058 1072 0.057 1073 0.057 1074 0.057 1075 0.056 1076 0.056 1077 0.055 1078 0.055 1079 0.055 1080 0.054 1081 0.053 1082 0.053 1083 0.052 1084 0.051 1085 0.051 1086 0.050 1087 0.049 1088 0.048 1089 0.047 1090 0.046 1091 0.045 1092 0.044 1093 0.043 1094 0.042 1095 0.041 1096 0.040 1097 0.038 1098 0.037 1099 0.036 1100 0.035 1101 0.034 1102 0.033 1103 0.032 1104 0.030 1105 0.029 1106 0.028 1107 0.027 1108 0.026 1109 0.025 1110 0.024 1111 0.579 1112 0.542 1113 0.444 1114 0.328 1115 0.209 1116 0.107 1117 0.038 1118 0.004 1119 0.001 1120 0.002 1121 0.000 1122 0.000 1123 0.000 1124 0.000 1125 0.000 1126 0.001 1127 0.002 1128 0.003 1129 0.004 1130 0.005 1131 0.006 1132 0.004 1133 0.001 1134 0.000 1135 0.000 1136 0.001 1137 0.003 1138 0.007 1139 0.011 1140 0.013 1141 0.013 1142 0.013 1143 0.013 1144 0.013 1145 0.013 1146 0.013 1147 0.013 1148 0.013 1149 0.013 1150 0.013 1151 0.013 1152 0.013 1153 0.013 1154 0.013 1155 0.013 1156 0.013 1157 0.013 1158 0.013 1159 0.013 1160 0.013 1161 0.013 1162 0.013 1163 0.013 1164 0.013 1165 0.013 1166 0.013 1167 0.013 1168 0.013 1169 0.013 1170 0.013 1171 0.013 1172 0.013 1173 0.012 1174 0.012 1175 0.012 1176 0.012 1177 0.012 1178 0.012 1179 0.012 1180 0.012 1181 0.012 1182 0.011 1183 0.011 1184 0.011 1185 0.011 1186 0.011 1187 0.010 1188 0.010 1189 0.010 1190 0.010 1191 0.009 1192 0.009 1193 0.009 1194 0.009 1195 0.008 1196 0.008 1197 0.008 1198 0.008 1199 0.007 1200 0.007 1201 0.007 1202 0.006 1203 0.006 1204 0.006 1205 0.006 1206 0.005 1207 0.005 1208 0.005 1209 0.005 1210 0.004 1211 0.004 1212 0.010 1213 0.010 1214 0.010 1215 0.007 1216 0.000 1217 0.000 1218 0.000 1219 0.000 1220 0.001 1221 0.002 1222 0.005 1223 0.009 1224 0.015 1225 0.023 1226 0.031 1227 0.039 1228 0.047 1229 0.054 1230 0.058 1231 0.062 1232 0.064 1233 0.071 1234 0.080 1235 0.090 1236 0.100 1237 0.110 1238 0.120 1239 0.130 1240 0.139 1241 0.148 1242 0.152 1243 0.152 1244 0.152 1245 0.152 1246 0.152 1247 0.152 1248 0.152 1249 0.152 1250 0.152 1251 0.152 1252 0.152 1253 0.152 1254 0.151 1255 0.151 1256 0.151 1257 0.151 1258 0.150 1259 0.150 1260 0.149 1261 0.149 1262 0.148 1263 0.147 1264 0.147 1265 0.146 1266 0.145 1267 0.143 1268 0.142 1269 0.141 1270 0.139 1271 0.137 1272 0.135 1273 0.133 1274 0.131 1275 0.129 1276 0.126 1277 0.123 1278 0.121 1279 0.118 1280 0.115 1281 0.112 1282 0.109 1283 0.106 1284 0.103 1285 0.100 1286 0.096 1287 0.093 1288 0.090 1289 0.087 1290 0.084 1291 0.081 1292 0.078 1293 0.074 1294 0.072 1295 0.069 1296 0.066 1297 0.063 1298 0.060 1299 0.058 1300 0.055 1301 0.053 1302 0.050 1303 0.048 1304 0.046 1305 0.044 1306 0.042 1307 0.040 1308 0.038 1309 0.036 1310 0.034 1311 0.032 1312 0.031 1313 0.000 1314 0.000 1315 0.003 1316 0.007 1317 0.014 1318 0.021 1319 0.030 1320 0.039 1321 0.048 1322 0.034 1323 0.018 1324 0.006 1325 0.000 1326 0.001 1327 0.008 1328 0.021 1329 0.040 1330 0.063 1331 0.089 1332 0.102 1333 0.115 1334 0.128 1335 0.140 1336 0.150 1337 0.158 1338 0.165 1339 0.169 1340 0.158 1341 0.142 1342 0.124 1343 0.106 1344 0.088 1345 0.071 1346 0.055 1347 0.043 1348 0.043 1349 0.043 1350 0.043 1351 0.043 1352 0.043 1353 0.043 1354 0.043 1355 0.043 1356 0.042 1357 0.042 1358 0.042 1359 0.042 1360 0.042 1361 0.042 1362 0.042 1363 0.042 1364 0.042 1365 0.042 1366 0.042 1367 0.042 1368 0.042 1369 0.041 1370 0.041 1371 0.041 1372 0.041 1373 0.041 1374 0.040 1375 0.040 1376 0.040 1377 0.040 1378 0.039 1379 0.039 1380 0.039 1381 0.038 1382 0.038 1383 0.037 1384 0.037 1385 0.036 1386 0.035 1387 0.035 1388 0.034 1389 0.033 1390 0.033 1391 0.032 1392 0.031 1393 0.030 1394 0.029 1395 0.028 1396 0.028 1397 0.027 1398 0.026 1399 0.025 1400 0.024 1401 0.023 1402 0.022 1403 0.022 1404 0.021 1405 0.020 1406 0.019 1407 0.018 1408 0.018 1409 0.017 1410 0.016 1411 0.015 1412 0.015 1413 0.014 1414 1.037 1415 1.041 1416 1.045 1417 0.984 1418 0.754 1419 0.854 1420 0.936 1421 1.096 1422 1.312 1423 1.531 1424 1.515 1425 1.440 1426 1.305 1427 1.124 1428 0.920 1429 0.764 1430 0.743 1431 0.716 1432 0.684 1433 0.646 1434 0.603 1435 0.556 1436 0.506 1437 0.454 1438 0.403 1439 0.353 1440 0.305 1441 0.261 1442 0.237 1443 0.311 1444 0.382 1445 0.441 1446 0.472 1447 0.472 1448 0.471 1449 0.471 1450 0.470 1451 0.470 1452 0.469 1453 0.468 1454 0.467 1455 0.466 1456 0.465 1457 0.463 1458 0.462 1459 0.460 1460 0.458 1461 0.455 1462 0.452 1463 0.449 1464 0.445 1465 0.441 1466 0.437 1467 0.432 1468 0.426 1469 0.420 1470 0.414 1471 0.407 1472 0.400 1473 0.392 1474 0.385 1475 0.376 1476 0.368 1477 0.359 1478 0.350 1479 0.340 1480 0.331 1481 0.321 1482 0.311 1483 0.302 1484 0.292 1485 0.282 1486 0.273 1487 0.263 1488 0.254 1489 0.244 1490 0.235 1491 0.226 1492 0.217 1493 0.208 1494 0.200 1495 0.192 1496 0.184 1497 0.176 1498 0.168 1499 0.161 1500 0.154 1501 0.147 1502 0.140 1503 0.134 1504 0.128 1505 0.122 1506 0.116 1507 0.111 1508 0.105 1509 0.100 1510 0.095 1511 0.091 1512 0.086 1513 0.082 1514 0.078 1515 0.006 1516 0.023 1517 0.054 1518 0.101 1519 0.165 1520 0.245 1521 0.338 1522 0.437 1523 0.536 1524 0.630 1525 0.715 1526 0.789 1527 0.850 1528 0.871 1529 0.841 1530 0.792 1531 0.729 1532 0.625 1533 0.663 1534 0.694 1535 0.716 1536 0.728 1537 0.729 1538 0.718 1539 0.698 1540 0.670 1541 0.646 1542 0.645 1543 0.644 1544 0.643 1545 0.642 1546 0.640 1547 0.639 1548 0.637 1549 0.635 1550 0.632 1551 0.629 1552 0.626 1553 0.623 1554 0.619 1555 0.614 1556 0.610 1557 0.604 1558 0.598 1559 0.592 1560 0.585 1561 0.577 1562 0.569 1563 0.560 1564 0.550 1565 0.540 1566 0.530 1567 0.519 1568 0.508 1569 0.496 1570 0.484 1571 0.471 1572 0.459 1573 0.446 1574 0.433 1575 0.420 1576 0.407 1577 0.394 1578 0.381 1579 0.368 1580 0.355 1581 0.342 1582 0.330 1583 0.318 1584 0.305 1585 0.294 1586 0.282 1587 0.271 1588 0.260 1589 0.249 1590 0.238 1591 0.228 1592 0.218 1593 0.209 1594 0.200 1595 0.191 1596 0.182 1597 0.174 1598 0.166 1599 0.158 1600 0.151 1601 0.144 1602 0.137 1603 0.130 1604 0.124 1605 0.118 1606 0.112 1607 0.107 1608 0.102 1609 0.097 1610 0.092 1611 0.087 1612 0.083 1613 0.079 1614 0.075 1615 0.071 1616 0.300 1617 0.176 1618 0.143 1619 0.036 1620 0.008 1621 0.004 1622 0.009 1623 0.000 1624 0.001 1625 0.012 1626 0.027 1627 0.023 1628 0.019 1629 0.014 1630 0.010 1631 0.006 1632 0.003 1633 0.001 1634 0.000 1635 0.000 1636 0.000 1637 0.001 1638 0.002 1639 0.005 1640 0.007 1641 0.010 1642 0.010 1643 0.010 1644 0.010 1645 0.010 1646 0.010 1647 0.010 1648 0.010 1649 0.010 1650 0.010 1651 0.010 1652 0.010 1653 0.010 1654 0.010 1655 0.010 1656 0.010 1657 0.010 1658 0.010 1659 0.010 1660 0.010 1661 0.010 1662 0.010 1663 0.010 1664 0.010 1665 0.010 1666 0.010 1667 0.010 1668 0.010 1669 0.010 1670 0.010 1671 0.010 1672 0.010 1673 0.009 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1856 0.320 1857 0.319 1858 0.318 1859 0.317 1860 0.316 1861 0.315 1862 0.314 1863 0.312 1864 0.310 1865 0.308 1866 0.306 1867 0.303 1868 0.301 1869 0.297 1870 0.294 1871 0.290 1872 0.286 1873 0.281 1874 0.277 1875 0.272 1876 0.266 1877 0.261 1878 0.255 1879 0.249 1880 0.243 1881 0.236 1882 0.230 1883 0.223 1884 0.217 1885 0.210 1886 0.203 1887 0.196 1888 0.190 1889 0.183 1890 0.177 1891 0.170 1892 0.164 1893 0.157 1894 0.151 1895 0.145 1896 0.139 1897 0.134 1898 0.128 1899 0.122 1900 0.117 1901 0.112 1902 0.107 1903 0.102 1904 0.098 1905 0.093 1906 0.089 1907 0.085 1908 0.081 1909 0.077 1910 0.073 1911 0.070 1912 0.066 1913 0.063 1914 0.060 1915 0.057 1916 0.054 1917 0.051 1918 0.049 1919 0.000 1920 0.000 1921 0.004 1922 0.013 1923 0.027 1924 0.046 1925 0.070 1926 0.097 1927 0.126 1928 0.156 1929 0.185 1930 0.242 1931 0.321 1932 0.386 1933 0.423 1934 0.443 1935 0.494 1936 0.540 1937 0.574 1938 0.594 1939 0.599 1940 0.591 1941 0.574 1942 0.573 1943 0.572 1944 0.571 1945 0.570 1946 0.569 1947 0.567 1948 0.566 1949 0.564 1950 0.562 1951 0.559 1952 0.556 1953 0.553 1954 0.550 1955 0.546 1956 0.542 1957 0.537 1958 0.532 1959 0.526 1960 0.520 1961 0.514 1962 0.506 1963 0.499 1964 0.491 1965 0.482 1966 0.473 1967 0.464 1968 0.454 1969 0.444 1970 0.434 1971 0.423 1972 0.412 1973 0.401 1974 0.390 1975 0.378 1976 0.367 1977 0.355 1978 0.344 1979 0.333 1980 0.321 1981 0.310 1982 0.299 1983 0.288 1984 0.277 1985 0.267 1986 0.256 1987 0.246 1988 0.236 1989 0.227 1990 0.217 1991 0.208 1992 0.199 1993 0.191 1994 0.183 1995 0.174 1996 0.167 1997 0.159 1998 0.152 1999 0.145 2000 0.138 2001 0.132 2002 0.126 2003 0.120 2004 0.114 2005 0.109 2006 0.103 2007 0.098 2008 0.093 2009 0.089 2010 0.084 2011 0.080 2012 0.076 2013 0.072 2014 0.069 2015 0.065 2016 0.062 2017 0.059 2018 0.056 2019 0.053 2020 0.968 2021 0.987 2022 0.993 2023 0.975 2024 0.959 2025 0.943 2026 0.937 2027 0.952 2028 0.968 2029 0.985 2030 0.996 2031 0.972 2032 0.950 2033 0.937 2034 0.951 2035 0.971 2036 0.991 2037 0.978 2038 0.984 2039 0.997 2040 0.995 2041 0.994 2042 0.997 2043 0.986 2044 0.992 2045 0.991 2046 0.975 2047 0.991 2048 0.985 2049 0.967 2050 0.958 2051 0.973 2052 0.994 2053 0.981 2054 0.995 2055 0.997 2056 0.966 2057 0.994 2058 0.989 2059 0.960 2060 0.966 2061 1.000 2062 0.970 2063 0.993 2064 0.987 2065 0.975 2066 0.984 2067 1.000 2068 0.983 2069 0.968 2070 0.954 2071 0.940 2072 0.928 2073 0.925 2074 0.936 2075 0.951 2076 0.967 2077 0.984 2078 0.998 2079 0.978 2080 0.959 2081 0.941 2082 0.924 2083 0.907 2084 0.891 2085 0.876 2086 0.861 2087 0.847 2088 0.833 2089 0.820 2090 0.808 2091 0.796 2092 0.784 2093 0.773 2094 0.762 2095 0.752 2096 0.742 2097 0.733 2098 0.724 2099 0.715 2100 0.706 2101 0.698 2102 0.691 2103 0.683 2104 0.676 2105 0.669 2106 0.663 2107 0.656 2108 0.650 2109 0.644 2110 0.639 2111 0.634 2112 0.629 2113 0.625 2114 0.620 2115 0.616 2116 0.612 2117 0.609 2118 0.605 2119 0.602 2120 0.599 2121 0.928 2122 0.946 2123 0.964 2124 0.983 2125 0.995 2126 0.963 2127 0.933 2128 0.945 2129 0.976 2130 0.995 2131 0.977 2132 0.961 2133 0.956 2134 0.970 2135 0.987 2136 0.994 2137 0.975 2138 0.957 2139 0.940 2140 0.928 2141 0.927 2142 0.937 2143 0.951 2144 0.969 2145 0.988 2146 0.977 2147 0.939 2148 0.983 2149 0.981 2150 0.955 2151 0.933 2152 0.937 2153 0.962 2154 0.990 2155 0.987 2156 0.968 2157 0.950 2158 0.931 2159 0.914 2160 0.928 2161 0.947 2162 0.967 2163 0.987 2164 0.993 2165 0.976 2166 0.961 2167 0.949 2168 0.964 2169 0.980 2170 0.996 2171 0.984 2172 0.965 2173 0.947 2174 0.929 2175 0.912 2176 0.896 2177 0.880 2178 0.865 2179 0.851 2180 0.837 2181 0.824 2182 0.811 2183 0.799 2184 0.787 2185 0.776 2186 0.765 2187 0.755 2188 0.745 2189 0.735 2190 0.726 2191 0.717 2192 0.709 2193 0.700 2194 0.693 2195 0.685 2196 0.678 2197 0.671 2198 0.664 2199 0.658 2200 0.652 2201 0.646 2202 0.640 2203 0.635 2204 0.630 2205 0.625 2206 0.620 2207 0.616 2208 0.612 2209 0.609 2210 0.605 2211 0.602 2212 0.598 2213 0.596 2214 0.593 2215 0.590 2216 0.588 2217 0.586 2218 0.584 2219 0.582 2220 0.580 2221 0.578 2222 0.978 2223 0.998 2224 0.972 2225 0.941 2226 0.912 2227 0.897 2228 0.919 2229 0.948 2230 0.977 2231 0.995 2232 0.979 2233 0.963 2234 0.948 2235 0.935 2236 0.932 2237 0.943 2238 0.958 2239 0.973 2240 0.989 2241 0.989 2242 0.960 2243 0.958 2244 0.988 2245 0.990 2246 0.974 2247 0.959 2248 0.954 2249 0.969 2250 0.984 2251 0.999 2252 0.980 2253 0.963 2254 0.974 2255 0.993 2256 0.987 2257 0.968 2258 0.949 2259 0.932 2260 0.915 2261 0.898 2262 0.883 2263 0.868 2264 0.853 2265 0.839 2266 0.826 2267 0.813 2268 0.801 2269 0.789 2270 0.778 2271 0.767 2272 0.756 2273 0.746 2274 0.737 2275 0.727 2276 0.718 2277 0.710 2278 0.702 2279 0.694 2280 0.686 2281 0.679 2282 0.672 2283 0.665 2284 0.658 2285 0.652 2286 0.646 2287 0.641 2288 0.635 2289 0.630 2290 0.625 2291 0.620 2292 0.615 2293 0.611 2294 0.606 2295 0.602 2296 0.598 2297 0.594 2298 0.591 2299 0.587 2300 0.584 2301 0.581 2302 0.578 2303 0.575 2304 0.573 2305 0.570 2306 0.568 2307 0.566 2308 0.564 2309 0.562 2310 0.560 2311 0.558 2312 0.557 2313 0.555 2314 0.554 2315 0.553 2316 0.552 2317 0.550 2318 0.549 2319 0.549 2320 0.548 2321 0.547 2322 0.546 2323 1.000 2324 0.975 2325 0.952 2326 0.939 2327 0.948 2328 0.963 2329 0.980 2330 0.998 2331 0.984 2332 0.998 2333 0.990 2334 0.998 2335 0.988 2336 0.994 2337 0.997 2338 0.986 2339 0.998 2340 0.998 2341 0.994 2342 0.982 2343 0.972 2344 0.969 2345 0.979 2346 0.992 2347 0.981 2348 0.946 2349 0.928 2350 0.948 2351 0.984 2352 0.987 2353 0.968 2354 0.950 2355 0.936 2356 0.931 2357 0.937 2358 0.955 2359 0.975 2360 0.996 2361 0.983 2362 0.964 2363 0.946 2364 0.928 2365 0.912 2366 0.896 2367 0.880 2368 0.865 2369 0.851 2370 0.838 2371 0.825 2372 0.812 2373 0.801 2374 0.789 2375 0.778 2376 0.768 2377 0.759 2378 0.750 2379 0.741 2380 0.734 2381 0.726 2382 0.720 2383 0.714 2384 0.708 2385 0.702 2386 0.697 2387 0.692 2388 0.687 2389 0.683 2390 0.678 2391 0.674 2392 0.671 2393 0.667 2394 0.664 2395 0.661 2396 0.658 2397 0.655 2398 0.652 2399 0.650 2400 0.647 2401 0.645 2402 0.643 2403 0.640 2404 0.638 2405 0.636 2406 0.634 2407 0.632 2408 0.630 2409 0.628 2410 0.626 2411 0.624 2412 0.622 2413 0.620 2414 0.619 2415 0.617 2416 0.615 2417 0.614 2418 0.612 2419 0.610 2420 0.609 2421 0.607 2422 0.605 2423 0.604 2424 1.000 2425 0.961 2426 0.970 2427 0.996 2428 0.981 2429 0.992 2430 0.994 2431 0.998 2432 0.985 2433 0.988 2434 0.984 2435 0.962 2436 0.940 2437 0.922 2438 0.944 2439 0.967 2440 0.990 2441 0.988 2442 0.968 2443 0.950 2444 0.932 2445 0.917 2446 0.931 2447 0.948 2448 0.967 2449 0.986 2450 0.994 2451 0.976 2452 0.964 2453 0.981 2454 0.999 2455 0.974 2456 0.970 2457 0.992 2458 0.987 2459 0.973 2460 0.987 2461 0.984 2462 0.970 2463 0.996 2464 0.983 2465 1.000 2466 0.980 2467 0.961 2468 0.943 2469 0.926 2470 0.909 2471 0.893 2472 0.878 2473 0.863 2474 0.849 2475 0.836 2476 0.823 2477 0.810 2478 0.798 2479 0.787 2480 0.776 2481 0.766 2482 0.756 2483 0.746 2484 0.737 2485 0.728 2486 0.720 2487 0.712 2488 0.704 2489 0.697 2490 0.690 2491 0.683 2492 0.677 2493 0.672 2494 0.667 2495 0.662 2496 0.657 2497 0.652 2498 0.648 2499 0.644 2500 0.640 2501 0.637 2502 0.634 2503 0.631 2504 0.628 2505 0.625 2506 0.623 2507 0.621 2508 0.618 2509 0.616 2510 0.614 2511 0.612 2512 0.610 2513 0.609 2514 0.607 2515 0.606 2516 0.604 2517 0.603 2518 0.601 2519 0.600 2520 0.599 2521 0.597 2522 0.596 2523 0.595 2524 0.594 2525 1.000 2526 0.968 2527 0.937 2528 0.944 2529 0.973 2530 0.997 2531 0.977 2532 0.959 2533 0.951 2534 0.964 2535 0.982 2536 0.998 2537 0.990 2538 0.989 2539 0.980 2540 0.994 2541 0.975 2542 0.958 2543 0.959 2544 0.976 2545 0.995 2546 0.969 2547 0.933 2548 0.909 2549 0.936 2550 0.973 2551 0.990 2552 0.962 2553 0.986 2554 0.982 2555 0.962 2556 0.980 2557 0.990 2558 0.977 2559 0.987 2560 0.968 2561 0.949 2562 0.932 2563 0.915 2564 0.898 2565 0.883 2566 0.868 2567 0.853 2568 0.840 2569 0.826 2570 0.813 2571 0.801 2572 0.790 2573 0.778 2574 0.768 2575 0.757 2576 0.748 2577 0.738 2578 0.730 2579 0.722 2580 0.714 2581 0.707 2582 0.700 2583 0.693 2584 0.687 2585 0.682 2586 0.677 2587 0.672 2588 0.667 2589 0.662 2590 0.658 2591 0.654 2592 0.650 2593 0.646 2594 0.643 2595 0.640 2596 0.637 2597 0.634 2598 0.631 2599 0.629 2600 0.626 2601 0.624 2602 0.622 2603 0.620 2604 0.618 2605 0.616 2606 0.614 2607 0.612 2608 0.610 2609 0.608 2610 0.607 2611 0.605 2612 0.603 2613 0.602 2614 0.600 2615 0.599 2616 0.597 2617 0.596 2618 0.595 2619 0.593 2620 0.592 2621 0.590 2622 0.589 2623 0.588 2624 0.586 2625 0.585 2626 0.989 2627 0.989 2628 0.967 2629 0.946 2630 0.944 2631 0.965 2632 0.987 2633 0.990 2634 0.970 2635 0.951 2636 0.940 2637 0.958 2638 0.977 2639 0.997 2640 0.990 2641 0.991 2642 0.980 2643 0.982 2644 0.993 2645 0.990 2646 0.966 2647 0.942 2648 0.919 2649 0.911 2650 0.933 2651 0.956 2652 0.980 2653 0.992 2654 0.996 2655 0.976 2656 0.957 2657 0.939 2658 0.922 2659 0.906 2660 0.890 2661 0.874 2662 0.860 2663 0.846 2664 0.832 2665 0.819 2666 0.807 2667 0.794 2668 0.783 2669 0.772 2670 0.761 2671 0.751 2672 0.741 2673 0.732 2674 0.723 2675 0.714 2676 0.706 2677 0.698 2678 0.691 2679 0.683 2680 0.676 2681 0.670 2682 0.664 2683 0.658 2684 0.652 2685 0.647 2686 0.643 2687 0.638 2688 0.634 2689 0.629 2690 0.626 2691 0.622 2692 0.619 2693 0.615 2694 0.612 2695 0.610 2696 0.607 2697 0.605 2698 0.603 2699 0.600 2700 0.599 2701 0.597 2702 0.595 2703 0.593 2704 0.592 2705 0.590 2706 0.588 2707 0.587 2708 0.586 2709 0.584 2710 0.583 2711 0.581 2712 0.580 2713 0.579 2714 0.578 2715 0.577 2716 0.575 2717 0.574 2718 0.573 2719 0.572 2720 0.571 2721 0.570 2722 0.569 2723 0.568 2724 0.567 2725 0.566 2726 0.565 2727 0.938 2728 0.956 2729 0.974 2730 0.994 2731 0.986 2732 0.968 2733 0.950 2734 0.947 2735 0.965 2736 0.983 2737 0.996 2738 0.976 2739 0.956 2740 0.937 2741 0.940 2742 0.960 2743 0.980 2744 0.998 2745 0.975 2746 0.972 2747 0.995 2748 0.984 2749 0.964 2750 0.945 2751 0.926 2752 0.916 2753 0.934 2754 0.952 2755 0.971 2756 0.990 2757 0.988 2758 0.967 2759 0.946 2760 0.926 2761 0.923 2762 0.941 2763 0.962 2764 0.984 2765 0.994 2766 0.974 2767 0.956 2768 0.938 2769 0.920 2770 0.904 2771 0.888 2772 0.873 2773 0.858 2774 0.844 2775 0.831 2776 0.818 2777 0.805 2778 0.793 2779 0.782 2780 0.771 2781 0.760 2782 0.750 2783 0.740 2784 0.731 2785 0.722 2786 0.713 2787 0.705 2788 0.697 2789 0.690 2790 0.683 2791 0.677 2792 0.671 2793 0.665 2794 0.660 2795 0.655 2796 0.650 2797 0.645 2798 0.641 2799 0.637 2800 0.633 2801 0.630 2802 0.626 2803 0.623 2804 0.620 2805 0.617 2806 0.614 2807 0.612 2808 0.609 2809 0.607 2810 0.605 2811 0.603 2812 0.601 2813 0.599 2814 0.597 2815 0.595 2816 0.594 2817 0.592 2818 0.590 2819 0.589 2820 0.587 2821 0.586 2822 0.585 2823 0.583 2824 0.582 2825 0.580 2826 0.579 2827 0.578 2828 0.893 2829 0.909 2830 0.926 2831 0.943 2832 0.961 2833 0.980 2834 1.000 2835 0.984 2836 0.969 2837 0.955 2838 0.968 2839 0.984 2840 1.000 2841 0.980 2842 0.962 2843 0.944 2844 0.927 2845 0.941 2846 0.958 2847 0.977 2848 0.996 2849 0.984 2850 0.965 2851 0.948 2852 0.931 2853 0.938 2854 0.955 2855 0.973 2856 0.992 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F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,179)=0.020000 INFO: F(4.003,2,179)=0.020000 INFO: Log-likelihood of full model= -9080.311 INFO: Residual variance= 28972781.109 INFO: Trait mean= 4327.088; Trait variation= 28972781.109 INFO: Number of output datapoints: 505 0 0.888 1 0.886 2 0.882 3 0.878 4 0.899 5 0.882 6 0.720 7 0.439 8 0.224 9 0.190 10 0.313 11 0.446 12 0.596 13 0.570 14 0.569 15 0.419 16 0.221 17 0.212 18 0.199 19 0.184 20 0.165 21 0.088 22 0.086 23 0.083 24 0.081 25 0.078 26 0.079 27 0.080 28 0.067 29 0.047 30 0.009 31 0.000 32 0.014 33 0.044 34 0.022 35 0.003 36 0.000 37 0.050 38 0.171 39 0.288 40 0.206 41 0.100 42 0.023 43 0.000 44 0.024 45 0.008 46 0.000 47 0.002 48 0.000 49 0.017 50 0.061 51 0.121 52 0.179 53 0.182 54 0.202 55 0.234 56 0.263 57 0.288 58 0.379 59 0.237 60 0.051 61 0.510 62 1.070 63 0.729 64 0.417 65 0.215 66 0.106 67 0.033 68 0.033 69 0.032 70 0.032 71 0.032 72 0.032 73 0.032 74 0.032 75 0.032 76 0.031 77 0.031 78 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0.000 1437 0.000 1438 0.000 1439 0.000 1440 0.000 1441 0.000 1442 0.000 1443 0.000 1444 0.000 1445 0.000 1446 0.000 1447 0.000 1448 0.000 1449 0.000 1450 0.000 1451 0.000 1452 0.000 1453 0.000 1454 0.000 1455 0.000 1456 0.000 1457 0.000 1458 0.000 1459 0.000 1460 0.000 1461 0.000 1462 0.000 1463 0.000 1464 0.000 1465 0.000 1466 0.000 1467 0.000 1468 0.000 1469 0.000 1470 0.000 1471 0.000 1472 0.000 1473 0.000 1474 0.000 1475 0.000 1476 0.000 1477 0.000 1478 0.000 1479 0.000 1480 0.000 1481 0.000 1482 0.000 1483 0.000 1484 0.000 1485 0.000 1486 0.000 1487 0.000 1488 0.000 1489 0.000 1490 0.000 1491 0.000 1492 0.000 1493 0.000 1494 0.000 1495 0.000 1496 0.000 1497 0.000 1498 0.000 1499 0.000 qtl/inst/contrib/bin/test/regression/t23out.txt0000644000175100001440000022074212422233634021325 0ustar hornikusersINFO: Augmentation routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:B: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=1.000000 INFO: Done with augmentation INFO: Marker 6 at chr 1 is dropped INFO: Cofactor at chr 1 is dropped INFO: Marker 15 at chr 1 is dropped INFO: Marker 16 at chr 1 is dropped INFO: Marker 17 at chr 1 is dropped INFO: Marker 42 at chr 4 is dropped INFO: Marker 48 at chr 4 is dropped INFO: Marker 105 at chr 11 is dropped INFO: Cofactor at chr 11 is dropped INFO: Marker 107 at chr 11 is dropped INFO: Marker 111 at chr 11 is dropped INFO: Marker 133 at chr 15 is dropped INFO: Marker 137 at chr 15 is dropped INFO: Marker 139 at chr 15 is dropped INFO: Cofactor at chr 15 is dropped INFO: Marker 148 at chr 16 is dropped INFO: Marker 150 at chr 17 is dropped INFO: Marker 151 at chr 17 is dropped INFO: Marker 154 at chr 17 is dropped INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.020 Alfa=0.020000 INFO: dimX:15 nInd:250 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,235)=0.020000 INFO: F(4.003,2,235)=0.020000 INFO: Log-likelihood of full model= -15627.273 INFO: Residual variance= 59.654 INFO: Trait mean= 101.611; Trait variation= 70.959 INFO: Marker 91 is dropped, resulting in reduced model logL = -15627.280 INFO: Marker 86 is dropped, resulting in reduced model logL = -15627.292 INFO: Marker 76 is dropped, resulting in reduced model logL = -15627.307 INFO: Marker 112 is dropped, resulting in reduced model logL = -15627.325 INFO: Marker 89 is dropped, resulting in reduced model logL = -15627.372 INFO: Marker 153 is dropped, resulting in reduced model logL = -15627.742 INFO: Marker 143 is dropped, resulting in reduced model logL = -15628.195 INFO: Marker 144 is dropped, resulting in reduced model logL = -15628.564 INFO: Marker 53 is dropped, resulting in reduced model logL = -15629.423 INFO: Marker 67 is dropped, resulting in reduced model logL = -15630.344 INFO: Marker 28 is dropped, resulting in reduced model logL = -15631.632 INFO: Marker 72 is dropped, resulting in reduced model logL = -15634.535 INFO: Number of output datapoints: 2020 0 0.142 1 0.142 2 0.142 3 0.142 4 0.170 5 0.222 6 0.546 7 0.615 8 2.092 9 2.617 10 4.286 11 4.633 12 5.127 13 5.132 14 4.778 15 4.023 16 0.959 17 2.137 18 2.692 19 2.574 20 2.715 21 2.708 22 2.703 23 2.788 24 2.893 25 2.449 26 1.824 27 1.634 28 1.463 29 1.260 30 1.030 31 0.950 32 0.880 33 0.804 34 0.721 35 0.636 36 0.549 37 0.465 38 0.386 39 0.313 40 0.248 41 0.192 42 0.192 43 0.192 44 0.191 45 0.191 46 0.191 47 0.191 48 0.190 49 0.190 50 0.190 51 0.189 52 0.189 53 0.188 54 0.188 55 0.187 56 0.187 57 0.186 58 0.185 59 0.184 60 0.183 61 0.181 62 0.180 63 0.178 64 0.177 65 0.175 66 0.173 67 0.171 68 0.168 69 0.166 70 0.163 71 0.160 72 0.157 73 0.154 74 0.151 75 0.148 76 0.144 77 0.140 78 0.137 79 0.133 80 0.129 81 0.126 82 0.122 83 0.118 84 0.114 85 0.110 86 0.107 87 0.103 88 0.099 89 0.095 90 0.092 91 0.088 92 0.085 93 0.082 94 0.078 95 0.075 96 0.072 97 0.069 98 0.066 99 0.063 100 0.060 101 0.311 102 0.312 103 0.312 104 0.313 105 0.313 106 0.416 107 0.480 108 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routine INFO: Step 1: Augmentation INFO: Crosstype determined by the algorithm:B: INFO: Augmentation parameters: Maximum augmentation=10000, Maximum augmentation per individual=250, Minprob=1.000000 INFO: Done with augmentation INFO: Marker 6 at chr 1 is dropped INFO: Marker 15 at chr 1 is dropped INFO: Marker 16 at chr 1 is dropped INFO: Marker 17 at chr 1 is dropped INFO: Marker 42 at chr 4 is dropped INFO: Marker 48 at chr 4 is dropped INFO: Marker 105 at chr 11 is dropped INFO: Marker 107 at chr 11 is dropped INFO: Marker 111 at chr 11 is dropped INFO: Marker 133 at chr 15 is dropped INFO: Marker 137 at chr 15 is dropped INFO: Marker 139 at chr 15 is dropped INFO: Marker 148 at chr 16 is dropped INFO: Marker 150 at chr 17 is dropped INFO: Marker 151 at chr 17 is dropped INFO: Marker 154 at chr 17 is dropped INFO: Prob=0.020 Alfa=0.020000 INFO: Prob=0.019 Alfa=0.020000 INFO: dimX:10 nInd:250 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,240)=0.020000 INFO: F(4.003,2,240)=0.020000 INFO: Log-likelihood of full model= -14699.611 INFO: Residual variance= 57.070 INFO: Trait mean= 101.611; Trait variation= 70.959 INFO: Marker 86 is dropped, resulting in reduced model logL = -14699.667 INFO: Marker 91 is dropped, resulting in reduced model logL = -14699.720 INFO: Marker 89 is dropped, resulting in reduced model logL = -14699.897 INFO: Marker 53 is dropped, resulting in reduced model logL = -14700.620 INFO: Marker 6 is dropped, resulting in reduced model logL = -14702.540 INFO: Marker 44 is dropped, resulting in reduced model logL = -14704.641 INFO: Marker 67 is dropped, resulting in reduced model logL = -14707.821 INFO: Number of output datapoints: 2020 0 0.058 1 0.057 2 0.063 3 0.080 4 0.100 5 0.120 6 0.140 7 0.159 8 0.174 9 0.185 10 0.198 11 0.246 12 0.296 13 0.343 14 0.379 15 0.400 16 0.404 17 0.325 18 0.468 19 0.786 20 0.811 21 0.834 22 0.835 23 0.929 24 0.964 25 0.885 26 0.633 27 0.300 28 3.695 29 3.875 30 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1944 0.348 1945 0.349 1946 0.349 1947 0.350 1948 0.351 1949 0.352 1950 0.352 1951 0.353 1952 0.354 1953 0.354 1954 0.355 1955 0.355 1956 0.356 1957 0.356 1958 0.356 1959 0.356 1960 0.356 1961 0.355 1962 0.354 1963 0.353 1964 0.351 1965 0.349 1966 0.346 1967 0.344 1968 0.340 1969 0.337 1970 0.333 1971 0.328 1972 0.324 1973 0.319 1974 0.313 1975 0.308 1976 0.302 1977 0.296 1978 0.290 1979 0.283 1980 0.277 1981 0.270 1982 0.263 1983 0.256 1984 0.250 1985 0.243 1986 0.236 1987 0.229 1988 0.223 1989 0.216 1990 0.210 1991 0.203 1992 0.197 1993 0.191 1994 0.185 1995 0.179 1996 0.173 1997 0.168 1998 0.162 1999 0.157 2000 0.152 2001 0.147 2002 0.142 2003 0.137 2004 0.132 2005 0.128 2006 0.124 2007 0.119 2008 0.115 2009 0.112 2010 0.108 2011 0.104 2012 0.101 2013 0.098 2014 0.094 2015 0.091 2016 0.088 2017 0.085 2018 0.083 2019 0.080 2020 0.968 2021 0.987 2022 0.993 2023 0.976 2024 0.959 2025 0.943 2026 0.937 2027 0.951 2028 0.968 2029 0.984 2030 0.996 2031 0.974 2032 0.952 2033 0.936 2034 0.948 2035 0.969 2036 0.991 2037 0.977 2038 0.983 2039 0.997 2040 0.995 2041 0.994 2042 0.997 2043 0.986 2044 0.992 2045 0.991 2046 0.973 2047 0.992 2048 0.984 2049 0.966 2050 0.960 2051 0.974 2052 0.994 2053 0.982 2054 0.995 2055 0.998 2056 0.967 2057 0.993 2058 0.990 2059 0.960 2060 0.966 2061 1.000 2062 0.967 2063 0.994 2064 0.987 2065 0.974 2066 0.985 2067 1.000 2068 0.983 2069 0.967 2070 0.952 2071 0.937 2072 0.924 2073 0.925 2074 0.939 2075 0.953 2076 0.968 2077 0.984 2078 0.998 2079 0.978 2080 0.959 2081 0.941 2082 0.924 2083 0.907 2084 0.891 2085 0.876 2086 0.861 2087 0.847 2088 0.833 2089 0.820 2090 0.808 2091 0.796 2092 0.784 2093 0.773 2094 0.762 2095 0.752 2096 0.742 2097 0.732 2098 0.723 2099 0.714 2100 0.706 2101 0.698 2102 0.690 2103 0.683 2104 0.676 2105 0.669 2106 0.662 2107 0.656 2108 0.649 2109 0.644 2110 0.638 2111 0.633 2112 0.627 2113 0.622 2114 0.617 2115 0.613 2116 0.608 2117 0.604 2118 0.600 2119 0.596 2120 0.592 2121 0.928 2122 0.946 2123 0.964 2124 0.983 2125 0.995 2126 0.963 2127 0.933 2128 0.945 2129 0.976 2130 0.995 2131 0.977 2132 0.960 2133 0.956 2134 0.971 2135 0.987 2136 0.994 2137 0.975 2138 0.956 2139 0.939 2140 0.928 2141 0.928 2142 0.939 2143 0.952 2144 0.969 2145 0.988 2146 0.977 2147 0.938 2148 0.983 2149 0.981 2150 0.955 2151 0.934 2152 0.936 2153 0.961 2154 0.989 2155 0.987 2156 0.968 2157 0.949 2158 0.931 2159 0.914 2160 0.928 2161 0.947 2162 0.967 2163 0.987 2164 0.993 2165 0.976 2166 0.961 2167 0.950 2168 0.964 2169 0.980 2170 0.996 2171 0.984 2172 0.965 2173 0.947 2174 0.929 2175 0.912 2176 0.896 2177 0.880 2178 0.865 2179 0.851 2180 0.837 2181 0.824 2182 0.811 2183 0.799 2184 0.787 2185 0.776 2186 0.765 2187 0.755 2188 0.745 2189 0.735 2190 0.726 2191 0.717 2192 0.708 2193 0.700 2194 0.692 2195 0.685 2196 0.679 2197 0.672 2198 0.667 2199 0.661 2200 0.656 2201 0.651 2202 0.647 2203 0.643 2204 0.639 2205 0.635 2206 0.632 2207 0.629 2208 0.626 2209 0.623 2210 0.621 2211 0.618 2212 0.616 2213 0.614 2214 0.612 2215 0.610 2216 0.608 2217 0.606 2218 0.605 2219 0.603 2220 0.602 2221 0.601 2222 0.978 2223 0.998 2224 0.971 2225 0.941 2226 0.911 2227 0.901 2228 0.919 2229 0.947 2230 0.977 2231 0.995 2232 0.979 2233 0.963 2234 0.948 2235 0.935 2236 0.932 2237 0.944 2238 0.958 2239 0.973 2240 0.989 2241 0.989 2242 0.961 2243 0.957 2244 0.988 2245 0.990 2246 0.974 2247 0.959 2248 0.954 2249 0.969 2250 0.984 2251 0.999 2252 0.980 2253 0.962 2254 0.975 2255 0.993 2256 0.987 2257 0.968 2258 0.949 2259 0.932 2260 0.915 2261 0.898 2262 0.883 2263 0.868 2264 0.853 2265 0.839 2266 0.826 2267 0.813 2268 0.801 2269 0.789 2270 0.778 2271 0.767 2272 0.756 2273 0.746 2274 0.737 2275 0.727 2276 0.718 2277 0.710 2278 0.702 2279 0.694 2280 0.686 2281 0.679 2282 0.672 2283 0.665 2284 0.658 2285 0.652 2286 0.646 2287 0.640 2288 0.635 2289 0.629 2290 0.624 2291 0.619 2292 0.615 2293 0.610 2294 0.606 2295 0.603 2296 0.599 2297 0.596 2298 0.593 2299 0.590 2300 0.588 2301 0.586 2302 0.584 2303 0.582 2304 0.581 2305 0.580 2306 0.578 2307 0.577 2308 0.576 2309 0.575 2310 0.574 2311 0.574 2312 0.573 2313 0.572 2314 0.572 2315 0.571 2316 0.570 2317 0.570 2318 0.569 2319 0.569 2320 0.568 2321 0.568 2322 0.567 2323 1.000 2324 0.979 2325 0.959 2326 0.939 2327 0.951 2328 0.965 2329 0.981 2330 0.998 2331 0.985 2332 0.998 2333 0.992 2334 0.998 2335 0.989 2336 0.994 2337 0.997 2338 0.986 2339 0.997 2340 0.998 2341 0.993 2342 0.981 2343 0.969 2344 0.970 2345 0.981 2346 0.993 2347 0.981 2348 0.945 2349 0.925 2350 0.950 2351 0.985 2352 0.987 2353 0.968 2354 0.949 2355 0.933 2356 0.927 2357 0.938 2358 0.956 2359 0.976 2360 0.996 2361 0.983 2362 0.964 2363 0.946 2364 0.928 2365 0.911 2366 0.895 2367 0.880 2368 0.865 2369 0.851 2370 0.837 2371 0.824 2372 0.811 2373 0.799 2374 0.787 2375 0.776 2376 0.766 2377 0.756 2378 0.747 2379 0.738 2380 0.730 2381 0.722 2382 0.715 2383 0.708 2384 0.702 2385 0.696 2386 0.691 2387 0.686 2388 0.681 2389 0.677 2390 0.673 2391 0.669 2392 0.666 2393 0.663 2394 0.659 2395 0.656 2396 0.653 2397 0.650 2398 0.647 2399 0.645 2400 0.642 2401 0.640 2402 0.637 2403 0.635 2404 0.633 2405 0.630 2406 0.628 2407 0.626 2408 0.624 2409 0.623 2410 0.621 2411 0.619 2412 0.617 2413 0.616 2414 0.614 2415 0.613 2416 0.611 2417 0.610 2418 0.609 2419 0.607 2420 0.606 2421 0.605 2422 0.604 2423 0.603 2424 1.000 2425 0.961 2426 0.970 2427 0.996 2428 0.981 2429 0.992 2430 0.994 2431 0.998 2432 0.985 2433 0.988 2434 0.984 2435 0.962 2436 0.940 2437 0.922 2438 0.944 2439 0.967 2440 0.990 2441 0.988 2442 0.968 2443 0.950 2444 0.932 2445 0.917 2446 0.931 2447 0.948 2448 0.967 2449 0.986 2450 0.994 2451 0.976 2452 0.964 2453 0.981 2454 0.999 2455 0.975 2456 0.970 2457 0.992 2458 0.987 2459 0.971 2460 0.987 2461 0.984 2462 0.970 2463 0.996 2464 0.984 2465 1.000 2466 0.980 2467 0.961 2468 0.943 2469 0.926 2470 0.909 2471 0.893 2472 0.878 2473 0.863 2474 0.849 2475 0.835 2476 0.822 2477 0.809 2478 0.797 2479 0.786 2480 0.775 2481 0.764 2482 0.754 2483 0.744 2484 0.735 2485 0.726 2486 0.717 2487 0.709 2488 0.701 2489 0.694 2490 0.687 2491 0.681 2492 0.675 2493 0.669 2494 0.663 2495 0.658 2496 0.654 2497 0.649 2498 0.645 2499 0.642 2500 0.638 2501 0.635 2502 0.632 2503 0.629 2504 0.627 2505 0.624 2506 0.622 2507 0.620 2508 0.618 2509 0.616 2510 0.614 2511 0.612 2512 0.610 2513 0.608 2514 0.607 2515 0.605 2516 0.604 2517 0.603 2518 0.601 2519 0.600 2520 0.599 2521 0.598 2522 0.597 2523 0.596 2524 0.595 2525 1.000 2526 0.968 2527 0.937 2528 0.943 2529 0.972 2530 0.997 2531 0.977 2532 0.959 2533 0.950 2534 0.964 2535 0.982 2536 0.998 2537 0.990 2538 0.990 2539 0.979 2540 0.994 2541 0.974 2542 0.958 2543 0.960 2544 0.977 2545 0.996 2546 0.969 2547 0.933 2548 0.909 2549 0.936 2550 0.973 2551 0.990 2552 0.962 2553 0.986 2554 0.982 2555 0.963 2556 0.979 2557 0.990 2558 0.977 2559 0.987 2560 0.968 2561 0.949 2562 0.932 2563 0.915 2564 0.898 2565 0.883 2566 0.868 2567 0.854 2568 0.840 2569 0.827 2570 0.815 2571 0.803 2572 0.792 2573 0.781 2574 0.771 2575 0.762 2576 0.754 2577 0.745 2578 0.738 2579 0.730 2580 0.723 2581 0.717 2582 0.710 2583 0.705 2584 0.700 2585 0.695 2586 0.690 2587 0.686 2588 0.682 2589 0.678 2590 0.674 2591 0.670 2592 0.667 2593 0.664 2594 0.661 2595 0.658 2596 0.655 2597 0.652 2598 0.650 2599 0.647 2600 0.645 2601 0.643 2602 0.640 2603 0.638 2604 0.636 2605 0.634 2606 0.632 2607 0.630 2608 0.629 2609 0.627 2610 0.625 2611 0.623 2612 0.622 2613 0.620 2614 0.619 2615 0.617 2616 0.616 2617 0.615 2618 0.613 2619 0.612 2620 0.611 2621 0.610 2622 0.608 2623 0.607 2624 0.606 2625 0.605 2626 0.989 2627 0.989 2628 0.967 2629 0.946 2630 0.944 2631 0.965 2632 0.987 2633 0.990 2634 0.970 2635 0.951 2636 0.940 2637 0.958 2638 0.977 2639 0.997 2640 0.990 2641 0.991 2642 0.980 2643 0.982 2644 0.993 2645 0.990 2646 0.965 2647 0.942 2648 0.919 2649 0.912 2650 0.934 2651 0.956 2652 0.980 2653 0.992 2654 0.996 2655 0.976 2656 0.957 2657 0.939 2658 0.922 2659 0.906 2660 0.890 2661 0.874 2662 0.860 2663 0.845 2664 0.832 2665 0.819 2666 0.806 2667 0.794 2668 0.783 2669 0.772 2670 0.761 2671 0.751 2672 0.741 2673 0.732 2674 0.723 2675 0.715 2676 0.707 2677 0.700 2678 0.693 2679 0.686 2680 0.680 2681 0.675 2682 0.670 2683 0.665 2684 0.661 2685 0.656 2686 0.653 2687 0.649 2688 0.646 2689 0.643 2690 0.640 2691 0.637 2692 0.634 2693 0.632 2694 0.630 2695 0.627 2696 0.625 2697 0.623 2698 0.621 2699 0.619 2700 0.618 2701 0.616 2702 0.614 2703 0.612 2704 0.611 2705 0.609 2706 0.608 2707 0.606 2708 0.605 2709 0.603 2710 0.602 2711 0.601 2712 0.600 2713 0.599 2714 0.597 2715 0.596 2716 0.595 2717 0.594 2718 0.593 2719 0.592 2720 0.591 2721 0.591 2722 0.590 2723 0.589 2724 0.588 2725 0.587 2726 0.586 2727 0.938 2728 0.956 2729 0.974 2730 0.994 2731 0.986 2732 0.968 2733 0.950 2734 0.947 2735 0.965 2736 0.983 2737 0.996 2738 0.976 2739 0.956 2740 0.937 2741 0.940 2742 0.960 2743 0.980 2744 0.998 2745 0.975 2746 0.972 2747 0.995 2748 0.983 2749 0.964 2750 0.944 2751 0.926 2752 0.917 2753 0.934 2754 0.952 2755 0.971 2756 0.990 2757 0.988 2758 0.967 2759 0.946 2760 0.926 2761 0.923 2762 0.941 2763 0.962 2764 0.984 2765 0.994 2766 0.974 2767 0.956 2768 0.938 2769 0.920 2770 0.904 2771 0.888 2772 0.873 2773 0.858 2774 0.844 2775 0.831 2776 0.818 2777 0.805 2778 0.793 2779 0.782 2780 0.770 2781 0.760 2782 0.750 2783 0.740 2784 0.730 2785 0.721 2786 0.713 2787 0.704 2788 0.697 2789 0.689 2790 0.683 2791 0.677 2792 0.671 2793 0.666 2794 0.661 2795 0.656 2796 0.652 2797 0.648 2798 0.644 2799 0.641 2800 0.637 2801 0.634 2802 0.631 2803 0.628 2804 0.626 2805 0.623 2806 0.621 2807 0.619 2808 0.616 2809 0.614 2810 0.613 2811 0.611 2812 0.609 2813 0.608 2814 0.606 2815 0.605 2816 0.604 2817 0.602 2818 0.601 2819 0.600 2820 0.599 2821 0.597 2822 0.596 2823 0.595 2824 0.594 2825 0.593 2826 0.592 2827 0.591 2828 0.893 2829 0.909 2830 0.926 2831 0.943 2832 0.961 2833 0.980 2834 1.000 2835 0.984 2836 0.969 2837 0.955 2838 0.968 2839 0.984 2840 1.000 2841 0.980 2842 0.962 2843 0.944 2844 0.927 2845 0.941 2846 0.958 2847 0.977 2848 0.996 2849 0.984 2850 0.965 2851 0.948 2852 0.931 2853 0.938 2854 0.955 2855 0.973 2856 0.992 2857 0.982 2858 0.953 2859 0.926 2860 0.931 2861 0.958 2862 0.987 2863 0.989 2864 0.969 2865 0.951 2866 0.933 2867 0.916 2868 0.900 2869 0.884 2870 0.869 2871 0.855 2872 0.841 2873 0.827 2874 0.814 2875 0.802 2876 0.790 2877 0.779 2878 0.768 2879 0.757 2880 0.747 2881 0.738 2882 0.728 2883 0.720 2884 0.711 2885 0.703 2886 0.696 2887 0.689 2888 0.682 2889 0.676 2890 0.670 2891 0.665 2892 0.660 2893 0.655 2894 0.651 2895 0.647 2896 0.643 2897 0.640 2898 0.637 2899 0.634 2900 0.631 2901 0.628 2902 0.625 2903 0.623 2904 0.621 2905 0.618 2906 0.616 2907 0.614 2908 0.613 2909 0.611 2910 0.609 2911 0.608 2912 0.606 2913 0.605 2914 0.603 2915 0.602 2916 0.601 2917 0.599 2918 0.598 2919 0.597 2920 0.596 2921 0.595 2922 0.594 2923 0.593 2924 0.592 2925 0.591 2926 0.590 2927 0.589 2928 0.588 2929 0.978 2930 0.998 2931 0.979 2932 0.957 2933 0.936 2934 0.941 2935 0.962 2936 0.985 2937 0.992 2938 0.972 2939 0.952 2940 0.933 2941 0.914 2942 0.896 2943 0.879 2944 0.863 2945 0.847 2946 0.850 2947 0.866 2948 0.883 2949 0.900 2950 0.918 2951 0.936 2952 0.956 2953 0.976 2954 0.996 2955 0.984 2956 0.966 2957 0.949 2958 0.932 2959 0.916 2960 0.931 2961 0.947 2962 0.964 2963 0.982 2964 0.999 2965 0.980 2966 0.987 2967 0.994 2968 0.974 2969 0.956 2970 0.938 2971 0.920 2972 0.904 2973 0.888 2974 0.873 2975 0.858 2976 0.844 2977 0.831 2978 0.818 2979 0.805 2980 0.793 2981 0.782 2982 0.771 2983 0.760 2984 0.750 2985 0.740 2986 0.730 2987 0.721 2988 0.713 2989 0.705 2990 0.697 2991 0.690 2992 0.683 2993 0.676 2994 0.670 2995 0.665 2996 0.659 2997 0.654 2998 0.649 2999 0.645 3000 0.641 3001 0.637 3002 0.633 3003 0.630 3004 0.627 3005 0.624 3006 0.622 3007 0.619 3008 0.617 3009 0.614 3010 0.612 3011 0.610 3012 0.608 3013 0.606 3014 0.605 3015 0.603 3016 0.602 3017 0.600 3018 0.599 3019 0.597 3020 0.596 3021 0.595 3022 0.594 3023 0.593 3024 0.592 3025 0.591 3026 0.590 3027 0.589 3028 0.588 3029 0.587 3030 0.978 3031 0.998 3032 0.986 3033 0.974 3034 0.988 3035 0.980 3036 0.988 3037 0.987 3038 0.979 3039 0.992 3040 0.993 3041 0.954 3042 0.978 3043 0.990 3044 0.971 3045 0.952 3046 0.935 3047 0.921 3048 0.928 3049 0.945 3050 0.963 3051 0.983 3052 0.997 3053 0.978 3054 0.959 3055 0.941 3056 0.926 3057 0.941 3058 0.960 3059 0.979 3060 0.999 3061 0.980 3062 0.961 3063 0.942 3064 0.924 3065 0.907 3066 0.916 3067 0.934 3068 0.952 3069 0.971 3070 0.990 3071 0.989 3072 0.969 3073 0.951 3074 0.933 3075 0.916 3076 0.900 3077 0.884 3078 0.869 3079 0.855 3080 0.841 3081 0.827 3082 0.814 3083 0.802 3084 0.790 3085 0.779 3086 0.768 3087 0.757 3088 0.747 3089 0.738 3090 0.728 3091 0.719 3092 0.711 3093 0.702 3094 0.694 3095 0.687 3096 0.679 3097 0.672 3098 0.666 3099 0.659 3100 0.653 3101 0.647 3102 0.642 3103 0.637 3104 0.632 3105 0.627 3106 0.623 3107 0.620 3108 0.616 3109 0.613 3110 0.611 3111 0.608 3112 0.606 3113 0.604 3114 0.601 3115 0.600 3116 0.598 3117 0.596 3118 0.595 3119 0.593 3120 0.592 3121 0.591 3122 0.590 3123 0.588 3124 0.587 3125 0.586 3126 0.586 3127 0.585 3128 0.584 3129 0.583 3130 0.582 3131 0.989 3132 0.986 3133 0.958 3134 0.931 3135 0.905 3136 0.906 3137 0.935 3138 0.964 3139 0.994 3140 0.976 3141 0.955 3142 0.984 3143 0.991 3144 0.974 3145 0.958 3146 0.943 3147 0.933 3148 0.948 3149 0.963 3150 0.979 3151 0.996 3152 0.986 3153 0.971 3154 0.956 3155 0.942 3156 0.947 3157 0.962 3158 0.977 3159 0.993 3160 0.988 3161 0.969 3162 0.950 3163 0.932 3164 0.915 3165 0.899 3166 0.883 3167 0.868 3168 0.854 3169 0.840 3170 0.827 3171 0.814 3172 0.802 3173 0.790 3174 0.778 3175 0.767 3176 0.757 3177 0.747 3178 0.737 3179 0.728 3180 0.719 3181 0.710 3182 0.702 3183 0.694 3184 0.686 3185 0.679 3186 0.672 3187 0.665 3188 0.659 3189 0.653 3190 0.647 3191 0.642 3192 0.636 3193 0.632 3194 0.627 3195 0.623 3196 0.620 3197 0.616 3198 0.613 3199 0.611 3200 0.608 3201 0.606 3202 0.603 3203 0.601 3204 0.599 3205 0.598 3206 0.596 3207 0.594 3208 0.593 3209 0.592 3210 0.590 3211 0.589 3212 0.588 3213 0.587 3214 0.586 3215 0.585 3216 0.584 3217 0.583 3218 0.582 3219 0.581 3220 0.581 3221 0.580 3222 0.579 3223 0.579 3224 0.578 3225 0.578 3226 0.577 3227 0.577 3228 0.576 3229 0.576 3230 0.576 3231 0.575 3232 0.946 3233 0.964 3234 0.983 3235 0.993 3236 0.996 3237 0.975 3238 0.954 3239 0.974 3240 0.995 3241 0.976 3242 0.946 3243 0.918 3244 0.890 3245 0.863 3246 0.841 3247 0.854 3248 0.880 3249 0.907 3250 0.935 3251 0.964 3252 0.993 3253 0.988 3254 0.973 3255 0.960 3256 0.948 3257 0.941 3258 0.952 3259 0.965 3260 0.978 3261 0.992 3262 0.990 3263 0.970 3264 0.952 3265 0.934 3266 0.917 3267 0.901 3268 0.885 3269 0.870 3270 0.855 3271 0.841 3272 0.828 3273 0.815 3274 0.803 3275 0.791 3276 0.779 3277 0.768 3278 0.758 3279 0.748 3280 0.738 3281 0.729 3282 0.720 3283 0.712 3284 0.704 3285 0.697 3286 0.690 3287 0.684 3288 0.678 3289 0.672 3290 0.667 3291 0.662 3292 0.658 3293 0.654 3294 0.650 3295 0.647 3296 0.644 3297 0.640 3298 0.637 3299 0.634 3300 0.632 3301 0.629 3302 0.627 3303 0.624 3304 0.622 3305 0.620 3306 0.618 3307 0.616 3308 0.615 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INFO: dimX:1 nInd:180 INFO: F(Threshold,Degrees of freedom 1,Degrees of freedom 2)=Alfa INFO: F(5.468,1,179)=0.020000 INFO: F(4.003,2,179)=0.020000 INFO: Log-likelihood of full model= -8847.557 INFO: Residual variance= 28972781.109 INFO: Trait mean= 4327.088; Trait variation= 28972781.109 INFO: Number of output datapoints: 505 0 0.875 1 0.911 2 0.845 3 0.698 4 0.504 5 0.300 6 0.209 7 0.345 8 0.556 9 0.598 10 0.596 11 0.567 12 0.546 13 0.366 14 0.218 15 0.184 16 0.153 17 0.148 18 0.137 19 0.121 20 0.101 21 0.080 22 0.080 23 0.080 24 0.074 25 0.062 26 0.049 27 0.027 28 0.010 29 0.001 30 0.003 31 0.036 32 0.021 33 0.001 34 0.011 35 0.086 36 0.212 37 0.287 38 0.264 39 0.214 40 0.142 41 0.067 42 0.007 43 0.007 44 0.000 45 0.000 46 0.028 47 0.089 48 0.147 49 0.179 50 0.188 51 0.177 52 0.249 53 0.316 54 0.371 55 0.369 56 0.302 57 0.154 58 0.049 59 1.088 60 0.598 61 0.224 62 0.114 63 0.035 64 0.033 65 0.032 66 0.032 67 0.032 68 0.032 69 0.032 70 0.032 71 0.032 72 0.031 73 0.031 74 0.031 75 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9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 0 9 0 9 0 9 9 0 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 1 9 1 9 1 1 9 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9qtl/inst/contrib/bin/test/std/markers1.txt0000644000175100001440000000413212422233634020315 0ustar hornikusers1 PVV4 0 1 AXR-1 6.398 1 HH.335C-Col 10.786 1 DF.162L/164C-Col 12.913 1 EC.480C 15.059 1 EC.66C 21.846 1 GD.86L 23.802 1 g2395 27.749 1 CC.98L-Col/101C 31.212 1 AD.121C 41.271 1 AD.106L-Col 42.345 1 GB.112L 52.082 1 GD.97L 59.115 1 EG.113L/115C 62.502 1 CD.89C 67.112 1 BF.206L-Col 73.166 1 CH.200C 78.859 1 DF.260L-Col 82.554 1 EC.88C 84.414 1 GD.160C 88.617 1 CH.215L 95.126 1 BF.116C 102.114 1 GH.157L-Col 105.304 1 CC.318C 112.183 1 CD.173L/175C-Col 115.914 1 FD.90L-Col 118.105 1 GH.127L-Col 121.434 1 HH.360L-Col 126.083 2 AD.156C 0 2 BF.325L 7.159 2 GH.580L 14.072 2 GD.145C 16.718 2 FD.81L 18.809 2 CH.284C 22.45 2 FD.222L-Col 26.742 2 CH.65C 31.125 2 BF.221L 35.255 2 FD.85C 39.845 2 FD.150C 44.939 2 Erecta 48.396 2 GD.298C 53.563 2 BH.120L-Col 58.773 2 DF.140C 62.854 2 T6A23 67.085 2 EC.235L-Col/247C 71.548 2 F4I1 75.14 2 MSAT2.22 80.717 3 DF.77C 0 3 GB.120C-Col 2.707 3 EG.75L 7.136 3 FD.111L-Col/136C 11.737 3 BF.270L-Col/271C 16.259 3 BH.88C 18.463 3 GH.390L 21.535 3 EC.83C/84L 26.798 3 GD.318C/320L 29.533 3 AD.92L 32.691 3 HH.410C 36.22 3 GB.210L 39.207 3 BF.134C-Col 41.622 3 BH.225C-Col 45.555 3 GB.80C-Col 48.037 3 HH.440L 50.789 3 HH.117C 55.937 3 GD.296C-Col 59.731 3 FD.98C 64.254 3 AD.182C 70.887 3 DF.65L-Col 73.401 3 HH.171C-Col/173L 75.971 3 AD.112L-Col 79.25 3 BH.109L-Col 81.744 3 HH.90L-Col 83.226 4 ANL2 0 4 GH.250C 7.328 4 GA1 9.027 4 C6L9 13.229 4 T7M24 19.933 4 BF.151L 22.034 4 EC.306L 26.461 4 BH.92L-Col 32.2 4 FD.154L 35.109 4 CD.84C-Col/85L 40.68 4 g4539 46.056 4 CD.329C-Col 51.029 4 FD.167L-Col 54.656 4 CH.70L/71C-Col 60.696 4 HH.159C-Col 64.629 4 VPMH47 69.388 4 GB.750C 76.565 4 BH.342C/347L-Col 84.002 5 FD.207L 0 5 CH.690C 3.08 5 BH.144L 7.506 5 EC.198L-Col 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5.5 D15Mit175 5.51 D15Mit53 7.7 D15Mit111 13.1 D15Mit56 16.4 D15Mit22 16.41 D15Mit206 17.5 D15Mit152 17.51 D15Mit156 29.5 D15Mit108 55.7 D15Mit79 63.4 D16Mit32 0 D16Mit4 25.1 D16Mit171 31.7 D16Mit5 32.8 D16Mit70 51.4 D16Mit106 51.4 D17Mit164 1.1 D17Mit143 1.1 D17Mit57 1.1 D17Mit113 2.2 D17Mit131 3.3 D17Mit46 3.31 D17Mit45 5.5 D17Mit23 6.6 D17Mit11 10.9 D17Mit10 19.7 D17Mit53 33.9 D17Mit221 50.3 D18Mit67 2.2 D18Mit17 14.2 D18Mit50 26.2 D18Mit4 37.2 D19Mit59 0 D19Mit40 17.5 D19Mit53 32.8 D19Mit137 55.7 DXMit55 1.1 DXMit22 20.8 DXMit16 29.5 DXMit130 43.7qtl/inst/contrib/bin/rtest/0000755000175100001440000000000012422233634015417 5ustar hornikusersqtl/inst/contrib/bin/rtest/test_mqm_hyper_prob.R0000644000175100001440000000223312422233634021624 0ustar hornikusers###################################################################### # # Regression test # # copyright (c) 2009 Pjotr Prins # first written July 2009 # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License, # version 3, as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but without any warranty; without even the implied warranty of # merchantability or fitness for a particular purpose. See the GNU # General Public License, version 3, for more details. # # A copy of the GNU General Public License, version 3, is available # at http://www.r-project.org/Licenses/GPL-3 # # Some basic regression/integration testing for some of the QTL mapping routines # # You can run it with: # # R --no-save --no-restore --no-readline --slave < ./tests/test_qtl.R ###################################################################### set.seed(100) library(qtl) data(hyper) h <- fill.geno(hyper) aaa <- mqm(h) # sink(paste('regression/',script,'.rnew',sep='')) aaa[25,3] aaa # sink() cat(script,'successful') qtl/inst/contrib/bin/rtest/regression/0000755000175100001440000000000012566656321017612 5ustar hornikusersqtl/inst/contrib/bin/rtest/regression/scanone_mr.rtest0000644000175100001440000001112212422233634023003 0ustar hornikusers chr pos lod D10M44 1 0.0000000 0.457256443 D1M3 1 0.9967536 0.687377692 D1M75 1 24.8477329 0.244500303 D1M215 1 40.4136087 0.070120158 D1M309 1 49.9946793 0.403217049 D1M218 1 52.8001989 0.428129381 D1M451 1 70.1120380 1.243387892 D1M504 1 70.8064156 1.670177915 D1M113 1 80.6232356 2.072608395 D1M355 1 81.3962314 2.099546061 D1M291 1 84.9347425 1.207636222 D1M209 1 92.6839378 1.077029281 D1M155 1 93.6434358 1.187361333 D2M365 2 0.0000000 0.650181186 D2M37 2 27.9417064 0.966380465 D2M396 2 47.1054126 0.708457175 D2M493 2 67.2618486 0.970694334 D2M226 2 77.3980531 0.099129726 D2M148 2 90.8562972 1.050081027 D3M265 3 0.0000000 1.612090620 D3M51 3 32.4783882 0.010489621 D3M106 3 43.9380310 0.453740239 D3M257 3 57.5933778 0.350662548 D3M147 3 63.1853999 1.722229874 D3M19 3 70.8389992 0.568140474 D4M2 4 0.0000000 0.243317233 D4M178 4 19.1607206 0.168825739 D4M187 4 35.3208564 0.089497701 D4M251 4 68.1031577 1.174431337 D5M148 5 0.0000000 1.438145038 D5M232 5 6.1039605 1.909839849 D5M257 5 19.2233538 5.592881582 D5M83 5 19.5488257 6.044957582 D5M307 5 23.7171414 5.352222138 D5M357 5 25.5000923 6.373633192 D5M205 5 30.8966519 5.728437840 D5M398 5 30.8976519 3.180821291 D5M91 5 32.9052187 5.839359207 D5M338 5 38.0680740 4.805622469 D5M188 5 44.0237627 3.249708646 D5M29 5 50.9847069 1.715029879 D5M168 5 61.8761342 2.374416033 D6M223 6 10.0000000 1.416886394 D6M188 6 18.1875357 1.296092066 D6M284 6 23.8721814 0.241924447 D6M39 6 31.0941021 1.334941184 D6M254 6 41.7950551 1.676749456 D6M194 6 45.1457923 1.451502191 D6M290 6 47.5298993 1.242429436 D6M25 6 51.2473599 2.320174554 D6M339 6 51.6507276 1.819532643 D6M59_ 6 55.3047761 2.929842018 D6M201 6 59.0098815 3.520500738 D6M15 6 59.3708928 3.242226720 D6M294 6 60.7624381 2.898064572 D7M246 7 0.0000000 0.203122690 D7M145 7 18.7885087 0.033912923 D7M62 7 34.9106206 0.068889696 D7M126 7 41.0304769 0.105848197 D7M105 7 60.1140878 0.571417248 D7M259 7 72.0842422 0.104834516 D8M94 8 0.0000000 0.693746389 D8M339 8 1.3398699 0.550880312 D8M178 8 11.4209116 0.354067333 D8M242 8 27.1406584 0.018444232 D8M213 8 32.9862459 0.034169214 D8M156 8 50.8636380 0.028466494 D9M247 9 0.0000000 0.998864005 D9M328 9 4.2182318 0.794527510 D9M106 9 14.7156490 0.762369167 D9M269 9 27.3241659 0.688124258 D9M346 9 32.9564395 0.372234007 D9M55 9 45.3356686 0.483243036 D9M18 9 52.5040374 0.255909401 D10M298 10 0.0000000 0.082679936 D10M294 10 24.7474504 0.759687621 D10M42_ 10 40.7098270 0.536720587 D10M10 10 48.7300442 0.584945946 D10M233 10 61.0562129 0.254427676 D11M78 11 0.0000000 0.183988494 D11M20 11 15.1539431 0.012474949 D11M242 11 26.4214863 0.186890388 D11M356 11 38.5214527 0.022762162 D11M327 11 42.1613924 0.159847542 D11M333 11 64.3448089 0.247025583 D12M105 12 0.0000000 0.642273556 D12M46 12 6.1792124 0.159674185 D12M34 12 21.5805108 0.101980348 D12M5 12 29.0840435 0.990636231 D12M99 12 41.7956887 1.179464292 D12M150 12 54.4558222 1.318019873 D13M59 13 0.0000000 1.917972839 D13M88 13 0.2867510 1.925915225 D13M21 13 10.3658829 2.812111181 D13M39 13 13.0498272 2.474672075 D13M167 13 13.0508272 2.978427920 D13M99 13 18.9088377 4.408391606 D13M233 13 21.0125846 3.869786360 D13M106 13 24.8753087 4.623139634 D13M147 13 26.1595405 5.819850844 D13M226 13 28.3927022 1.430587049 D13M290 13 28.3937022 4.511683695 D13M151 13 35.9870720 1.388891305 D14M14 14 0.0000000 0.059602996 D14M115 14 23.9074712 0.041936280 D14M265 14 32.7867882 0.056892890 D14M266 14 45.5502177 0.072533054 D15M226 15 0.0000000 0.121657830 D15M100 15 13.4619485 1.310281926 D15M209 15 18.7908094 2.713340673 D15M144 15 19.3647262 2.739312760 D15M68 15 23.9137277 3.066473485 D15M239 15 25.1265015 2.745144967 D15M241 15 31.2760651 0.606605377 D15M34 15 42.9720699 0.075628903 D16M154 16 0.0000000 0.540067800 D16M4 16 16.7668399 0.991960779 D16M139 16 26.2313473 0.267852056 D16M86 16 41.7990103 0.798130954 D17M260 17 0.0000000 0.181887280 D17M66 17 11.7282258 0.418911479 D17M88 17 17.3352742 0.428084799 D17M129 17 38.8480650 0.085680227 D18M94 18 0.0000000 1.230336528 D18M58 18 0.6855995 0.702230260 D18M106 18 16.9838629 0.174021233 D18M186 18 20.8998972 0.816410482 D19M68 19 0.0000000 0.376932627 D19M117 19 16.3639828 0.436675089 D19M65 19 32.8293490 0.007363598 D19M10 19 44.4943174 0.000000000 DXM186 X 0.0000000 0.678435461 DXM64 X 42.3459320 0.002756074 qtl/inst/contrib/bin/rtest/regression/mqm_listeria1.rtest0000644000175100001440000005251612422233634023442 0ustar hornikusers chr pos (cM) LOD T264 info LOD*info D10M44 1 0.0000000 0.2468037892 1.0112567 0.2495819745 D1M3 1 0.9967536 0.2833844374 1.0000000 0.2833844374 c1.loc5 1 5.0000000 0.4303027321 0.8385091 0.3608127476 c1.loc10 1 10.0000000 0.3849760892 0.6540819 0.2518058810 c1.loc15 1 15.0000000 0.2975905975 0.6127354 0.1823442823 c1.loc20 1 20.0000000 0.1914506558 0.8004076 0.1532385565 D1M75 1 24.8477329 0.1065300245 1.0000000 0.1065300245 c1.loc25 1 25.0000000 0.1038626707 1.0052790 0.1044109576 c1.loc30 1 30.0000000 0.0682345647 0.8277973 0.0564843875 c1.loc35 1 35.0000000 0.0315735621 0.8156745 0.0257537484 c1.loc40 1 40.0000000 0.0085845593 0.9949497 0.0085412048 D1M215 1 40.4136087 0.0146640837 1.0000000 0.0146640837 c1.loc45 1 45.0000000 0.0820782270 0.8312660 0.0682288357 D1M309 1 49.9946793 0.3911452308 1.0000000 0.3911452308 c1.loc50 1 50.0000000 0.3914744716 1.0110158 0.3957868849 D1M218 1 52.8001989 0.4291525127 1.0000000 0.4291525127 c1.loc55 1 55.0000000 0.4587519098 0.9169946 0.4206730469 c1.loc60 1 60.0000000 0.5785916553 0.7253178 0.4196628117 c1.loc65 1 65.0000000 0.6559284386 0.8063577 0.5289129515 c1.loc70 1 70.0000000 0.6732524854 1.0064067 0.6775658058 D1M451 1 70.1120380 0.6894816150 1.0000000 0.6894816150 D1M504 1 70.8064156 0.7900648700 1.0000000 0.7900648700 c1.loc75 1 75.0000000 1.3975215856 0.8507244 1.1889057346 c1.loc80 1 80.0000000 1.9276477589 0.9878746 1.9042743159 D1M113 1 80.6232356 1.8874289728 1.0000000 1.8874289728 D1M355 1 81.3962314 1.8375458291 1.0000000 1.8375458291 D1M291 1 84.9347425 1.6091978117 1.0000000 1.6091978117 c1.loc85 1 85.0000000 1.6049865998 1.0083582 1.6184014118 c1.loc90 1 90.0000000 1.4774637289 0.8926007 1.3187852031 D1M209 1 92.6839378 1.3524437293 1.0000000 1.3524437293 D1M155 1 93.6434358 1.3077495321 1.0000000 1.3077495321 c1.loc95 1 95.0000000 1.2445596732 0.9580946 1.1924059543 D2M365 2 0.0000000 0.2345022584 1.0112567 0.2371419695 c2.loc5 2 5.0000000 0.2872093393 0.8125387 0.2333687094 c2.loc10 2 10.0000000 0.3336110827 0.6549263 0.2184906828 c2.loc15 2 15.0000000 0.3611036561 0.5787827 0.2090005426 c2.loc20 2 20.0000000 0.3641545853 0.7105283 0.2587421397 c2.loc25 2 25.0000000 0.3471342390 0.8902393 0.3090325553 D2M37 2 27.9417064 0.3496779919 1.0000000 0.3496779919 c2.loc30 2 30.0000000 0.3514578399 0.9200661 0.3233644340 c2.loc35 2 35.0000000 0.3809380180 0.7274651 0.2771191125 c2.loc40 2 40.0000000 0.3653037023 0.7227017 0.2640056243 c2.loc45 2 45.0000000 0.3083154051 0.9176248 0.2829178722 D2M396 2 47.1054126 0.3038823607 1.0000000 0.3038823607 c2.loc50 2 50.0000000 0.2977876718 0.8814154 0.2624746352 c2.loc55 2 55.0000000 0.3104374797 0.6845933 0.2125234134 c2.loc60 2 60.0000000 0.2873880441 0.7049558 0.2025958576 c2.loc65 2 65.0000000 0.2376796477 0.9083654 0.2158999613 D2M493 2 67.2618486 0.2139366245 1.0000000 0.2139366245 c2.loc70 2 70.0000000 0.1851937662 0.9171539 0.1698511830 c2.loc75 2 75.0000000 0.1199195486 0.9265510 0.1111115746 D2M226 2 77.3980531 0.1499157792 1.0000000 0.1499157792 c2.loc80 2 80.0000000 0.1824624297 0.8920957 0.1627739541 c2.loc85 2 85.0000000 0.4476315745 0.7782912 0.3483877343 c2.loc90 2 90.0000000 0.6894219660 0.9728505 0.6707044778 D2M148 2 90.8562972 0.6935993462 1.0000000 0.6935993462 c2.loc95 2 95.0000000 0.7138140812 0.8563093 0.6112456290 D3M265 3 0.0000000 0.2764476704 1.0112567 0.2795595466 c3.loc5 3 5.0000000 0.2756480620 0.8097364 0.2232022659 c3.loc10 3 10.0000000 0.2613311167 0.6381781 0.1667757841 c3.loc15 3 15.0000000 0.2288501441 0.5081055 0.1162800233 c3.loc20 3 20.0000000 0.1805457619 0.5608255 0.1012546697 c3.loc25 3 25.0000000 0.1271790140 0.7186674 0.0913994115 c3.loc30 3 30.0000000 0.0809536327 0.9065535 0.0733888023 D3M51 3 32.4783882 0.1147975762 1.0000000 0.1147975762 c3.loc35 3 35.0000000 0.1492317637 0.9266812 0.1382902754 c3.loc40 3 40.0000000 0.4030547967 0.8932593 0.3600324551 D3M106 3 43.9380310 0.5384125502 1.0000000 0.5384125502 c3.loc45 3 45.0000000 0.5749144819 0.9716120 0.5585938082 c3.loc50 3 50.0000000 0.3736121477 0.8077143 0.3017718776 c3.loc55 3 55.0000000 0.1629398492 0.9113475 0.1484948192 D3M257 3 57.5933778 0.1084795948 1.0000000 0.1084795948 c3.loc60 3 60.0000000 0.0579411588 0.9104728 0.0527538485 D3M147 3 63.1853999 0.0477193148 1.0000000 0.0477193148 c3.loc65 3 65.0000000 0.0418963226 0.9429022 0.0395041345 c3.loc70 3 70.0000000 0.1017039586 0.9797509 0.0996445455 D3M19 3 70.8389992 0.1031992157 1.0000000 0.1031992157 c3.loc75 3 75.0000000 0.1106149153 0.8557215 0.0946555562 D4M2 4 0.0000000 0.1589370885 1.0112567 0.1607261886 c4.loc5 4 5.0000000 0.1298860483 0.8265549 0.1073579513 c4.loc10 4 10.0000000 0.0806237778 0.7049464 0.0568354397 c4.loc15 4 15.0000000 0.0320488627 0.8536611 0.0273588680 D4M178 4 19.1607206 0.0100362316 1.0000000 0.0100362316 c4.loc20 4 20.0000000 0.0055959558 0.9865269 0.0055205609 c4.loc25 4 25.0000000 0.0020783198 0.8572445 0.0017816281 c4.loc30 4 30.0000000 0.0282591263 0.8705164 0.0246000324 c4.loc35 4 35.0000000 0.0738199474 1.0017690 0.0739505356 D4M187 4 35.3208564 0.0753890370 1.0000000 0.0753890370 c4.loc40 4 40.0000000 0.0982715352 0.8167954 0.0802677411 c4.loc45 4 45.0000000 0.1194323596 0.6401432 0.0764538119 c4.loc50 4 50.0000000 0.1313389442 0.5034655 0.0661246298 c4.loc55 4 55.0000000 0.1303189464 0.5433797 0.0708126714 c4.loc60 4 60.0000000 0.1199125651 0.6920710 0.0829880076 c4.loc65 4 65.0000000 0.1061896415 0.8789003 0.0933301089 D4M251 4 68.1031577 0.1009631309 1.0000000 0.1009631309 c4.loc70 4 70.0000000 0.0977683638 0.9374402 0.0916519959 D5M148 5 0.0000000 1.6149357648 1.0112567 1.6331145405 c5.loc5 5 5.0000000 1.7887435997 0.9667933 1.7293453279 D5M232 5 6.1039605 2.0270495136 1.0000000 2.0270495136 c5.loc10 5 10.0000000 2.8680662344 0.8321966 2.3867949576 c5.loc15 5 15.0000000 4.1109569654 0.8623833 3.5452204863 D5M257 5 19.2233538 4.9264451494 1.0000000 4.9264451494 D5M83 5 19.5488257 4.9892905651 1.0000000 4.9892905651 c5.loc20 5 20.0000000 5.0764079012 0.9894672 5.0229389226 D5M307 5 23.7171414 5.3690713919 1.0000000 5.3690713919 c5.loc25 5 25.0000000 5.4700753150 0.9994996 5.4673382923 D5M357 5 25.5000923 5.4655962613 1.0000000 5.4655962613 c5.loc30 5 30.0000000 5.4252930462 0.9682801 5.2532032775 D5M205 5 30.8966519 5.3355763643 1.0000000 5.3355763643 D5M398 5 30.8976519 5.3354763069 1.0000000 5.3354763069 D5M91 5 32.9052187 5.1346043485 1.0000000 5.1346043485 c5.loc35 5 35.0000000 4.9250059256 0.9348411 4.6040979031 D5M338 5 38.0680740 4.4015033928 1.0000000 4.4015033928 c5.loc40 5 40.0000000 4.0718607166 0.9486721 3.8628605265 D5M188 5 44.0237627 3.3063085995 1.0000000 3.3063085995 c5.loc45 5 45.0000000 3.1205718768 0.9900996 3.0896770769 c5.loc50 5 50.0000000 3.1045244713 0.9897121 3.0725853182 D5M29 5 50.9847069 3.0482642520 1.0000000 3.0482642520 c5.loc55 5 55.0000000 2.8188545982 0.8193246 2.3095569836 c5.loc60 5 60.0000000 2.0417428668 0.9031524 1.8440049743 D5M168 5 61.8761342 1.9032420819 1.0000000 1.9032420819 c5.loc65 5 65.0000000 1.6726307248 0.8925258 1.4928661187 c6.loc0 6 0.0000000 1.3285307659 0.6732195 0.8943928540 c6.loc5 6 5.0000000 1.3505385292 0.8273808 1.1174097034 c6.loc10 6 10.0000000 1.3667198276 1.0112567 1.3821045219 D6M223 6 10.0000000 1.3667198276 1.0000000 1.3667198276 c6.loc15 6 15.0000000 1.3243757846 0.8574658 1.1356069225 D6M188 6 18.1875357 0.9969329331 1.0000000 0.9969329331 c6.loc20 6 20.0000000 0.8107456913 0.9596752 0.7780525212 D6M284 6 23.8721814 0.4565643697 1.0000000 0.4565643697 c6.loc25 6 25.0000000 0.3534048770 0.9569487 0.3381903410 c6.loc30 6 30.0000000 0.9935021443 0.9671574 0.9608729893 D6M39 6 31.0941021 1.0475133410 1.0000000 1.0475133410 c6.loc35 6 35.0000000 1.2403310126 0.8500114 1.0542955206 c6.loc40 6 40.0000000 1.2179747214 0.9338342 1.1373864041 D6M254 6 41.7950551 1.1709454525 1.0000000 1.1709454525 c6.loc45 6 45.0000000 1.0869779940 1.0060137 1.0935147419 D6M194 6 45.1457923 1.0953044423 1.0000000 1.0953044423 D6M290 6 47.5298993 1.2314649095 1.0000000 1.2314649095 c6.loc50 6 50.0000000 1.3725366263 0.9287446 1.2747360092 D6M25 6 51.2473599 1.5419122579 1.0000000 1.5419122579 D6M339 6 51.6507276 1.5966844795 1.0000000 1.5966844795 c6.loc55 6 55.0000000 2.0514731458 1.0033284 2.0583012439 D6M59_ 6 55.3047761 2.0489838355 1.0000000 2.0489838355 D6M201 6 59.0098815 2.0187217632 1.0000000 2.0187217632 D6M15 6 59.3708928 2.0157731422 1.0000000 2.0157731422 c6.loc60 6 60.0000000 2.0106348036 0.9834329 1.9773243275 D6M294 6 60.7624381 1.9714421734 1.0000000 1.9714421734 c6.loc65 6 65.0000000 1.7536130986 0.8542805 1.4980774794 D7M246 7 0.0000000 0.1502216273 1.0112567 0.1519126205 c7.loc5 7 5.0000000 0.0895836780 0.8428974 0.0755098532 c7.loc10 7 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0.0048249036 c19.loc45 19 45.0000000 0.0046567118 0.9911010 0.0046152718 qtl/inst/contrib/bin/rtest/test_mqm_listeria1.R0000644000175100001440000000232112422233634021346 0ustar hornikusers###################################################################### # # Regression test # # copyright (c) 2009 Pjotr Prins # first written July 2009 # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License, # version 3, as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but without any warranty; without even the implied warranty of # merchantability or fitness for a particular purpose. See the GNU # General Public License, version 3, for more details. # # A copy of the GNU General Public License, version 3, is available # at http://www.r-project.org/Licenses/GPL-3 # # Some basic regression/integration testing for some of the QTL mapping routines # # You can run it with: # # R --no-save --no-restore --no-readline --slave < ./tests/test_qtl.R ###################################################################### script='mqm_listeria1' library(qtl) data(listeria) augmentedcross <- mqmaugment(listeria, minprob=1.0) result <- mqmscan(augmentedcross) sink(paste('regression/',script,'.rnew',sep='')) result sink() cat(script,'successful') qtl/inst/contrib/bin/rtest/test_augmentation.R0000644000175100001440000000420112422233634021271 0ustar hornikuserslibrary(qtl) set.seed(1000) version = mqm_version() cat("R/qtl=",version$RQTL) cat("R-MQM=",version$RMQM) cat("MQM=",version$MQM) testaugmentation <- function(cross, ...){ crossML <- mqmaugment(cross, ...) crossIMP <- mqmaugment(cross,strategy="impute", ...) crossDROP <- mqmaugment(cross,strategy="drop", ...) res1 <- mqmscan(crossML) res2 <- mqmscan(crossIMP) res3 <- mqmscan(crossDROP) plot(res1,res2,res3,lty=c(1,2,3),col=c("black","blue","red")) legend("topleft",c("MinProb","Imputation","Drop"),lty=c(1,2,3),col=c("black","blue","red")) list(res1,res2,res3) } stabilitytest <- function(cross, ...){ crossML1 <- mqmaugment(cross, minprob=0.5,maxaugind=16,...) crossML2 <- mqmaugment(cross, minprob=0.05,maxaugind=16,...) crossML3 <- mqmaugment(cross, minprob=0.005,maxaugind=16,...) res1 <- mqmscan(crossML1) res2 <- mqmscan(crossML2) res3 <- mqmscan(crossML3) plot(res1,res2,res3,lty=c(1,2,3),col=c("black","blue","red")) legend("topleft",c("minprob 0.5","minprob 0.05","minprob 0.005"),lty=c(1,2,3),col=c("black","blue","red")) list(res1,res2,res3) } data(multitrait) multimissing <- simulatemissingdata(multitrait,25) #r <- stabilitytest(multimissing,verbose=TRUE) r <- testaugmentation(multimissing) if(!round(r[[1]][3,3],3)==0.764) stop("Multitrait ML dataaugmentation error") if(!round(r[[1]][3,3],3)==round(r[[2]][3,3],3)) stop("Multitrait ML compared versus IMP error") if(!round(r[[3]][3,3],3)==0.844) stop("Multitrait DROP dataaugmentation error") data(hyper) r <- testaugmentation(hyper,maxaugind=32,minprob=0.75) if(!round(r[[1]][3,3],3)==0.568) stop("Hyper ML dataaugmentation error") if(!round(r[[1]][3,3],3)==round(r[[2]][3,3],3)) stop("Hyper ML compared versus IMP error") if(!round(r[[3]][3,3],3)==0.252) stop("Hyper DROP dataaugmentation error") data(listeria) r <- testaugmentation(listeria) if(!round(r[[1]][3,3],3)==0.352) stop("Listeria ML dataaugmentation error") if(!round(r[[1]][3,3],3)==round(r[[2]][3,3],3)) stop("Listeria ML compared versus IMP error") if(!round(r[[3]][3,3],3)==0.034) stop("Listeria DROP dataaugmentation error") cat("testaugmentation.R, tests succesfully run!") qtl/inst/contrib/bin/rtest/test_scanone_mr.R0000644000175100001440000000220512422233634020724 0ustar hornikusers###################################################################### # # test_scanon_mr.R # # copyright (c) 2009 Pjotr Prins # first written July 2009 # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License, # version 3, as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but without any warranty; without even the implied warranty of # merchantability or fitness for a particular purpose. See the GNU # General Public License, version 3, for more details. # # A copy of the GNU General Public License, version 3, is available # at http://www.r-project.org/Licenses/GPL-3 # # Some basic regression/integration testing for some of the QTL mapping routines # # You can run it with: # # R --no-save --no-restore --no-readline --slave < ./tests/test_qtl.R ###################################################################### library(qtl) data(listeria) mr = scanone(listeria,method='mr') sink('regression/scanone_mr.rnew') mr sink() cat("test_qtl.R tests succesfully run!") qtl/inst/sampledata/0000755000175100001440000000000011562010003014144 5ustar hornikusersqtl/inst/sampledata/listeria_qc_cro.txt0000644000175100001440000014755511562004332020100 0ustar hornikusers# 123456789 -filetype Rcross.out -n 120 -p 134 -cross SF2 -traits 1 -Names of traits... 1 T264 -otraits 0 -s 1 1 2 2 2 1 1 1 2 2 1 1 1 1 1 1 0 0 -1 0 0 2 2 2 2 2 2 0 1 0 1 0 0 1 1 1 1 1 -1 1 1 1 2 2 0 0 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 2 2 2 1 1 1 0 0 1 2 2 2 2 2 2 12 -1 1 1 1 0 0 0 0 0 0 0 2 2 1 1 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 2 2 2 2 1 1 1 12 1 1 118.317 2 1 -1 2 2 2 1 1 1 1 1 1 1 1 1 2 2 1 -1 1 0 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 1 1 2 2 2 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 1 1 1 2 2 2 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 1 0 0 1 1 1 1 -1 1 1 1 1 1 0 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 2 2 1 1 0 1 1 12 1 1 264 3 1 -1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 -1 -1 1 1 1 1 1 2 2 0 0 1 1 0 0 0 0 1 1 1 -1 1 1 1 1 0 1 1 1 1 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 1 1 1 2 1 1 2 2 2 2 2 2 2 1 0 0 0 0 1 1 1 1 1 1 2 2 1 1 1 2 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 0 1 1 2 2 1 1 0 0 1 1 194.917 4 1 2 2 1 1 1 1 2 2 2 2 2 2 2 0 -1 0 -1 1 1 2 1 1 1 1 1 1 1 1 1 -1 1 0 0 0 0 0 -1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 1 1 1 1 0 1 2 1 1 2 2 2 2 2 1 0 0 1 0 0 0 12 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 2 1 1 1 1 0 0 0 1 1 2 0 1 1 2 0 0 0 0 2 1 1 12 1 0 264 5 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 -1 -1 1 2 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 -1 -1 1 1 1 1 1 0 0 0 0 0 0 -1 1 1 1 1 1 1 2 1 0 0 1 1 0 0 0 0 0 1 2 2 2 1 1 1 1 0 1 1 1 1 2 2 2 2 2 1 0 0 0 0 0 0 12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 2 2 2 0 0 0 1 0 0 0 0 1 0 0 12 0 0 145.417 6 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 -1 0 2 2 2 2 2 2 1 1 1 1 0 0 0 0 0 0 1 -1 1 1 1 -1 1 0 0 0 1 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 12 1 -1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 12 0 1 177.233 7 1 1 1 1 1 0 0 0 0 1 1 1 1 1 2 2 2 2 -1 1 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 -1 0 0 0 -1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 0 0 0 0 1 1 1 1 1 0 0 0 1 1 2 2 1 0 0 0 0 12 1 2 2 2 2 2 2 2 2 2 2 0 1 1 1 0 0 0 0 0 0 0 -1 2 1 1 1 1 0 0 1 1 1 2 2 2 2 0 0 1 1 264 8 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 -1 0 0 1 2 1 1 1 1 1 -1 1 2 2 2 2 2 2 2 2 -1 2 2 1 1 1 0 0 0 0 1 1 -1 2 2 2 2 2 2 1 1 1 1 1 2 0 0 1 1 1 1 2 2 2 2 2 2 1 0 0 -1 0 0 1 2 1 2 2 1 0 1 1 1 1 1 12 1 1 0 0 0 -1 0 0 -1 0 1 1 2 1 1 2 2 2 2 1 1 1 1 2 2 2 2 1 1 1 1 0 0 -1 1 1 1 1 12 0 -1 76.667 9 1 0 0 1 2 2 2 1 1 1 1 1 -1 1 0 1 1 1 1 2 0 1 1 0 0 0 1 1 1 2 1 1 1 1 0 0 0 -1 0 0 0 -1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 2 2 2 2 2 1 2 2 -1 2 2 0 1 1 1 0 0 0 0 1 2 2 2 2 2 2 1 1 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 1 0 0 0 0 0 0 0 1 2 2 2 0 0 1 1 2 2 -1 2 1 1 1 0 0 1 90.75 10 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 2 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 2 2 2 1 1 1 2 2 2 2 2 1 0 0 1 1 1 1 0 1 1 1 1 2 0 -1 0 0 0 0 12 1 1 1 1 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 1 1 1 1 2 2 2 1 0 0 0 0 0 0 0 0 1 2 2 12 1 0 76.167 11 1 0 1 1 1 1 1 1 1 1 1 1 -1 1 0 0 2 2 2 2 1 0 0 1 1 1 1 1 1 2 1 1 1 1 1 1 1 -1 1 1 1 2 2 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1 -1 0 0 0 0 0 0 1 1 2 2 2 2 2 0 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 2 12 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 0 0 0 0 0 0 0 1 2 2 2 1 1 2 2 2 0 0 0 0 2 1 1 12 0 1 104.083 12 1 0 0 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 0 1 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 -1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 2 2 1 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 1 1 0 0 0 0 1 0 -1 0 0 0 0 0 1 1 -1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 0 0 1 12 1 1 194.5 13 1 0 0 1 1 1 1 2 2 2 2 2 -1 2 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 -1 1 1 1 -1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 0 0 -1 0 1 12 1 0 0 0 0 0 0 0 1 1 1 2 2 2 2 0 0 0 0 0 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 75.917 14 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 2 2 1 -1 1 2 1 1 1 2 1 1 1 1 1 2 2 2 2 2 2 2 -1 2 2 2 -1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 -1 1 1 0 0 0 0 0 1 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1 0 1 1 1 1 1 2 12 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 1 0 0 0 2 2 2 2 0 0 -1 1 1 2 -1 12 0 1 75.833 15 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 2 2 2 2 2 2 1 1 2 2 0 0 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 1 1 1 1 1 1 12 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 -1 -1 0 0 0 0 0 -1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 0 0 0 90.25 16 1 1 1 2 2 2 2 1 1 1 1 1 0 0 2 2 1 1 0 0 1 -1 1 1 1 1 2 2 2 2 1 2 2 2 2 2 2 -1 2 2 2 2 1 1 1 1 1 1 2 -1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 1 1 2 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 2 0 0 1 -1 0 0 0 0 0 0 0 2 2 2 1 2 2 1 1 2 -1 -1 2 2 2 2 12 0 0 103.667 17 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 1 -1 1 1 2 -1 0 0 0 0 2 1 1 1 1 1 1 1 2 2 2 -1 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 1 1 1 0 1 1 1 2 1 2 2 2 2 2 1 2 2 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 0 12 2 2 2 2 2 2 2 2 2 2 2 0 0 0 1 1 0 0 0 0 0 0 -1 2 1 1 0 0 0 0 0 0 -1 -1 0 1 -1 0 0 0 1 128.4 18 1 2 2 1 1 -1 1 1 1 1 1 1 2 2 2 2 1 1 1 2 0 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 0 0 0 -1 0 0 0 0 1 1 2 2 2 2 0 0 0 1 1 -1 1 1 1 0 0 0 1 1 1 12 2 2 2 2 2 2 2 2 -1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 -1 2 1 -1 1 0 0 0 122.25 19 1 1 1 0 1 1 1 2 2 2 2 2 -1 2 1 1 0 0 0 1 0 0 1 0 1 0 0 -1 1 0 0 0 0 0 0 0 0 -1 0 0 0 -1 0 1 1 0 1 1 1 1 1 1 1 2 2 2 1 1 1 1 0 0 2 2 2 1 1 0 0 0 0 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 0 12 2 2 2 2 2 2 1 1 1 1 1 0 1 1 2 1 0 0 0 0 0 0 -1 1 2 2 2 2 2 -1 1 1 1 -1 1 2 2 2 -1 0 1 264 20 1 1 1 1 1 0 0 0 0 0 0 -1 1 1 1 1 1 0 0 0 0 0 0 0 -1 0 0 1 1 1 2 1 1 1 1 1 1 -1 1 0 0 0 0 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 1 1 -1 1 1 1 1 1 0 0 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 -1 12 1 1 1 1 0 0 0 0 0 0 0 2 1 0 0 2 2 2 2 2 2 1 1 1 1 0 0 1 1 1 2 0 0 -1 0 2 2 2 12 0 1 72.6 21 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 2 2 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 -1 0 0 0 -1 0 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 1 2 1 1 1 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 2 2 12 1 1 1 1 1 1 1 1 1 1 1 2 1 0 0 2 2 2 2 1 1 1 1 1 1 0 0 2 2 2 1 1 1 -1 1 1 1 1 12 1 0 264 22 1 2 2 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 0 0 1 2 2 2 1 1 2 2 2 2 0 0 1 1 1 1 1 -1 1 1 1 -1 2 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 0 0 0 1 1 2 2 2 2 0 0 0 1 2 2 2 0 0 0 0 0 1 1 2 2 2 2 2 2 1 1 1 1 12 1 -1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 -1 1 -1 -1 1 0 0 0 0 1 0 264 23 1 0 0 0 0 0 0 1 1 2 2 2 2 2 -1 0 0 0 0 1 0 1 1 0 0 0 2 2 2 1 0 0 0 0 0 0 0 -1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 2 2 2 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 0 1 1 1 1 1 1 0 0 0 0 1 1 12 -1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 -1 0 0 0 1 12 0 0 264 24 1 1 1 1 -1 1 1 1 1 1 1 1 1 0 1 1 2 2 2 0 1 1 1 1 2 2 1 1 0 2 -1 2 2 2 2 2 2 -1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 -1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 12 -1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 2 2 2 2 2 2 2 2 0 0 0 0 1 1 1 2 0 0 -1 0 1 1 1 12 1 1 81.717 25 1 -1 1 1 1 1 1 2 2 2 2 2 2 2 1 0 0 -1 1 1 0 0 0 0 0 0 1 1 1 1 2 -1 2 2 1 1 1 1 1 0 0 0 0 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 -1 0 0 0 0 1 2 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2 1 1 1 2 1 1 -1 2 1 2 1 12 0 0 264 26 1 -1 1 2 1 1 1 1 1 1 1 1 1 1 0 2 2 -1 1 0 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 2 2 1 1 1 1 -1 1 1 1 -1 1 1 1 0 2 2 1 -1 1 1 1 0 0 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 0 0 0 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 2 1 1 1 0 1 1 1 1 1 1 1 2 2 2 2 1 1 2 1 1 1 -1 1 2 2 1 12 -1 1 264 27 1 -1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 -1 1 1 0 1 1 1 1 1 0 -1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 -1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 1 -1 1 1 1 2 12 0 0 116.483 28 1 -1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 -1 0 0 1 0 0 0 0 1 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 0 0 1 1 -1 1 1 1 1 1 1 2 1 1 1 0 -1 2 2 2 2 2 2 1 2 2 2 2 2 1 0 0 1 1 1 1 2 2 2 2 2 0 0 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 0 0 -1 0 2 1 1 12 1 0 87.467 29 1 1 1 1 0 -1 1 1 1 1 1 1 1 1 -1 0 1 1 1 2 2 2 2 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 -1 1 1 1 1 2 2 2 -1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 0 1 1 1 2 1 2 2 2 1 1 1 0 0 0 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 0 0 0 -1 0 1 1 1 12 0 1 264 30 1 0 0 0 1 1 2 1 1 1 1 1 1 1 -1 1 1 1 -1 1 1 2 2 1 1 1 2 1 2 1 -1 1 1 1 1 1 1 1 1 1 0 -1 1 0 1 1 1 1 1 -1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 2 2 2 1 1 -1 1 1 2 0 1 1 1 0 1 1 1 1 1 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 1 1 1 1 2 1 1 -1 1 1 1 1 12 1 1 . 31 1 0 0 1 1 1 1 0 0 0 0 0 1 1 2 2 2 1 1 0 1 0 0 1 1 1 1 1 2 1 1 1 0 0 0 0 0 0 0 0 0 -1 0 2 1 1 1 1 1 -1 1 1 1 1 -1 1 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 1 1 2 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 1 1 1 1 1 1 1 1 1 1 0 1 2 2 1 1 1 2 0 0 -1 0 1 1 1 12 1 1 74.417 32 1 0 0 1 1 2 2 1 1 1 1 0 0 0 -1 1 2 2 2 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 -1 2 2 2 2 2 2 0 0 0 0 1 -1 2 2 2 1 1 -1 1 1 1 0 0 0 0 2 1 1 1 2 0 -1 1 1 0 0 2 2 2 2 0 0 2 2 -1 1 1 1 -1 1 1 0 1 0 1 2 2 1 0 0 1 1 1 1 2 1 1 1 1 1 0 0 1 1 0 0 -1 0 1 1 1 12 1 1 264 33 1 2 2 1 0 0 0 0 0 1 1 1 1 1 2 2 2 2 1 1 2 1 1 0 0 0 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 0 1 -1 1 1 1 1 2 2 2 2 1 1 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 2 -1 1 1 1 1 12 1 1 264 34 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 0 0 1 0 1 1 1 1 1 2 -1 1 2 1 1 1 1 1 1 2 1 2 2 2 2 2 1 1 1 1 1 1 -1 1 1 0 0 1 1 1 1 0 0 0 -1 1 1 1 1 1 -1 1 2 2 2 2 2 2 0 0 0 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 -1 1 2 1 1 12 1 1 174.567 35 1 2 2 2 2 2 2 2 2 2 2 2 1 -1 1 1 1 1 0 1 1 0 1 1 2 2 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 2 2 -1 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 0 0 2 2 1 0 1 1 0 0 0 0 0 0 0 0 0 -1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 2 2 2 2 2 2 -1 2 1 0 0 12 0 1 88.583 36 1 1 1 2 2 2 2 2 2 2 2 2 1 -1 0 1 1 2 2 2 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 0 0 0 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 -1 1 1 1 1 1 2 2 2 2 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0.01000 -1 -1 -1 -1 -1 0 0 *D16M139 0 0.01000 -1 -1 -1 -1 -1 0 0 *D16M86 0 0.01000 -1 -1 -1 -1 -1 0 0 *D17M260 0 0.01000 -1 -1 -1 -1 -1 0 0 *D17M66 0 0.01000 -1 -1 -1 -1 -1 0 0 *D17M88 0 0.01000 -1 -1 -1 -1 -1 0 0 *D17M129 0 0.01000 -1 -1 -1 -1 -1 0 0 *D18M94 0 0.01000 -1 -1 -1 -1 -1 0 0 *D18M58 0 0.01000 -1 -1 -1 -1 -1 0 0 *D18M106 0 0.01000 -1 -1 -1 -1 -1 0 0 *D18M186 0 0.01000 -1 -1 -1 -1 -1 0 0 *D19M68 0 0.01000 -1 -1 -1 -1 -1 0 0 *D19M117 0 0.01000 -1 -1 -1 -1 -1 0 0 *D19M65 0 0.01000 -1 -1 -1 -1 -1 0 0 *D19M10 0 0.01000 -1 -1 -1 -1 -1 0 0 *DXM186 0 0.01000 -1 -1 -1 -1 -1 0 0 *DXM64 0 0.01000 -1 -1 -1 -1 -1 0 0 *Classes: *no_class * * * * * * * * * * *Chromosomes: 20 *chr1 13 -1 0 0 -298.980 0 1 2 3 4 5 6 7 8 9 10 11 12 0.0099 0.1897 0.1338 0.0872 0.0273 0.1463 0.0069 0.0891 0.0077 0.0342 0.0718 0.0095 0 0 0 0 0 0 0 0 0 0 0 0 *chr2 6 -1 0 0 -236.885 13 14 15 16 17 18 0.2141 0.1592 0.1659 0.0917 0.1180 0 0 0 0 0 *chr3 6 -1 0 0 -188.552 19 20 21 22 23 24 0.2389 0.1024 0.1195 0.0529 0.0710 0 0 0 0 0 *chr4 4 -1 0 0 -130.364 25 26 27 28 0.1592 0.1381 0.2404 0 0 0 *chr5 13 -1 0 0 -255.946 29 30 31 32 33 34 35 36 37 38 39 40 41 0.0575 0.1154 0.0032 0.0400 0.0175 0.0512 0.0000 0.0197 0.0491 0.0561 0.0650 0.0979 0 0 0 0 0 0 0 0 0 0 0 0 *chr6 13 -1 0 0 -225.672 42 43 44 45 46 47 48 49 50 51 52 53 54 0.0755 0.0537 0.0672 0.0963 0.0324 0.0233 0.0358 0.0040 0.0352 0.0357 0.0036 0.0137 0 0 0 0 0 0 0 0 0 0 0 0 *chr7 6 -1 0 0 -200.373 55 56 57 58 59 60 0.1566 0.1378 0.0576 0.1586 0.1065 0 0 0 0 0 *chr8 6 -1 0 0 -179.468 61 62 63 64 65 66 0.0132 0.0913 0.1349 0.0552 0.1503 0 0 0 0 0 *chr9 7 -1 0 0 -194.112 67 68 69 70 71 72 73 0.0405 0.0947 0.1114 0.0533 0.1097 0.0668 0 0 0 0 0 0 *chr10 5 -1 0 0 -185.157 74 75 76 77 78 0.1952 0.1367 0.0741 0.1092 0 0 0 0 *chr11 6 -1 0 0 -198.605 79 80 81 82 83 84 0.1307 0.1009 0.1075 0.0351 0.1792 0 0 0 0 0 *chr12 6 -1 0 0 -183.318 85 86 87 88 89 90 0.0581 0.1326 0.0697 0.1122 0.1118 0 0 0 0 0 *chr13 12 -1 0 0 -202.550 91 92 93 94 95 96 97 98 99 100 101 102 0.0029 0.0913 0.0261 0.0000 0.0553 0.0206 0.0372 0.0127 0.0218 0.0000 0.0704 0 0 0 0 0 0 0 0 0 0 0 *chr14 4 -1 0 0 -133.607 103 104 105 106 0.1900 0.0814 0.1126 0 0 0 *chr15 8 -1 0 0 -174.188 107 108 109 110 111 112 113 114 0.1180 0.0505 0.0057 0.0435 0.0120 0.0579 0.1043 0 0 0 0 0 0 0 *chr16 4 -1 0 0 -120.598 115 116 117 118 0.1425 0.0862 0.1338 0 0 0 *chr17 4 -1 0 0 -125.809 119 120 121 122 0.1045 0.0530 0.1748 0 0 0 *chr18 4 -1 0 0 -111.361 123 124 125 126 0.0068 0.1391 0.0377 0 0 0 *chr19 4 -1 0 0 -110.565 127 128 129 130 0.1396 0.1403 0.1040 0 0 0 *chrx 2 -1 0 0 -91.297 131 132 0.2856 0 *Assignments and Placements: *D10M44 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M3 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M75 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M215 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M309 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M218 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M451 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M504 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M113 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M355 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M291 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M209 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D1M155 5 0 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D2M365 5 1 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D2M37 5 1 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D2M396 5 1 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D2M493 5 1 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D2M226 5 1 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D2M148 5 1 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D3M265 5 2 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D3M51 5 2 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D3M106 5 2 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D3M257 5 2 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D3M147 5 2 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D3M19 5 2 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D4M2 5 3 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D4M178 5 3 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D4M187 5 3 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D4M251 5 3 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M148 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M232 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M257 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M83 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M307 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M357 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M205 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M398 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M91 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M338 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M188 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M29 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D5M168 5 4 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M223 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M188 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M284 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M39 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M254 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M194 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M290 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M25 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M339 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M59_ 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M201 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M15 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D6M294 5 5 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D7M246 5 6 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D7M145 5 6 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D7M62 5 6 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D7M126 5 6 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D7M105 5 6 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D7M259 5 6 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D8M94 5 7 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D8M339 5 7 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D8M178 5 7 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D8M242 5 7 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D8M213 5 7 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D8M156 5 7 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M247 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M328 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M106 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M269 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M346 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M55 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D9M18 5 8 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D10M298 5 9 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D10M294 5 9 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D10M42_ 5 9 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D10M10 5 9 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D10M233 5 9 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D11M78 5 10 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D11M20 5 10 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D11M242 5 10 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D11M356 5 10 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D11M327 5 10 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D11M333 5 10 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D12M105 5 11 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D12M46 5 11 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D12M34 5 11 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D12M5 5 11 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D12M99 5 11 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D12M150 5 11 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M59 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M88 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M21 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M39 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M167 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M99 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M233 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M106 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M147 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M226 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M290 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D13M151 5 12 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D14M14 5 13 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D14M115 5 13 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D14M265 5 13 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D14M266 5 13 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M226 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M100 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M209 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M144 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M68 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M239 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M241 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D15M34 5 14 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D16M154 5 15 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D16M4 5 15 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D16M139 5 15 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D16M86 5 15 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D17M260 5 16 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D17M66 5 16 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D17M88 5 16 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D17M129 5 16 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D18M94 5 17 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D18M58 5 17 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D18M106 5 17 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D18M186 5 17 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D19M68 5 18 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D19M117 5 18 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D19M65 5 18 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *D19M10 5 18 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *DXM186 5 19 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *DXM64 5 19 -1 0.000000 0.499600 | -1 0.000000 -999.987650 1 0 *MapmakerStatusInfo: *PrintNames: 0 *Tolerance: 0.001000 *Units: 1 *MapFunc: 0 *DefaultLOD: 3.000000 *DefaultTheta: 0.316060 *UseThreePoint: 1 *TripletLOD: 3.000000 *TripletTheta: 0.316060 *TripletNumLinks: 2 *ThreePointThreshold: 4.000000 *ThreePointWindow: 9 *TripletErrorRate: 0.000000 *NptThreshold: 2.000000 *NptFirstThreshold: 3.000000 *NptWindow: 7 *NptMinIndivs: 1 *NptCodominant: 0 *NptMinTheta: 0.009007 *PrintAllMaps: 0 *UseErrorRate: 0 *ErrorLodThreshold: 1.000000 *ErrorSingleThreshold: 2.000000 *ErrorNetThreshold: 3.000000 *Contexts: 1 *ActiveContext: 0 *Context 1 *SexSpecific: 0 *CompressDNA: 1 *UseNum: 0 *MaxProblemSize: 10000000 *SavedNames: 0 *SequenceHistory: 20 0 D10M44 D1M3 D1M75 D1M215 D1M309 D1M218 D1M451 D1M504 D1M113 D1M355 D1M291 D1M209 D1M155 1 D2M365 D2M37 D2M396 D2M493 D2M226 D2M148 2 D3M265 D3M51 D3M106 D3M257 D3M147 D3M19 3 D4M2 D4M178 D4M187 D4M251 4 D5M148 D5M232 D5M257 D5M83 D5M307 D5M357 D5M205 D5M398 D5M91 D5M338 D5M188 D5M29 D5M168 5 D6M223 D6M188 D6M284 D6M39 D6M254 D6M194 D6M290 D6M25 D6M339 D6M59_ D6M201 D6M15 D6M294 6 D7M246 D7M145 D7M62 D7M126 D7M105 D7M259 7 D8M94 D8M339 D8M178 D8M242 D8M213 D8M156 8 D9M247 D9M328 D9M106 D9M269 D9M346 D9M55 D9M18 9 D10M298 D10M294 D10M42_ D10M10 D10M233 10 D11M78 D11M20 D11M242 D11M356 D11M327 D11M333 11 D12M105 D12M46 D12M34 D12M5 D12M99 D12M150 12 D13M59 D13M88 D13M21 D13M39 D13M167 D13M99 D13M233 D13M106 D13M147 D13M226 D13M290 D13M151 13 D14M14 D14M115 D14M265 D14M266 14 D15M226 D15M100 D15M209 D15M144 D15M68 D15M239 D15M241 D15M34 15 D16M154 D16M4 D16M139 D16M86 16 D17M260 D17M66 D17M88 D17M129 17 D18M94 D18M58 D18M106 D18M186 18 D19M68 D19M117 D19M65 D19M10 19 DXM186 DXM64 qtl/inst/sampledata/map.txt0000644000175100001440000000345311562004332015477 0ustar hornikusers20 13 0.0100 0.2219 0.1508 0.0947 0.0280 0.1665 0.0069 0.0969 0.0077 0.0353 0.0769 0.0096 D10M44 D1M3 D1M75 D1M215 D1M309 D1M218 D1M451 D1M504 D1M113 D1M355 D1M291 D1M209 D1M155 6 0.2536 0.1828 0.1913 0.1000 0.1314 D2M365 D2M37 D2M396 D2M493 D2M226 D2M148 6 0.2857 0.1126 0.1333 0.0557 0.0759 D3M265 D3M51 D3M106 D3M257 D3M147 D3M19 4 0.1827 0.1562 0.2877 D4M2 D4M178 D4M187 D4M251 13 0.0574 0.1154 0.0033 0.0400 0.0175 0.0513 0.0000 0.0197 0.0490 0.0562 0.0650 0.0979 D5M148 D5M232 D5M257 D5M83 D5M307 D5M357 D5M205 D5M398 D5M91 D5M338 D5M188 D5M29 D5M168 13 0.0755 0.0537 0.0672 0.0963 0.0324 0.0233 0.0358 0.0040 0.0352 0.0357 0.0036 0.0137 D6M223 D6M188 D6M284 D6M39 D6M254 D6M194 D6M290 D6M25 D6M339 D6M59_ D6M201 D6M15 D6M294 6 0.1795 0.1559 0.0609 0.1821 0.1175 D7M246 D7M145 D7M62 D7M126 D7M105 D7M259 6 0.0134 0.0995 0.1522 0.0582 0.1715 D8M94 D8M339 D8M178 D8M242 D8M213 D8M156 7 0.0421 0.1035 0.1235 0.0561 0.1213 0.0712 D9M247 D9M328 D9M106 D9M269 D9M346 D9M55 D9M18 5 0.1952 0.1366 0.0741 0.1093 D10M298 D10M294 D10M42_ D10M10 D10M233 6 0.1471 0.1108 0.1187 0.0363 0.2083 D11M78 D11M20 D11M242 D11M356 D11M327 D11M333 6 0.0615 0.1493 0.0745 0.1244 0.1240 D12M105 D12M46 D12M34 D12M5 D12M99 D12M150 12 0.0029 0.0912 0.0262 0.0000 0.0552 0.0206 0.0372 0.0126 0.0218 0.0000 0.0705 D13M59 D13M88 D13M21 D13M39 D13M167 D13M99 D13M233 D13M106 D13M147 D13M226 D13M290 D13M151 4 0.2224 0.0879 0.1249 D14M14 D14M115 D14M265 D14M266 8 0.1315 0.0531 0.0057 0.0454 0.0121 0.0612 0.1149 D15M226 D15M100 D15M209 D15M144 D15M68 D15M239 D15M241 D15M34 4 0.1617 0.0935 0.1508 D16M154 D16M4 D16M139 D16M86 4 0.1152 0.0558 0.2028 D17M260 D17M66 D17M88 D17M129 4 0.0068 0.1391 0.0377 D18M94 D18M58 D18M106 D18M186 4 0.1580 0.1589 0.1146 D19M68 D19M117 D19M65 D19M10 2 0.3447 DXM186 DXM64 qtl/inst/sampledata/README.txt0000644000175100001440000000573011562004332015657 0ustar hornikusers Sample data for the R/qtl package These files contain sample data in several formats, so that the user may better understand how data may be formatted for import via the read.cross function. These are the same as the "listeria" data set included with the R/qtl package. Note: Replace the "..." in the directory string to the appropriate location of the sampledata directory (for example, "/usr/local/lib/R/library/qtl" or "c:/R/rw1081/library/qtl"). 1. "csv" format File: listeria.csv Data import: listeria.a <- read.cross("csv", ".../sampledata", "listeria.csv") 2. "csvr" format (rotated "csv") File: listeria_rot.csv Data import: listeria.a2 <- read.cross("csvr", ".../sampledata", "listeria_rot.csv") 3. "csvs" format (like "csv", but with separate files for phenotype and genotype data) Files: listeria_gen.csv Genotype data listeria_phe.csv Phenotype data Data import: listeria.a3 <- read.cross("csvs", ".../sampledata", "listeria_gen.csv", "listeria_phe.csv") 4. "csvsr" format (like "csvr", but both files are rotated) Files: listeria_gen_rot.csv Genotype data listeria_phe_rot.csv Phenotype data Data import: listeria.a4 <- read.cross("csvsr", ".../sampledata", "listeria_gen_rot.csv", "listeria_phe_rot.csv") 5. "mm" (mapmaker) format Files: listeria_raw.txt "raw" file (with genotype and phenotype data) listeria_map.txt Genetic map information (markers must be in order; map positions are not required) listeria_maps.txt Genetic map information, as produced by Mapmaker/exp Data import: listeria.b <- read.cross("mm", ".../sampledata", "listeria_raw.txt",,"listeria_map.txt") listeria.bb <- read.cross("mm", ".../sampledata", "listeria_raw.txt",,"listeria_maps.txt") 6. "qtx" (Mapmanager QTX) format File: listeria.qtx Data import: listeria.c <- read.cross("qtx", ".../sampledata", "listeria.qtx") 7. "qtlcart" (QTL Cartographer) format Files: listeria_qc_cro.txt Genotype/phenotype data listeria_qc_map.txt Genetic map information Data import: listeria.d <- read.cross("qtlcart", ".../sampledata", "listeria_qc_cro.txt", "listeria_qc_map.txt") 8. "karl" format Files: gen.txt Genotype data phe.txt Phenotype data map.txt Genetic map information Data import: listeria.e <- read.cross("karl", ".../sampledata", genfile="gen.txt", phefile="phe.txt", mapfile="map.txt") or just the following: listeria.e <- read.cross("karl", ".../sampledata") qtl/inst/sampledata/listeria_raw.txt0000644000175100001440000004600711562004332017411 0ustar hornikusersdata type f2 intercross 120 133 1 symbols a=A h=H b=B c=C *D10M44 b--bhhhhabaaahahabhhhbah----haaabbbhhbbhbabhahhhh-b-abahhbhh bhhhahhhahhbh-b-h-aahbhhhhabahah---h---baa-b-abbhabhahbhahaa *D1M3 bbhbhhhhabhaahahabhhhbahhhhhhaaabbbhhbbbbabhahhhhhbb-b-h-bhh bhhhahhhahhbhhbhhhaahbhhhhabahahhhhhahbbaabbhhbbhabhahbhahaa *D1M75 bbhhhbhhhhhhhahbahahhhahhbhhhahhhbbbhbhbbabhaahahhbbhbhbhbhh bhhhhhhaahhbh-hahhhabahhhbabhh------------bb--bbb-----b--b-- *D1M215 hbhhhbhhbhhhhahbahhhaha-hhhaahhhabbbhbhbbaabhabahabbabhhhbhh bhhhhbhaahhbhhhahbbh-ahhhbhbahhhhhbhhhhahhbhhbbhbh-haabbbbah *D1M309 hhhhbbaabahhhahba-haahahhhha-hhbabbbhbabbaabhabahabbabbhabha bhbhhbhaahhbhhhhbbbhhahhhbabhhhhhabhhhaahhbhhahhbbahaabbbbah *D1M218 hhhhhbaabahhhahbahhaahahhhhahbhbabbbhbabbaabbabahabbabbhabha bhbhhbhaahhbhhhhbbbhbahhhbhbhhhhhab--haahhhhhahbbbahaahbbbah *D1M451 bhhbhbaahahhbahhhhbaahhhbhhahhahabbbhhabhahbbhbahabhabbbhhha bhbahbhhhhhhhhahbbhhbahhbbbhbhahhabhhhhaahhbaaabhbahaahbbbah *D1M504 bhhbhbaahahhbahhhhbaahhhbhhahhahabbbahabhahbbhbahabhabbbhhha bhbahbhhhhhhhhahbbhhbahhbbbhbaahhabhhhhaahhbaaabhbahaahbbbah *D1M113 hhbbhbhhhahhbahhhhbahhbhbhhahhahhbbbahabhaabbhbahabhabbbbhha hhbaahhhbhhahhahhbhababhhbbhbaahhabhhhhaabhhaahbhbahaahbbhah *D1M355 hhbbhbhhhahhbahhhhbahhbhbhhahhahhbbbahabhaabbhbahabhabbbbhha hhbaahhbbhhahhahhbhababhhbbhbaahhahhhhhaabhhaahbhbahaahbbhah *D1M291 hhbbhbhhhahhbahhhhb-hhbhbhhahhaahbbbahabhhabbhbhhabhabbbbhha hhhhah-bbhhahhahhbhababhhbbhbaahhah-hhh-abahaahbh-hhaahhbhah *D1M209 hhbbhbhh-a-h-ahahb-hh-bhbhaahhhahbhhahab-hah-h-hhhbhabbbbhha hhhhahhbbh-ahhahhbhababhhhbhbhaab-hhbhhaa-ahaahbhbhba-hhhhaa *D1M155 hhbbhbhhhahhbahahbbhhhbabhaahhhahb-----bhhahbhbhhhbhabbbbhha hhhhahhbbhhahhahhbhababhhhbbbhhabahhbhhaahahaahbhbhbaahhhhaa *D2M365 hbhabhbhaaabhhhbhbhhhh-hhahh--b-bbhabahaha-haahhaahaba-ahhhh hhba-bbhhhhhbbhbhhhbahhhbbhhhhhhhhhbhahhhhahbbbhhbhbaabhhaba *D2M37 abh-habhhaabhbhbbbhhhhahabhhahbhbhhhhbhaaahhahahhhhaahbhahhh babaabhhbahabhabhhhhhahhbabhhhhhaahbbaahbhhhbbhhbbabhhbhhabh *D2M396 ahhahabahhbhhbahhhahahababhbhhbbbhhhhbbaahhh-abhhhhhahbaah-h hhbahbhhbabahhabh-hhhahhbahhhhhbahahbhahbahhbbhhbbahhhhbbahh *D2M493 -----ab-hhbhhhah-haaahab----hhhbbahba-baah-a---------hb-habh hhbahb-h-a-a----hahhhhbhaahhahhbahhbbhhhhababbhabbhahbhbhhhh *D2M226 ah-h---ahhbhh-hahhaaaaabhhhah-hbhaababbh-hbaababhaabhhbabhb- hhbah-hbhahahhhahhhhhhbhaaaaahhhahhbbhhhhababbhahbhahbhbhhha *D2M148 aahhhahabhbhhhhahbhabahahahabhahhhhbabhhahbaabhbhahbhahabhbh hhbahbhbhahahhhahhbhhhbhaaaaahhhhhbhbhhhhabaabhahbhabhahhhha *D3M265 bbhbbbbhaahaabbhbaaabhahahahbhhhbahhhahaaabhahahhhhahbhhhhha hhhhaabhbhhhhaaahhhbhahbhhhhhhabhhhhhhaahahhaahabahhhhhhhhbh *D3M51 bbhhabhbhhahahb--aaahbhhahhabbahhhahhah-hahaahhhahhhbhhhhhhh hhhhhahbhhbhaaaabahbbaabaahahh------------------------------ *D3M106 bbhhabhhhhabahbhaahahbhhahhabbahhhhhhahhhhaaahhaahhhbahhhhab bhbahahbhhbhahhahahbbaabahhaaa------------------------------ *D3M257 bhhhabhhahhhahbhahaahbahahhahhhhahhhaahhhhaahhbaahhhbhhhhhab bhhahbhbbhbhahhahahhbahhahbaaaaahhhahhhahhbabah-hhhhh-ahhhhh *D3M147 bhbhabhhahhhabbhahh-hhabahhahhhhahbh---hhhaahhbaahhhbhhhahab bhhahbhbbhhhahhahhhhhahhahhaaaaahhhahhhahhbhbahhhhhhhaa-hhhb *D3M19 bhbhabhhaahhahbhahaahhabahhhhhhhahbhahahhhahbhbaahbbbhhhaaab bhhahbhbbhhhhbhahhhhhahhhhhhaa------------------------------ *D4M2 ahahahhhhbhhhhhbbaaahbbhhhahabhhhbhhahhhhbhhhhhahbhbhhhhhahh hhbbbhbhaabbaabhbahhhabbhhabbb------------------------------ *D4M178 hhahahh-hhhhhhhbhh-hhbbhhh-bhhhab-bbahhhbb-ah-hhh--h-hhhhhah hhbb-h-baahbbahhbah-hahb-ha-bb------------------------------ *D4M187 ahhhahhhhhhbhhbbhhhhhbbahbhhhbbabhbbahhbbbahhahhhhbabbahhhab hhbhhhbbhahbbhhbbhhbhahhhhabbb------------------------------ *D4M251 hhhhhhhbbhbbahbbhhahhbhbhbhhahhhbbbhabhhhbhbhahhahbabbhhhhab bahhhbbbbbhbhbhhhbhhbahahhbhahbahhahbhbhhhaaabhhahbahaabahhh *D5M148 aha--ahbhhhbhbahhhabhaa-bbhba-hahhbhbaabbhahbb--bhbhb-hhaaaa hahb-hhba-abhhh-a-hbbbahbbhhbahhhabahbbhhhhbabahbahhbhbhbhbh *D5M232 ahahaaabhhhbhbabhhahhaab-bhbahhahhbhbhabbhahbbaabhbhbbhhhaaa hahbhhbbhhhbbhhhaahhbbahbhhhbhhhhabhhb--h-hbaba-bahhb--hbhbh *D5M257 hhaaaaabhhhbhbhbhhahahabbhhbahaahhhhbhbbbhhhhbahbhbhbbahhaaa hhhbhhbbaaahbhhaaahabbahbabhbhhhhabhhbbhhahbababaahhbhhabhhh *D5M83 hhaaaaabhhhbhbhbhhahahabbhhbahaahhhhbhbbbhhhhbahbhbhbbahhaaa hhhbhhbbaaahbhhaaahabbahbabhbhhhhabhhbbhhahbhbabaahhbhhabhhh *D5M307 hhhaaaabahhbhbhbbhahahabhhhbahaahhhhbhbbbhhhhbahbhb-hbahhaha ahhbh-bbaaahbhhaaahabbahbabhbhhhhabhhbbhhaahhbabaahhb-hab-hh *D5M357 hhhaaaabahhbhbhbbhahahabhhhbahaahhhhbhbbbhhahbahbhbhhbahhahh ahhbhhbbaaahbhhahahabhahbabhbhhhhabhhbbhhaahhbabaahhbbhabhhh *D5M205 hhha-habahhbhbhbbhahahabhhhbahaahbhhbhbbbhhahbah--bhhbahhbbh ahhbhhbbahahbhaahahabhahbabhbbhhhbbhhbhhhaahhbabaahh-bhabhhh *D5M398 ------------------------hhhbahaahhhhbhbbbhhahbah--bhhbahhbbh ahhbhhbbahah-haahahabhahbabhbb------------------------------ *D5M91 hhhahhabahhbhbhbbhahahabhhhbahaahbhhbhbbbhhahbahbhbhhbahhbbb ahhbhhbhahahbhaahahabhahbabhbbhhhbhhhbhhhaahhbabaahhhbhabhhb *D5M338 hhhahhabahhbhbhbbhaaahhhahhbahaahbhhbhbbbhhahbahbbbhhbahhbbb ahhbhhhhabahbhaahahahhahbabhbbhhhbhhabhhhhahhbabahhhhbhahhhb *D5M188 hbhahhahahhbhbhbbhaaahhhahhbhaahhbhabhbbbhhaabahbbbahbahbbbb ahhbhhhhabhhbhhabahahhahbabhbbhhhbhhabhhhhahhbabahhhabhahbh- *D5M29 bbhah--h-hbb--hbbh-a--hhahhb---hhbha-hbbba-a-b-abb-aa-ahbbbb h-hbhh------bhhhbahahha-b-b-b------------------------------- *D5M168 bbaahhhhhhbbhhhhbhaaabahaahbhhahbbhabhbbbabaabaabhhaahahbbbh hbhbhhhhahhhbahhbaaahhbhbabbbbhhbbahaabhahhhhbabhhhbabhhhbhb *D6M223 aahhaahahhhhbhhhhahbbhhabbaahabhbhhbahahhbhbahhahhhhahbhhbhh haahabbhabbhhbhhhhhh-hbhahahhbhhhaahhhbabbaaahahbbbabaaabhbh *D6M188 aahhaahahhhhhbhhhahbhhhabbaahhhhbhhhabhhhbhbahhahhhhahbhhbhh haaaahhaabbhhbhhhhhhhhbbahahhh------------------------------ *D6M284 hahhaahahhhhhbhhbaabhhhahhaahhhhhhhhabhhhbhbahhahhhhahbhhhhh hhaaahhaahbhhbhhhhhhhhhbahahhhhhhahbhhbabhahhhahbhbahahabhbh *D6M39 hahhahhahhhhhbahbahbhhhahhaabhhhhhhhhbhhhbhbhhbahhhhahbbbhhh hhahahhahhbhhbhhhbhbhhhbahahhhhhhahbhhbahaahhhahbhbahahahabh *D6M254 habhabhhbhhhhbahbhhhhbhahhhhbhhhahhhhbhhhbhhhhbabhhhhhbbbhhh hhahhhhabhbhhbhbhbhbhhhbahahhahhhhabhhbahaahhhahhhbhhhhahhba *D6M194 habhabhhbhhhhbabbhhhhbhahhhhbhhhahhhhbhahbhhhhbahhhhhhbbbhbh hhahhhhabhbhhbhbhbhbhbabahahha------------------------------ *D6M290 habh-bh-bhahhba-bhhhhbhah-------a-hhhbhahb-ah-ba-h---h-bbhbh hh-hhhhabhbh---bhb-bhbabahahha------------------------------ *D6M25 habhhbbbhhahhbabbhhhhbhahhhhbhhbahhhhbhahh-ahhbahhbhhhbbbhbh hhahhhhabhbhhbabhbhbhbabahahhahhhhahhhbah-hhhaabhhbhh--hhhba *D6M339 habhhbbbhaahhbabbhhhhbhahhhhbhhbahhhhbhahhhahhbahhbhhhbbbhbh hhahhhhabhbhhbabhbhbhbabahahhahhhhahhhbahahhhaabhhbhhhbhhhba *D6M59_ bhbhhbbbhaahhbabhhhhhbhahhhhbhhbaahhhbhahhhahhbahhbhhhbbbhhh hhahhhhabhbhbbabhbhbhbabahahha------------------------------ *D6M201 bhbbhbbbhaahhbabhhbhhbhah-hhbhhbaahhhbhahhhahhbahhbhbhbbbhhh hhahhhahbhbhbbabhbhbhbahahahhahhahahhhhhhahhhaabahbhhhbhhhha *D6M15 bhbbhbbbhaahhbabhhbhhbhahhhhbh-bahhhhbhahhhahhbahhbhbhbbbhhh hhahhhahbhbhbbabhbhbhbahahahhahhahahhhhhhahhhaabahbhhhbhhhha *D6M294 bhbbhbbbhaahhbabhhbhhbhahhhhbhhbahhhhbhahhhahhbahhbhbhbbbhhh hhahhhahbhbhbbabhbhbhbahhhhhhhhhahahhhhhhahhhaabahbhbhbhhhha *D7M246 bhabbaahbhhhababahhhhhabhhhbbhbabhhhhbbhhhahbhbaahbaahhhhabb hhbbahhhhaahhbbhhhhahabhbaabab------------------------------ *D7M145 bbabhaahbaahhbhbhhhhhhhhhahhbababhhhhbbbbaaabhha--bahahhhabb hhbhhbhaha-hhbhhhhhhhhbabaabh-babhbahhhahbhbabhhhhhahhbhahaa *D7M62 hbabaaahbahhhhhbhhhbahbhhbhhbababahhbbbbbaaabhhaabbahhhhhahb hhbhhbhahhbhhhhhbhhhahhahahbhhbahhh-hhbahaahahhahh-ah-bhahbh *D7M126 hbabaahhbah-h-hbhbhbaab-hbhhbababahhbbbabaaabhh--bbahhahha-- habhhb-a----h-hhbhh-a-h-a-----ba-hhah-bab-ahaa-abhaahb-h-hbh *D7M105 hbabhahhbhhhhhbbbbahaabhbhhahhbhbahbbbbabaaabbhaabahahahbhhh bhhhhhbahhbbbabhbhhhabahahhhhhbahhhaahhhbaahaahabhhahbhbhhbh *D7M259 hbahhahbhh-hhhhbhbahhaha--b-hhb-b--bbbhahh-a--haa-ahaha-bhhh bhahhh----hbbahhbhhhahahahbhhh------------------------------ *D8M94 hhhbahbabbahhahbbhbbahahahhbbahbbhbhbahahahabhbhbbhhaahbhhhh hhhahahhbhbbhahhbbhhhhhhhhaahhhabhabbbhabbhhahbaabhbhaaabhhh *D8M339 hhhbahbabbahhahbbabbahahahbbbahbbhbhhahahahabhbhbbhhaahbhhhh bhhahahhbhbbhahhbbhhhhhhhhaahhhabhabbbh-bbhha-baabhbhaaabhhh *D8M178 hhhbabbh-bahhahbbabbabhhahbbbhhbbh-hhahahhhabhbhbbhhhaahahhh bhhhhahhbhbbha-hbhhhhhhh-aaahhhahhhbbbbabbhbhhbaahhbh-aahhaa *D8M242 hbbbabbhbhabhaabbahhhbhhaabbbhhhbhbhhahhahhabhaabbahhaahabbh bhhhhahbhhbhhahbhahhhhhhhaaaab------------------------------ *D8M213 bbhbabbhbhabhaabb-hhhbhhhabbbbhhbhbhaahhahhahhaahbahhaahabbh bbhhhahbahbhhahbhahhhhhbaaaaabhhh-hbbh-abbhbbabhahabhbaahhhh *D8M156 bbhhhhbhahahhhahhaa-bbhhbhhbhbh-b-bhahhhhh-a--aa-bah-a--abbh b-ab-a-ba-bh-ah-aahhbhab-ahhh-hbhhabbhbabahbbahhhhhbhbahhhhh *D9M247 bhbhbhbbhbhahbhhbaahhahhhhahhbahhhhhhhhbabhbhbbbahahhhhahhba haaabbbbhhhhahaabhhahaahahhbhbaahhbhahabhaabbhhhhhh-ahbbahba *D9M328 babhbhbbhbhahbhhbaahhahhhhabhhahhbbhhhhbabhbhbbbahahhhaaahba haaabbbbhhhbahaabhhahaahahhbhbaa-h--hh-b--aabhbhh-hbah---hba *D9M106 habhbhbbhbbahbahhaahhahhhhabhhahhbbhbhhbabbbhbbbhaahhhaahabh hhaabbbhbhhhhhhabhhahaahahhbhhhahhbahhhbhaahbhbbbhhbhhbbahba *D9M269 hhbhhhababbaabahhhhhahhhhhabh-aahbbhbhhhab-ba--bhaabhhaahah- h-aa-hbhbhhh-hhabbhabahhabhhhh------------------------------ *D9M346 hhbhhhababbaabahhhhhabhhhhabhhhahbbhbhbhhhbbabbb-------a---- ---a--------hhhabbhahahbabhhhhhahabhbbhbhhahbhbbbaabhhbbaaba *D9M55 hhbhhhabahbaabahabhaabhhhhabhhhahbbhbhbhhhabahbhhahbbhhhhaah baaahhbahhbahahahbaahahbabhhha------------------------------ *D9M18 hhbhhhahaabaahahabhaabhhhhahhbbahbbbbhbhbhabahbhhahhbhhhhaah baaahhbahhbahahahhaahhababbhha------------------------------ *D10M298 hahaahhahaabhbhhabhhhahhabhaaahbaabhhhahhhhahahhabbhbhhahhaa habhaaaahhhahahhhbba-haaahhaaahhhhabbhhbhhbhhabbahhhahbbahhh *D10M294 haahhhhabhhbahahabbbhahaabhahhhhhahbhhhaahhaaahhabbhbhhhhhha habhahaahbhahahbabhahhhhahaaaa------------------------------ *D10M42_ baabhhh-bhhbhhahaabbhahhabhhhhah-ahbhhhaahahahhh-hhhbhb-bhha babhhhhhhbhabaababhhhhhhahahaaabhhhhahbhhabaahhabhhhbhahhhbh *D10M10 baahhhhabhhbhhabaabbha-hhhhhhhahhhhbhbhaahahahhhaahabhbhbhha bhhhhhhhhba-haahabhhhhhhahabaaabhhhhhbahh-b-abhab-hh---h-h-- *D10M233 baahhhhabhabhhbbhabhhaabhhhhbaabhhhbhbbaahahhbhhaaahhhbbhhha bhhhbhhbhbhahaahabbhbhhhahabaaabhhhhhhabhabaahhhbhhhbhhhhhbh *D11M78 hahbbhahbaababhhahbhahhbhhhhhhaahhhhbaahhhbhhhhbaaahhahhhaab bbhhaahbbabhhhhhhbhbabhahbhahh------------------------------ *D11M20 hahbbbabbhhbahahahbbahhbhhabbha-hhhhbaahbhbhhaabaa-hbh-hhhhb bbhbaahbbhhhhhhhhbhbabahhahahhhbhbbhhaahbhhhhhhhbhhbhhahhhhh *D11M242 hahbbbahhhhbahaba-hbhbhbhhabbhhhbhhhhhahb-bbhaabaaahbhahhbhh bbhba-hbbhabhhhhhbhhabahbahhaa--hbbh-aah-bhhh-hhbhhb-hhhhhhb *D11M356 aahbbbhbhhhbahhhahhbhbhbbhabbhhhbhhhhhhhbhbbhaahhbaabbahhhhh bbhbhahhbhabbhhbhbhha-abbaahaa------------------------------ *D11M327 aahbbbhbhhhbahahahhbhbhbbhabhhhabhahhhhhbhbbhaahhbaabbaahhhh bbhbhahhbhabbhhbhhbhababbaahaaahhbbahaahhbahhhhabhhbhhbbbbhb *D11M333 hahhhbbhhbhhhaabbhhbhbhbhhabhhhabbahhhhhbhbbahhhhbhhbbaaaaha aahbhahhbhabbbbhhahhabhhbahhahhhabbah-hha-hbaahabhh-ahbbbhhb *D12M105 babaabbaaaahahhhaahbabahhahahbabhhbhhahaahbhabhhaabahaahaaba hbhbhbbhhabhbhbbhhahahhbhbbhah------------------------------ *D12M46 babaabhha-aaahhhaahbabahhahaahabhhbhhahaaab-abhhaa-aaaahaaha hbababbhhabhbhbbhhhhahhbhbbhah------------------------------ *D12M34 bhhhahahhaaaahhhaahbahahhahhahabbhhhhahhaahbhhhhaahaaaa-aaha ahahhbbhhh-hbabbhhhhaahhhhbhabahbhhhbhhhhbaaahhabhbhbahhhhbh *D12M5 bbhaahahhaha-hhhahhbahahhhhbahabbhahhahhaahbahbhaahaaaahhaha ahaahbbhbhbababbbbhhaaahhhbhabahbhh-bhbhh-aaahhab--hbahhh-bh *D12M99 bbhaahahhahaahhhahhhbhhhbhhbhhaabhhhhabhaahhahhhaahahaabhaha ahaahbbhbhbabahbbbbhaaahhhbahh------------------------------ *D12M150 bhbaahahhabhhbhhaha-bhhhbhhbhhaabhh-hhbbaa-hah-haabahaabha-h ahhabbbbbhbabahhbbbhaaaahbbabhahhhhhbahahaahhhbhbbbhhhbahahb *D13M59 caacccccaccacccaccccccccbbhhhaabahahahbabbhhhhhhcccacccccaaa cacccacacacccaahbhccacaacccaacccacaaacaccccccccaccccccaacccc *D13M88 -aahhhhhahh-hbhabbbhhh--bbhhhaabahahahbabbhhhhhhabbabhhhbaaa hababahahahbbhahbhhhahaahb-a-hhhahaaahahhhhhhhhahhhhhhaahhhh *D13M21 hhahh-bhahhaabaabbbhh-hhbbhhhaa-a-ah-hhab-b---hahbb-bah-b-a- b-b-b-aahaabbhahbhbaabaah-haahhhahaahhahhhhhaahahhhhhhaahahh *D13M39 hhahhhbaahhaabaabbbhhhhhbbbhhaahahahahhab-bhhahahb-abahhbaaa hhbbbaaahaabbhahbhbaahaahahaah------------------------------ *D13M167 hhahhhbaahhaabaabbbhhhhhbbbhhaahahahahhabhbhhahahbbabahhbaaa hhbbbaaahaabbhahbhbaahaahbhaa------------------------------- *D13M99 ahahhhbaahhaabahbbbahhhabhbhhaahahabahhabhbhhahabbbabahhbaaa hhbbbaaahaabbhahbhhahhaahahaahhhahaahaahabhhaaaahbahhhaahahh *D13M233 a-ahhhb-ahhaabahbbbahhhahhbh-aa-ahabahhabhbhhahabbbab--ab--a h-bbbaaahaahbhahbhh--ha-h-haahhhahaahaahabhbaaahhbahhhaa-ahh *D13M106 ahhhhhbaahhhabahbbhahhhahhbhhaahahabahhabhbhhhhabbhabahabaaa hhbbbaaahaahbhahbhhahhaahahaaaahahaahaahabhbaaahhbahhhahhhhh *D13M147 ahhhhhbaahhhabahbbhahhhahhbhhaahahabahhabhbhhhbabbhabahabhaa hhhbbaaahaahbhahbhhahhaahahaaaahahaahaahabhbaaahhbahhaahhhhh *D13M226 ahhhhhb-ahh-hbhhb-hahhhahhbhhaaaah---h--b-bhh-habbhabahab--- hh-bb-aa--ahbhahbhhahha-h--aaa-hahaah--habh---ahhbahh-hh-hhh *D13M290 ahhhhhbaahhhhbhhbbhahhhahhbhhaahahabahaabhbhhhhabbhabahabhaa hhhbbhaahaahbhahbhhahhaahahaaaahahaa-aahabhbaaahhbahhahhhhhh *D13M151 ahhhhhbhahhhhbhhbbhahahahhbhhabaahhbahaahhbbhhhabbhabaaabhhh hhhbbhaabaahbhahbhhahhaahahaaa-aabahhhaaabhbaaahhbahhhbhhhhh *D14M14 babhhaahbabbbhabahabbhahhbbbhhhhhhhhahahhahhh-bhahbbhhbhhbba hahahhhhhabb-hhbbbahhhhhaahhab------------------------------ *D14M115 bbbahahbbhhbbhaaahhhhaahhhhhhhhbhhhhahaa-a-a----hhbbhhbahhbb hahahhh---bbhbbbbbhhahahhahhabahbhbhahbahhhhbhbaahahhhhaahhb *D14M265 hbbahahhbbhbbhhaahhaaaahhhhhhhhbhbhhahaahabhbhaahabhhhbahhhb hahaahbbhhhbhbbhbbhhahahhahaabahbhbhah--hh-hbhhaa-ahhhhhahhb *D14M266 hbhhhahhbhhbbhhhhhbaahahhhhhhhhhhbhhahhahbbhbhhahabhhhhahhhb haahhhbbhhhhabbhbhbaahaahahaab------------------------------ *D15M226 abbhahabhhahah--hhhbbhhbhabhbhhahahbhh--hhhaaabhhhahahhahhhb hb--hhhhhbbaahhbahahhhbbba--hbabaahhhhhbhhhhabhabhabbbbahahh *D15M100 hhbbahabahahab-aahabbhabhhbbhhhahahhhhbbh-------hhabhhhaahhb abhb------bhhhhbahabhhh-ba---babaahhhhhbhahbabhhbhabbhbaaahh *D15M209 hhbhababahahabaaahabbhabhhbbhhhhhahhahbbhahaaahhhhhbhahaahbb abhbahbhhbb-hhhbhhabhhhbbahbhbhbaahhhhhbaahbabhhbhhbbhbaaahh *D15M144 hhbhababahahabaaahabbhabhhbbhhhhhahhahbbhahaaahhhhhbhahaahbb abhbahbhhbbhahhbhhabhhhbbahbhb------------------------------ *D15M68 hhbhabahahahabaaahabhhabhhbbhhhhhahhahbb-ahhaa-bhhhbhahaahbb abhbabbahbbhahhbhhabhahbbahbhbhbaaahhhhbaahbhb-hbhhbhhbaaahb *D15M239 hhbhabahahahhbaaahabhhabhhbbhhhhhahhahbbhahhaahbhhhbhahaahbb abhbabhahbbhahhbhhabhahbbahbhbhbaaahhhhbaahbhbhhbhhhhhbaaahb *D15M241 hhbaabahahahhbaaahahhhabbhbbhbhbhahhahbbhahhhah-aahbhahaahbb abhhabhaa-b-ahhbhhabhah-hah-hhhbhaahhhhbaaabhbhhbhhhhhbaaahb *D15M34 hhbaab-hahhhhb-a-h-hhhabbhhbbbhhhahaahhb-abbh-hbaab-babaahb- ahb-abbaa-b-ahhbhh-bbaahhahbah------------------------------ *D16M154 ahbaahbbhbbbbhhbbahhhaaahbahhaahhahhabhaahbbaabhhaahbabhhbbh hhbaaabhbhhahhbbaahhbhbahhbbaa------------------------------ *D16M4 ahbhbhhbbbbbhahbhabhhaaahbabhahhhaahhbhaahbbaabhbaahbabh-hhh hhbhaahbbhhhbabhahahhhbhbhbbaaahbaahhbaaahahbhbhhhbbhaahhhhh *D16M139 ahbhbhhbbbbbhahbhabaahaabbabhabhhaahbbhaahbbaabhbahhhahhhhhh hhbhahhbbhhh-ahaahahhabhhhabaa------------------------------ *D16M86 aabbbhhbbhhhhahhaabaahhabbabhhbhhaahbbhahhhbahbhbahahahahbhh habbhhhbbhhhhhhahhhabahhhhahaa------------------------------ *D17M260 hhaaahhhaahhhbhbaabhbhhhhhabhhhahhbahhbhabhhahhaabhhhaababhh hbhhhhabbhhhhhhhbbhabbbahbaabb------------------------------ *D17M66 hhahahahaabhhbhbahbhbhhhhhabhhhahabahhbhabhhhahaabhhhaahabbb hbhhhhhbhhhbhhhhhbbabhbbhbabhb------------------------------ *D17M88 hhahahahhabhhbhhah-hbhhhhbabhhhhhhbahhbhabhh-a-aabhbhhahabbb hhhhhahbhhhbhhhhhbbabhhbhbabhb---b---a-ah-h-hhabh-hahaa--bbh *D17M129 hhabhbhhhabhhbhhahhbh-hbbhahabbhahbahhbhabh-hahhahhbhhahahbb hhahbahbahhbbahahhbhhahbbbabhhbbhbaabhbahhabhhaa-hahhhahhhbb *D18M94 bbhaahhabaahhabbahhahhaahhaaahaabhbhhbhaabbaabbbhhhhahhbahbb hhhhbhbahahahhahhhbbaaahhhahah------------------------------ *D18M58 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2 0 2 1 1 0 0 0 0 1 2 2 2 3 3 3 3 3 3 2 2 2 2 2 1 1 1 2 2 2 2 0 0 2 2 0 0 0 0 2 2 2 2 2 0 2 2 0 2 2 3 0 3 2 0 2 1 2 0 0 2 2 0 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 2 2 0 1 0 0 2 0 2 2 2 0 2 2 0 3 2 0 0 2 0 0 1 5 2 1 0 0 1 1 2 2 2 2 2 0 2 2 0 1 1 1 0 1 1 1 0 0 2 0 0 0 0 3 2 0 2 2 3 0 0 0 0 2 2 1 1 0 1 1 1 1 1 1 1 1 1 1 3 3 2 2 2 2 3 0 0 2 2 0 0 0 0 2 3 3 2 2 2 2 2 0 2 2 2 0 2 3 0 3 3 3 0 0 3 3 0 2 2 2 0 1 3 3 3 0 2 2 1 0 2 2 3 3 3 0 3 0 0 2 0 3 0 3 0 2 2 0 2 2 0 0 3 3 0 2 5 2 2 0 0 2 2 2 2 2 2 2 0 2 2 0 2 2 2 0 2 2 2 0 0 2 0 0 0 0 3 3 0 2 2 2 0 0 0 0 2 1 1 1 0 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 1 2 0 0 2 3 0 0 0 0 2 2 2 2 2 2 2 2 0 3 3 0 0 3 2 0 2 2 1 0 0 1 1 0 1 1 1 0 1 2 2 2 0 2 2 1 0 2 2 1 1 1 0 1 0 0 2 0 2 0 2 0 2 3 0 3 3 0 0 2 2 0 3 5 2 2 0 0 2 2 2 2 2 2 2 0 3 3 0 2 2 2 0 3 3 3 0 0 2 0 0 0 0 2 3 0 2 2 2 0 0 0 0 1 1 qtl/tests/listeria.map0000644000175100001440000000402011562004334014537 0ustar hornikusers1 D10M44 0.00 1 D1M3 1.00 1 D1M75 24.85 1 D1M215 40.41 1 D1M309 49.99 1 D1M218 52.80 1 D1M451 70.11 1 D1M504 70.81 1 D1M113 80.62 1 D1M355 81.40 1 D1M291 84.93 1 D1M209 92.68 1 D1M155 93.64 2 D2M365 0.00 2 D2M37 27.94 2 D2M396 47.11 2 D2M493 67.26 2 D2M226 77.40 2 D2M148 90.86 3 D3M265 0.00 3 D3M51 32.48 3 D3M106 43.94 3 D3M257 57.59 3 D3M147 63.19 3 D3M19 70.84 4 D4M2 0.00 4 D4M178 19.16 4 D4M187 35.32 4 D4M251 68.10 5 D5M148 0.00 5 D5M232 6.10 5 D5M257 19.22 5 D5M83 19.55 5 D5M307 23.72 5 D5M357 25.50 5 D5M205 30.90 5 D5M398 30.91 5 D5M91 32.91 5 D5M338 38.07 5 D5M188 44.02 5 D5M29 50.98 5 D5M168 61.88 6 D6M223 10.00 6 D6M188 18.19 6 D6M284 23.87 6 D6M39 31.09 6 D6M254 41.80 6 D6M194 45.15 6 D6M290 47.53 6 D6M25 51.25 6 D6M339 51.65 6 D6M59_ 55.30 6 D6M201 59.01 6 D6M15 59.37 6 D6M294 60.76 7 D7M246 0.00 7 D7M145 18.79 7 D7M62 34.91 7 D7M126 41.03 7 D7M105 60.11 7 D7M259 72.08 8 D8M94 0.00 8 D8M339 1.34 8 D8M178 11.42 8 D8M242 27.14 8 D8M213 32.99 8 D8M156 50.86 9 D9M247 0.00 9 D9M328 4.22 9 D9M106 14.72 9 D9M269 27.32 9 D9M346 32.96 9 D9M55 45.34 9 D9M18 52.50 10 D10M298 0.00 10 D10M294 24.75 10 D10M42_ 40.71 10 D10M10 48.73 10 D10M233 61.06 11 D11M78 0.00 11 D11M20 15.15 11 D11M242 26.42 11 D11M356 38.52 11 D11M327 42.16 11 D11M333 64.34 12 D12M105 0.00 12 D12M46 6.18 12 D12M34 21.58 12 D12M5 29.08 12 D12M99 41.80 12 D12M150 54.46 13 D13M59 0.00 13 D13M88 0.29 13 D13M21 10.37 13 D13M39 13.05 13 D13M167 13.06 13 D13M99 18.91 13 D13M233 21.01 13 D13M106 24.88 13 D13M147 26.16 13 D13M226 28.39 13 D13M290 28.40 13 D13M151 35.99 14 D14M14 0.00 14 D14M115 23.91 14 D14M265 32.79 14 D14M266 45.55 15 D15M226 0.00 15 D15M100 13.46 15 D15M209 18.79 15 D15M144 19.36 15 D15M68 23.91 15 D15M239 25.13 15 D15M241 31.28 15 D15M34 42.97 16 D16M154 0.00 16 D16M4 16.77 16 D16M139 26.23 16 D16M86 41.80 17 D17M260 0.00 17 D17M66 11.73 17 D17M88 17.34 17 D17M129 38.85 18 D18M94 0.00 18 D18M58 0.69 18 D18M106 16.98 18 D18M186 20.90 19 D19M68 0.00 19 D19M117 16.36 19 D19M65 32.83 19 D19M10 44.49 X DXM186 0.00 X DXM64 42.35 qtl/tests/test_scanonevar.R0000644000175100001440000000256112514000574015555 0ustar hornikuserslibrary(qtl) data(map10) map10 <- map10[1:2] set.seed(8789993) simcross <- sim.cross(map10, n.ind=125, type="bc", model=rbind(c(1, 50, 1.5), c(2, 50, 0))) simcross$pheno[,1] <- simcross$pheno[,1] + rnorm(nind(simcross), 0, 2*simcross$qtlgeno[,2]) simcross <- calc.genoprob(simcross) out <- scanonevar(simcross, tol=0.01) summary(out, format="allpeaks") #### data(fake.bc) fake.bc <- fake.bc[1:2,1:150] # only chr 1 and 2, and first 100 individuals fake.bc <- calc.genoprob(fake.bc, step=5) out <- scanonevar(fake.bc, tol=0.01) summary(out, format="allpeaks") covar <- fake.bc$pheno[,c("sex", "age")] out <- scanonevar(fake.bc, mean_covar=covar, var_covar=covar, tol=0.01) summary(out, format="allpeaks") #########Simulate a vQTL on Chromosome 1######## chromo=1 qtl.position=14 # 50 cM N=nind(fake.bc) a1<-fake.bc$geno[[chromo]]$prob[,,1] y <- fake.bc$pheno$pheno1 y <- y + rnorm(N,0,exp(a1[,qtl.position])) out <- scanonevar(fake.bc, y, mean_covar=covar, var_covar=covar) summary(out, format="allpeaks") out <- scanonevar(fake.bc, y, mean_covar=covar, tol=0.01) summary(out, format="allpeaks") out <- scanonevar(fake.bc, y, var_covar=covar, tol=0.01) summary(out, format="allpeaks") out <- scanonevar(fake.bc, y, tol=0.01) summary(out, format="allpeaks") qtl/tests/test_io.R0000644000175100001440000000370212135326133014024 0ustar hornikusers###################################################################### # # TestIO/input.R # # copyright (c) 2002, Karl W Broman # last modified Feb, 2002 # first written Feb, 2002 # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License, # version 3, as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but without any warranty; without even the implied warranty of # merchantability or fitness for a particular purpose. See the GNU # General Public License, version 3, for more details. # # A copy of the GNU General Public License, version 3, is available # at http://www.r-project.org/Licenses/GPL-3 # # This file contains code for testing the cross IO in R/qtl. # # Needed input files: # # gen.txt, map.txt, phe.txt [Karl's format] # listeria.raw, listeria.map [mapmaker format] # listeria.raw, listeria2.map [mapmaker format; no marker pos] # listeria.csv [csv format] # listeria2.csv [csv format; no marker pos] # ###################################################################### library(qtl) ############################## # Reading ############################## # Read CSV format csv <- read.cross("csv", "", "listeria.csv") csv2 <- read.cross("csv", "", "listeria2.csv", estimate=FALSE) # Read mapmaker format mm <- read.cross("mm", "", "listeria.raw", "listeria.map") mm2 <- read.cross("mm", "", "listeria.raw", "listeria2.map", estimate=FALSE) ############################## # Writing ############################## # Write in CSV format write.cross(csv, "csv", filestem="junk1") csv3 <- read.cross("csv", "", "junk1.csv", genotypes=c("AA","AB","BB","not BB","not AA")) comparecrosses(csv, csv3) # Write in mapmaker format write.cross(csv, "mm", filestem="junk2") # Cleanup unlink("junk1.csv") unlink("junk2.raw") unlink("junk2.prep") qtl/tests/testaugmentation.R0000644000175100001440000000132012135326133015743 0ustar hornikusers# Test augmentation with MQM # # Note: the full version of this test has moved to ./contrib/bin/rtest, # as it takes a long time to run. The full version can be run with: # # cd contrib/bin # rm CMakeCache.txt ; cmake -DTEST_R=TRUE # make testR library(qtl) set.seed(1000) version = mqm_version() cat("R/qtl=",version$RQTL) cat("R-MQM=",version$RMQM) cat("MQM=",version$MQM) testaugmentation <- function(cross, ...){ crossML <- mqmaugment(cross, ...) res1 <- mqmscan(crossML,logtransform=TRUE) list(res1) } data(listeria) r <- testaugmentation(listeria) if(!round(r[[1]][3,3],3)==0.307) stop("Listeria ML dataaugmentation error") cat("testaugmentation.R, tests succesfully run!") qtl/tests/test_tidyIO.Rout.save0000644000175100001440000000233012424176622016306 0ustar hornikusers R version 3.1.1 (2014-07-10) -- "Sock it to Me" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(qtl) > data(hyper) > > # write to tidy format > write.cross(hyper, "tidy", "hyper_tidy") > > # read back in > x <- read.cross("tidy", "", genfile="hyper_tidy_gen.csv", + mapfile="hyper_tidy_map.csv", phefile="hyper_tidy_phe.csv", + genotypes=c("BB", "BA", "AA")) --Read the following data: 250 individuals 174 markers 2 phenotypes --Cross type: bc > > # compare results > comparecrosses(x, hyper) Crosses are identical. > > proc.time() user system elapsed 0.687 0.052 0.726 qtl/tests/test_io.Rout.save0000644000175100001440000000722612135326133015516 0ustar hornikusers R version 2.11.0 (2010-04-22) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ###################################################################### > # > # TestIO/input.R > # > # copyright (c) 2002, Karl W Broman > # last modified Feb, 2002 > # first written Feb, 2002 > # > # This program is free software; you can redistribute it and/or > # modify it under the terms of the GNU General Public License, > # version 3, as published by the Free Software Foundation. > # > # This program is distributed in the hope that it will be useful, > # but without any warranty; without even the implied warranty of > # merchantability or fitness for a particular purpose. See the GNU > # General Public License, version 3, for more details. > # > # A copy of the GNU General Public License, version 3, is available > # at http://www.r-project.org/Licenses/GPL-3 > # > # This file contains code for testing the cross IO in R/qtl. > # > # Needed input files: > # > # gen.txt, map.txt, phe.txt [Karl's format] > # listeria.raw, listeria.map [mapmaker format] > # listeria.raw, listeria2.map [mapmaker format; no marker pos] > # listeria.csv [csv format] > # listeria2.csv [csv format; no marker pos] > # > ###################################################################### > > library(qtl) > > ############################## > # Reading > ############################## > # Read CSV format > csv <- read.cross("csv", "", "listeria.csv") --Read the following data: 120 individuals 133 markers 1 phenotypes --Cross type: f2 Warning message: In fixXgeno.f2(cross, alleles) : --Assuming that all individuals are female. > csv2 <- read.cross("csv", "", "listeria2.csv", estimate=FALSE) --Read the following data: 120 individuals 133 markers 1 phenotypes --Cross type: f2 Warning message: In fixXgeno.f2(cross, alleles) : --Assuming that all individuals are female. > > # Read mapmaker format > mm <- read.cross("mm", "", "listeria.raw", "listeria.map") --Read the following data: Type of cross: f2 Number of individuals: 120 Number of markers: 133 Number of phenotypes: 1 --Cross type: f2 Warning message: In fixXgeno.f2(cross, alleles) : --Assuming that all individuals are female. > mm2 <- read.cross("mm", "", "listeria.raw", "listeria2.map", estimate=FALSE) --Read the following data: Type of cross: f2 Number of individuals: 120 Number of markers: 133 Number of phenotypes: 1 --Cross type: f2 Warning message: In fixXgeno.f2(cross, alleles) : --Assuming that all individuals are female. > > ############################## > # Writing > ############################## > # Write in CSV format > write.cross(csv, "csv", filestem="junk1") > csv3 <- read.cross("csv", "", "junk1.csv", genotypes=c("AA","AB","BB","not BB","not AA")) --Read the following data: 120 individuals 133 markers 3 phenotypes --Cross type: f2 > comparecrosses(csv, csv3) Crosses are identical. > > # Write in mapmaker format > write.cross(csv, "mm", filestem="junk2") > > # Cleanup > unlink("junk1.csv") > unlink("junk2.raw") > unlink("junk2.prep") > qtl/tests/phe.txt0000644000175100001440000000131611562004334013546 0ustar hornikusersT264 118.317 264 194.917 264 145.417 177.233 264 76.667 90.75 76.167 104.083 194.5 75.917 75.833 90.25 103.667 128.4 122.25 264 72.6 264 264 264 81.717 264 264 116.483 87.467 264 - 74.417 264 264 174.567 88.583 264 95 264 86.05 71.517 112.767 264 264 117.817 185.3 85.367 264 70.883 98.45 85.1 216.367 94.65 111.817 90.9 264 170.517 111.717 264 75.383 84.35 97.667 97.783 264 90.433 264 90.05 90.083 90.117 264 71.967 264 - 264 264 74.267 - - 264 264 264 109.867 264 264 96.017 136.417 168.25 120.7 114.55 94.033 67.683 93.833 93.867 139.867 117.933 77.8 117.833 264 77.733 93.183 77.633 77.55 264 117.433 93.067 99.867 82.333 163.75 82.017 264 264 91.283 140.767 81.733 75.667 76.483 116.467 116.517 139.55 264 116.2 qtl/tests/listeria2.map0000644000175100001440000000241211562004334014624 0ustar hornikusers1 D10M44 1 D1M3 1 D1M75 1 D1M215 1 D1M309 1 D1M218 1 D1M451 1 D1M504 1 D1M113 1 D1M355 1 D1M291 1 D1M209 1 D1M155 2 D2M365 2 D2M37 2 D2M396 2 D2M493 2 D2M226 2 D2M148 3 D3M265 3 D3M51 3 D3M106 3 D3M257 3 D3M147 3 D3M19 4 D4M2 4 D4M178 4 D4M187 4 D4M251 5 D5M148 5 D5M232 5 D5M257 5 D5M83 5 D5M307 5 D5M357 5 D5M205 5 D5M398 5 D5M91 5 D5M338 5 D5M188 5 D5M29 5 D5M168 6 D6M223 6 D6M188 6 D6M284 6 D6M39 6 D6M254 6 D6M194 6 D6M290 6 D6M25 6 D6M339 6 D6M59_ 6 D6M201 6 D6M15 6 D6M294 7 D7M246 7 D7M145 7 D7M62 7 D7M126 7 D7M105 7 D7M259 8 D8M94 8 D8M339 8 D8M178 8 D8M242 8 D8M213 8 D8M156 9 D9M247 9 D9M328 9 D9M106 9 D9M269 9 D9M346 9 D9M55 9 D9M18 10 D10M298 10 D10M294 10 D10M42_ 10 D10M10 10 D10M233 11 D11M78 11 D11M20 11 D11M242 11 D11M356 11 D11M327 11 D11M333 12 D12M105 12 D12M46 12 D12M34 12 D12M5 12 D12M99 12 D12M150 13 D13M59 13 D13M88 13 D13M21 13 D13M39 13 D13M167 13 D13M99 13 D13M233 13 D13M106 13 D13M147 13 D13M226 13 D13M290 13 D13M151 14 D14M14 14 D14M115 14 D14M265 14 D14M266 15 D15M226 15 D15M100 15 D15M209 15 D15M144 15 D15M68 15 D15M239 15 D15M241 15 D15M34 16 D16M154 16 D16M4 16 D16M139 16 D16M86 17 D17M260 17 D17M66 17 D17M88 17 D17M129 18 D18M94 18 D18M58 18 D18M106 18 D18M186 19 D19M68 19 D19M117 19 D19M65 19 D19M10 X DXM186 X DXM64 qtl/tests/test_qtl.R0000644000175100001440000000531212423554032014215 0ustar hornikusers###################################################################### # # test_qtl.R # # copyright (c) 2009, Karl W Broman, Pjotr Prins # first written July 2009 # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License, # version 3, as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but without any warranty; without even the implied warranty of # merchantability or fitness for a particular purpose. See the GNU # General Public License, version 3, for more details. # # A copy of the GNU General Public License, version 3, is available # at http://www.r-project.org/Licenses/GPL-3 # # Some basic regression/integration testing for some of the QTL mapping routines # # You can run it with: # # R --no-save --no-restore --no-readline --slave < ./tests/test_qtl.R ###################################################################### library(qtl) version = mqm_version() cat("R/qtl=",version$RQTL) cat("R-MQM=",version$RMQM) cat("MQM=",version$MQM) data(listeria) if (nind(listeria)!=120) stop("Number of individuals incorrect") # ---- a quick test of standard R/qtl scanone mr = scanone(listeria, method='mr') test = round(mr[15,]$lod*1000) cat(mr[15,]$lod,test) if (test != 966) stop("scanone_mr gives an incorrect result") # ---- a quick test of MQM for R/qtl augmentedcross <- mqmaugment(listeria, minprob=1.0, verbose=TRUE) nind = nind(augmentedcross) if (nind!=120) stop("Number of individuals incorrect: ",nind) result <- mqmscan(augmentedcross, logtransform=TRUE, outputmarkers = FALSE,off.end=0) test1 = round(result[5,5]*1000) test2 = round(max(result[,5]*1000)) cat("test1 = ",test1,"\n") cat("test2 = ",test2,"\n") if (test1 != 76) stop("MQM gives an unexpected result (1)") if (test2 != 5384) stop("MQM gives an unexpected result (2)") # ---- Test for negative markerlocations data(hyper) hyper <- fill.geno(hyper) #Mess up the markers by shifting temp <- shiftmap(hyper, offset=10^7) out.temp <- mqmscan(temp,verb=TRUE,off.end=10) if(!(rownames(out.temp)[3]=="D1Mit296")) stop("MQM something wrong with positive shifts in location") #Mess up the dataset by moving 1 marker infront of the chromosome hyper$geno[[1]]$map[1] <- -10 res <- mqmscan(hyper,verbose=T,off.end=100) if(any(is.na(res[,3]))) stop("MQM failed to handle negative locations correctly") if(!(rownames(res)[2]=="c1.loc-95")) stop("MQM something wrong with negative locations") #to -15 because off.end defaults to 10 cat("Version information:\n") cat("R/qtl = ",version$RQTL,"\n") cat("R-MQM = ",version$RMQM,"\n") cat("MQM = ",version$MQM,"\n\n") cat("test_qtl.R tests succesfully run!") qtl/tests/test_mapqtl_io.R0000644000175100001440000000054712422233634015410 0ustar hornikusers# test input/output in mapqtl format library(qtl) data(fake.4way) write.cross(fake.4way, "mapqtl", "fake_4way_mapqtl") x <- read.cross("mapqtl", "", genfile="fake_4way_mapqtl.loc", phefile="fake_4way_mapqtl.qua", mapfile="fake_4way_mapqtl_female.map") x <- replace.map(x, pull.map(fake.4way)) comparecrosses(x, fake.4way) qtl/tests/map.txt0000644000175100001440000000345411562004334013554 0ustar hornikusers20 13 0.0100 0.2219 0.1508 0.0947 0.0280 0.1665 0.0069 0.0969 0.0077 0.0353 0.0769 0.0096 D10M44 D1M3 D1M75 D1M215 D1M309 D1M218 D1M451 D1M504 D1M113 D1M355 D1M291 D1M209 D1M155 6 0.2536 0.1828 0.1913 0.1000 0.1314 D2M365 D2M37 D2M396 D2M493 D2M226 D2M148 6 0.2857 0.1126 0.1333 0.0557 0.0759 D3M265 D3M51 D3M106 D3M257 D3M147 D3M19 4 0.1827 0.1562 0.2877 D4M2 D4M178 D4M187 D4M251f 13 0.0574 0.1154 0.0033 0.0400 0.0175 0.0513 0.0000 0.0197 0.0490 0.0562 0.0650 0.0979 D5M148 D5M232 D5M257 D5M83 D5M307 D5M357 D5M205 D5M398 D5M91 D5M338 D5M188 D5M29 D5M168 13 0.0755 0.0537 0.0672 0.0963 0.0324 0.0233 0.0358 0.0040 0.0352 0.0357 0.0036 0.0137 D6M233 D6M188 D6M284 D6M39 D6M254 D6M194 D6M290 D6M25 D6M339 D6M59_ D6M201 D6M15 D6M294 6 0.1795 0.1559 0.0609 0.1821 0.1175 D7M246 D7M145 D7M62 D7M126 D7M105 D7M259 6 0.0134 0.0995 0.1522 0.0582 0.1715 D8M94 D8M339 D8M178 D8M242 D8M213 D8M156 7 0.0421 0.1035 0.1235 0.0561 0.1213 0.0712 D9M247 D9M328 D9M106 D9M269 D9M346 D9M55 D9M18 5 0.1952 0.1366 0.0741 0.1093 D10M298 D10M294 D10M42_ D10M10 D10M233 6 0.1471 0.1108 0.1187 0.0363 0.2083 D11M78 D11M20 D11M242 D11M356 D11M327 D11M333 6 0.0615 0.1493 0.0745 0.1244 0.1240 D12M105 D12M46 D12M34 D12M5 D12M99 D12M150 12 0.0029 0.0912 0.0262 0.0000 0.0552 0.0206 0.0372 0.0126 0.0218 0.0000 0.0705 D13M59 D13M88 D13M21 D13M39 D13M167 D13M99 D13M233 D13M106 D13M147 D13M226 D13M290 D13M151 4 0.2224 0.0879 0.1249 D14M14 D14M115 D14M265 D14M266 8 0.1315 0.0531 0.0057 0.0454 0.0121 0.0612 0.1149 D15M226 D15M100 D15M209 D15M144 D15M68 D15M239 D15M241 D15M34 4 0.1617 0.0935 0.1508 D16M154 D16M4 D16M139 D16M86 4 0.1152 0.0558 0.2028 D17M260 D17M66 D17M88 D17M129 4 0.0068 0.1391 0.0377 D18M94 D18M58 D18M106 D18M186 4 0.1580 0.1589 0.1146 D19M68 D19M117 D19M65 D19M10 2 0.3447 DXM186 DXM64 qtl/tests/test_mapqtl_io.Rout.save0000644000175100001440000000233412424414457017077 0ustar hornikusers R version 3.1.1 (2014-07-10) -- "Sock it to Me" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # test input/output in mapqtl format > > library(qtl) > data(fake.4way) > > write.cross(fake.4way, "mapqtl", "fake_4way_mapqtl") > > x <- read.cross("mapqtl", "", genfile="fake_4way_mapqtl.loc", + phefile="fake_4way_mapqtl.qua", + mapfile="fake_4way_mapqtl_female.map") --Read the following data: Number of individuals: 250 Number of markers: 157 Number of phenotypes: 2 --Cross type: 4way > > x <- replace.map(x, pull.map(fake.4way)) > > comparecrosses(x, fake.4way) Crosses are identical. > > proc.time() user system elapsed 4.311 0.247 4.547 qtl/tests/testthat.R0000644000175100001440000000004412424176622014221 0ustar hornikuserslibrary(testthat) test_check("qtl") qtl/tests/listeria2.csv0000644000175100001440000010216511562004334014650 0ustar hornikusersT264,D10M44,D1M3,D1M75,D1M215,D1M309,D1M218,D1M451,D1M504,D1M113,D1M355,D1M291,D1M209,D1M155,D2M365,D2M37,D2M396,D2M493,D2M226,D2M148,D3M265,D3M51,D3M106,D3M257,D3M147,D3M19,D4M2,D4M178,D4M187,D4M251,D5M148,D5M232,D5M257,D5M83,D5M307,D5M357,D5M205,D5M398,D5M91,D5M338,D5M188,D5M29,D5M168,D6M223,D6M188,D6M284,D6M39,D6M254,D6M194,D6M290,D6M25,D6M339,D6M59_,D6M201,D6M15,D6M294,D7M246,D7M145,D7M62,D7M126,D7M105,D7M259,D8M94,D8M339,D8M178,D8M242,D8M213,D8M156,D9M247,D9M328,D9M106,D9M269,D9M346,D9M55,D9M18,D10M298,D10M294,D10M42_,D10M10,D10M233,D11M78,D11M20,D11M242,D11M356,D11M327,D11M333,D12M105,D12M46,D12M34,D12M5,D12M99,D12M150,D13M59,D13M88,D13M21,D13M39,D13M167,D13M99,D13M233,D13M106,D13M147,D13M226,D13M290,D13M151,D14M14,D14M115,D14M265,D14M266,D15M226,D15M100,D15M209,D15M144,D15M68,D15M239,D15M241,D15M34,D16M154,D16M4,D16M139,D16M86,D17M260,D17M66,D17M88,D17M129,D18M94,D18M58,D18M106,D18M186,D19M68,D19M117,D19M65,D19M10,DXM186,DXM64 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116.2,A,A,-,H,H,H,H,H,H,H,H,A,A,A,H,H,H,A,A,H,-,-,H,B,-,-,-,-,H,H,H,H,H,H,H,H,-,B,B,-,-,B,H,-,H,H,A,-,-,A,A,-,A,A,A,-,A,H,H,H,-,H,H,A,-,H,H,A,A,A,-,A,-,-,H,-,H,-,H,-,H,B,-,B,B,-,-,H,H,-,B,C,H,H,-,-,H,H,H,H,H,H,H,-,B,B,-,H,H,H,-,B,B,B,-,-,H,-,-,-,-,H,B,-,H,H,H,-,-,-,-,A,A qtl/tests/testthat/0000755000175100001440000000000012567121772014104 5ustar hornikusersqtl/tests/testthat/test-scantwoperm.R0000644000175100001440000000436312424176622017550 0ustar hornikuserscontext("scantwo perms") test_that("scantwo and scantwopermhk give same results", { data(hyper) hyper <- calc.genoprob(hyper[c(18,19,"X"),]) set.seed(92999298) out1 <- scantwo(hyper, method="hk", n.perm=3, verbose=FALSE) set.seed(92999298) out2 <- scantwopermhk(hyper, n.perm=3, verbose=FALSE) expect_equivalent(out1, out2) # X-chr-specific permutations set.seed(92999298) out1 <- scantwo(hyper, method="hk", n.perm=3, perm.Xsp=TRUE, verbose=FALSE) set.seed(92999298) out2 <- scantwopermhk(hyper, n.perm=3, perm.Xsp=TRUE, verbose=FALSE) expect_equivalent(out1, out2) }) test_that("summary.scantwo works with X-chr-specific perms", { data(hyper) set.seed(23615071) hyper <- calc.genoprob(fill.geno(hyper[c(18,19,"X"),])) # selected chr; imputed genotypes out2 <- scantwo(hyper, method="hk", verbose=FALSE) set.seed(17370120) operm1 <- scantwopermhk(hyper, n.perm=100, verbose=FALSE) set.seed(17370120) operm2 <- scantwopermhk(hyper, n.perm=100, perm.Xsp=TRUE, verbose=FALSE) # no significant pairs sum1 <- summary(out2, perms=operm1, alpha=0.05) sum2 <- summary(out2, perms=operm2, alpha=0.05) expect_equal(nrow(sum1), 0) expect_equal(nrow(sum2), 0) # p-values match expectation; not X-chr-specific sum1 <- summary(out2, perms=operm1, pvalues=TRUE) lodcol <- grep("^lod", names(sum1)) expect_equal(lodcol, c(5, 7, 9, 13, 15)) for(i in 1:5) expect_equal(sum1[,lodcol[i]+1], sapply(sum1[,lodcol[i]], function(a) mean(operm1[[i]] >= a))) # p-values match expectation; X-chr-specific sum2 <- summary(out2, perms=operm2, pvalues=TRUE) pairtype <- paste0(ifelse(sum2$chr1=="X", "X", "A"), ifelse(sum2$chr2=="X", "X", "A")) pairtype <- match(pairtype, c("AA", "AX", "XX")) L <- attr(operm2, "L") pow <- sum(L)/L lodcol <- grep("^lod", names(sum1)) expect_equal(lodcol, c(5, 7, 9, 13, 15)) for(j in 1:nrow(sum2)) { for(i in 1:5) { lod <- sum2[j,lodcol[i]] p <- sum2[j,lodcol[i]+1] nominal_p <- mean(operm2[[pairtype[j]]][[i]] >= lod) adj_p <- 1 - (1-nominal_p)^pow[pairtype[j]] expect_equivalent(p, adj_p) } } }) qtl/tests/testthat/test-fliporder.R0000644000175100001440000000244712476130441017171 0ustar hornikuserscontext("flip.order") test_that("flip.order, when applied twice, should get us back to the same thing", { data(hyper) # reduce size set.seed(53307443) hyper <- hyper[,sample(nind(hyper), 8)] hyper <- calc.genoprob(hyper, step=1) hyper <- sim.geno(hyper, step=10, n.draws=2) hyper <- argmax.geno(hyper, step=1) hyper <- calc.errorlod(hyper) hyperfl <- flip.order(hyper, chr=c(1, 4, 6, 15)) summary(hyperfl) hyperfl2 <- flip.order(hyperfl, chr=c(1, 4, 6, 15)) summary(hyperfl2) # having flipped twice, should be back to where we were # (except starting locations for each chromosome map) expect_null(comparecrosses(shiftmap(hyper), shiftmap(hyperfl2))) }) test_that("flip.order for 4-way cross", { data(fake.4way) # reduce size set.seed(36461124) fake.4way <- fake.4way[,sample(nind(fake.4way), 8)] fake.4way <- calc.genoprob(fake.4way, step=1) fake.4way.fl <- flip.order(fake.4way, chr=c(1, 4, 6, 15)) summary(fake.4way.fl) fake.4way.fl2 <- flip.order(fake.4way.fl, chr=c(1, 4, 6, 15)) summary(fake.4way.fl2) # having flipped twice, should be back to where we were # (except starting locations for each chromosome map) expect_null(comparecrosses(shiftmap(fake.4way), shiftmap(fake.4way.fl2))) }) qtl/tests/testthat/test-stepwiseqtl.R0000644000175100001440000000235012424176622017564 0ustar hornikuserscontext("stepwiseqtl") test_that("stepwiseqtl works with X-chr-specific perms", { data(fake.f2) fake.f2 <- calc.genoprob(fake.f2) set.seed(17370120) operm1 <- scantwopermhk(fake.f2, n.perm=10, verbose=FALSE) set.seed(17370120) operm2 <- scantwopermhk(fake.f2, n.perm=10, perm.Xsp=TRUE, verbose=FALSE) pen1 <- calc.penalties(operm1) pen2 <- calc.penalties(operm2) out.sq1 <- stepwiseqtl(fake.f2, max.qtl=4, penalties=pen1, method="hk", verbose=FALSE) expect_equal(out.sq1$chr, c("1", "8", "13", "X")) expect_equal(out.sq1$pos, c(37.11, 61.20, 24.03, 14.20)) out.sq2 <- stepwiseqtl(fake.f2, max.qtl=4, penalties=pen2, method="hk", verbose=FALSE) expect_equal(out.sq2$chr, c("1", "13", "X")) expect_equal(out.sq2$pos, c(37.11, 24.03, 14.20)) out.sq3 <- stepwiseqtl(fake.f2, chr=1:19, max.qtl=4, penalties=pen2, method="hk", verbose=FALSE) expect_equal(out.sq3$chr, c("1", "13")) expect_equal(out.sq3$pos, c(37.11, 24.03)) pen2b <- calc.penalties(operm2, alpha=0.2) out.sq2b <- stepwiseqtl(fake.f2, max.qtl=6, penalties=pen2b, method="hk", verbose=FALSE) expect_equal(out.sq2b$chr, c("1", "8", "13", "X")) 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bahhhbbbbbhbhbhhhbhhbahahhbhahbahhahbhbhhhaaabhhahbahaabahhh *D5M148 aha--ahbhhhbhbahhhabhaa-bbhba-hahhbhbaabbhahbb--bhbhb-hhaaaa hahb-hhba-abhhh-a-hbbbahbbhhbahhhabahbbhhhhbabahbahhbhbhbhbh *D5M232 ahahaaabhhhbhbabhhahhaab-bhbahhahhbhbhabbhahbbaabhbhbbhhhaaa hahbhhbbhhhbbhhhaahhbbahbhhhbhhhhabhhb--h-hbaba-bahhb--hbhbh *D5M257 hhaaaaabhhhbhbhbhhahahabbhhbahaahhhhbhbbbhhhhbahbhbhbbahhaaa hhhbhhbbaaahbhhaaahabbahbabhbhhhhabhhbbhhahbababaahhbhhabhhh *D5M83 hhaaaaabhhhbhbhbhhahahabbhhbahaahhhhbhbbbhhhhbahbhbhbbahhaaa hhhbhhbbaaahbhhaaahabbahbabhbhhhhabhhbbhhahbhbabaahhbhhabhhh *D5M307 hhhaaaabahhbhbhbbhahahabhhhbahaahhhhbhbbbhhhhbahbhb-hbahhaha ahhbh-bbaaahbhhaaahabbahbabhbhhhhabhhbbhhaahhbabaahhb-hab-hh *D5M357 hhhaaaabahhbhbhbbhahahabhhhbahaahhhhbhbbbhhahbahbhbhhbahhahh ahhbhhbbaaahbhhahahabhahbabhbhhhhabhhbbhhaahhbabaahhbbhabhhh *D5M205 hhha-habahhbhbhbbhahahabhhhbahaahbhhbhbbbhhahbah--bhhbahhbbh ahhbhhbbahahbhaahahabhahbabhbbhhhbbhhbhhhaahhbabaahh-bhabhhh *D5M398 ------------------------hhhbahaahhhhbhbbbhhahbah--bhhbahhbbh ahhbhhbbahah-haahahabhahbabhbb------------------------------ *D5M91 hhhahhabahhbhbhbbhahahabhhhbahaahbhhbhbbbhhahbahbhbhhbahhbbb ahhbhhbhahahbhaahahabhahbabhbbhhhbhhhbhhhaahhbabaahhhbhabhhb *D5M338 hhhahhabahhbhbhbbhaaahhhahhbahaahbhhbhbbbhhahbahbbbhhbahhbbb ahhbhhhhabahbhaahahahhahbabhbbhhhbhhabhhhhahhbabahhhhbhahhhb *D5M188 hbhahhahahhbhbhbbhaaahhhahhbhaahhbhabhbbbhhaabahbbbahbahbbbb ahhbhhhhabhhbhhabahahhahbabhbbhhhbhhabhhhhahhbabahhhabhahbh- *D5M29 bbhah--h-hbb--hbbh-a--hhahhb---hhbha-hbbba-a-b-abb-aa-ahbbbb h-hbhh------bhhhbahahha-b-b-b------------------------------- *D5M168 bbaahhhhhhbbhhhhbhaaabahaahbhhahbbhabhbbbabaabaabhhaahahbbbh hbhbhhhhahhhbahhbaaahhbhbabbbbhhbbahaabhahhhhbabhhhbabhhhbhb *D6M223 aahhaahahhhhbhhhhahbbhhabbaahabhbhhbahahhbhbahhahhhhahbhhbhh haahabbhabbhhbhhhhhh-hbhahahhbhhhaahhhbabbaaahahbbbabaaabhbh *D6M188 aahhaahahhhhhbhhhahbhhhabbaahhhhbhhhabhhhbhbahhahhhhahbhhbhh haaaahhaabbhhbhhhhhhhhbbahahhh------------------------------ *D6M284 hahhaahahhhhhbhhbaabhhhahhaahhhhhhhhabhhhbhbahhahhhhahbhhhhh hhaaahhaahbhhbhhhhhhhhhbahahhhhhhahbhhbabhahhhahbhbahahabhbh *D6M39 hahhahhahhhhhbahbahbhhhahhaabhhhhhhhhbhhhbhbhhbahhhhahbbbhhh hhahahhahhbhhbhhhbhbhhhbahahhhhhhahbhhbahaahhhahbhbahahahabh *D6M254 habhabhhbhhhhbahbhhhhbhahhhhbhhhahhhhbhhhbhhhhbabhhhhhbbbhhh hhahhhhabhbhhbhbhbhbhhhbahahhahhhhabhhbahaahhhahhhbhhhhahhba *D6M194 habhabhhbhhhhbabbhhhhbhahhhhbhhhahhhhbhahbhhhhbahhhhhhbbbhbh hhahhhhabhbhhbhbhbhbhbabahahha------------------------------ *D6M290 habh-bh-bhahhba-bhhhhbhah-------a-hhhbhahb-ah-ba-h---h-bbhbh hh-hhhhabhbh---bhb-bhbabahahha------------------------------ *D6M25 habhhbbbhhahhbabbhhhhbhahhhhbhhbahhhhbhahh-ahhbahhbhhhbbbhbh hhahhhhabhbhhbabhbhbhbabahahhahhhhahhhbah-hhhaabhhbhh--hhhba *D6M339 habhhbbbhaahhbabbhhhhbhahhhhbhhbahhhhbhahhhahhbahhbhhhbbbhbh hhahhhhabhbhhbabhbhbhbabahahhahhhhahhhbahahhhaabhhbhhhbhhhba *D6M59_ bhbhhbbbhaahhbabhhhhhbhahhhhbhhbaahhhbhahhhahhbahhbhhhbbbhhh hhahhhhabhbhbbabhbhbhbabahahha------------------------------ *D6M201 bhbbhbbbhaahhbabhhbhhbhah-hhbhhbaahhhbhahhhahhbahhbhbhbbbhhh hhahhhahbhbhbbabhbhbhbahahahhahhahahhhhhhahhhaabahbhhhbhhhha *D6M15 bhbbhbbbhaahhbabhhbhhbhahhhhbh-bahhhhbhahhhahhbahhbhbhbbbhhh hhahhhahbhbhbbabhbhbhbahahahhahhahahhhhhhahhhaabahbhhhbhhhha *D6M294 bhbbhbbbhaahhbabhhbhhbhahhhhbhhbahhhhbhahhhahhbahhbhbhbbbhhh hhahhhahbhbhbbabhbhbhbahhhhhhhhhahahhhhhhahhhaabahbhbhbhhhha *D7M246 bhabbaahbhhhababahhhhhabhhhbbhbabhhhhbbhhhahbhbaahbaahhhhabb hhbbahhhhaahhbbhhhhahabhbaabab------------------------------ *D7M145 bbabhaahbaahhbhbhhhhhhhhhahhbababhhhhbbbbaaabhha--bahahhhabb hhbhhbhaha-hhbhhhhhhhhbabaabh-babhbahhhahbhbabhhhhhahhbhahaa *D7M62 hbabaaahbahhhhhbhhhbahbhhbhhbababahhbbbbbaaabhhaabbahhhhhahb hhbhhbhahhbhhhhhbhhhahhahahbhhbahhh-hhbahaahahhahh-ah-bhahbh *D7M126 hbabaahhbah-h-hbhbhbaab-hbhhbababahhbbbabaaabhh--bbahhahha-- 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baaahhbahhbahahahhaahhababbhha------------------------------ *D10M298 hahaahhahaabhbhhabhhhahhabhaaahbaabhhhahhhhahahhabbhbhhahhaa habhaaaahhhahahhhbba-haaahhaaahhhhabbhhbhhbhhabbahhhahbbahhh *D10M294 haahhhhabhhbahahabbbhahaabhahhhhhahbhhhaahhaaahhabbhbhhhhhha habhahaahbhahahbabhahhhhahaaaa------------------------------ *D10M42_ baabhhh-bhhbhhahaabbhahhabhhhhah-ahbhhhaahahahhh-hhhbhb-bhha babhhhhhhbhabaababhhhhhhahahaaabhhhhahbhhabaahhabhhhbhahhhbh *D10M10 baahhhhabhhbhhabaabbha-hhhhhhhahhhhbhbhaahahahhhaahabhbhbhha bhhhhhhhhba-haahabhhhhhhahabaaabhhhhhbahh-b-abhab-hh---h-h-- *D10M233 baahhhhabhabhhbbhabhhaabhhhhbaabhhhbhbbaahahhbhhaaahhhbbhhha bhhhbhhbhbhahaahabbhbhhhahabaaabhhhhhhabhabaahhhbhhhbhhhhhbh *D11M78 hahbbhahbaababhhahbhahhbhhhhhhaahhhhbaahhhbhhhhbaaahhahhhaab bbhhaahbbabhhhhhhbhbabhahbhahh------------------------------ *D11M20 hahbbbabbhhbahahahbbahhbhhabbha-hhhhbaahbhbhhaabaa-hbh-hhhhb bbhbaahbbhhhhhhhhbhbabahhahahhhbhbbhhaahbhhhhhhhbhhbhhahhhhh *D11M242 hahbbbahhhhbahaba-hbhbhbhhabbhhhbhhhhhahb-bbhaabaaahbhahhbhh bbhba-hbbhabhhhhhbhhabahbahhaa--hbbh-aah-bhhh-hhbhhb-hhhhhhb *D11M356 aahbbbhbhhhbahhhahhbhbhbbhabbhhhbhhhhhhhbhbbhaahhbaabbahhhhh bbhbhahhbhabbhhbhbhha-abbaahaa------------------------------ *D11M327 aahbbbhbhhhbahahahhbhbhbbhabhhhabhahhhhhbhbbhaahhbaabbaahhhh bbhbhahhbhabbhhbhhbhababbaahaaahhbbahaahhbahhhhabhhbhhbbbbhb *D11M333 hahhhbbhhbhhhaabbhhbhbhbhhabhhhabbahhhhhbhbbahhhhbhhbbaaaaha aahbhahhbhabbbbhhahhabhhbahhahhhabbah-hha-hbaahabhh-ahbbbhhb *D12M105 babaabbaaaahahhhaahbabahhahahbabhhbhhahaahbhabhhaabahaahaaba hbhbhbbhhabhbhbbhhahahhbhbbhah------------------------------ *D12M46 babaabhha-aaahhhaahbabahhahaahabhhbhhahaaab-abhhaa-aaaahaaha hbababbhhabhbhbbhhhhahhbhbbhah------------------------------ *D12M34 bhhhahahhaaaahhhaahbahahhahhahabbhhhhahhaahbhhhhaahaaaa-aaha ahahhbbhhh-hbabbhhhhaahhhhbhabahbhhhbhhhhbaaahhabhbhbahhhhbh *D12M5 bbhaahahhaha-hhhahhbahahhhhbahabbhahhahhaahbahbhaahaaaahhaha ahaahbbhbhbababbbbhhaaahhhbhabahbhh-bhbhh-aaahhab--hbahhh-bh *D12M99 bbhaahahhahaahhhahhhbhhhbhhbhhaabhhhhabhaahhahhhaahahaabhaha ahaahbbhbhbabahbbbbhaaahhhbahh------------------------------ *D12M150 bhbaahahhabhhbhhaha-bhhhbhhbhhaabhh-hhbbaa-hah-haabahaabha-h ahhabbbbbhbabahhbbbhaaaahbbabhahhhhhbahahaahhhbhbbbhhhbahahb *D13M59 caacccccaccacccaccccccccbbhhhaabahahahbabbhhhhhhcccacccccaaa cacccacacacccaahbhccacaacccaacccacaaacaccccccccaccccccaacccc *D13M88 -aahhhhhahh-hbhabbbhhh--bbhhhaabahahahbabbhhhhhhabbabhhhbaaa hababahahahbbhahbhhhahaahb-a-hhhahaaahahhhhhhhhahhhhhhaahhhh *D13M21 hhahh-bhahhaabaabbbhh-hhbbhhhaa-a-ah-hhab-b---hahbb-bah-b-a- b-b-b-aahaabbhahbhbaabaah-haahhhahaahhahhhhhaahahhhhhhaahahh *D13M39 hhahhhbaahhaabaabbbhhhhhbbbhhaahahahahhab-bhhahahb-abahhbaaa hhbbbaaahaabbhahbhbaahaahahaah------------------------------ *D13M167 hhahhhbaahhaabaabbbhhhhhbbbhhaahahahahhabhbhhahahbbabahhbaaa hhbbbaaahaabbhahbhbaahaahbhaa------------------------------- *D13M99 ahahhhbaahhaabahbbbahhhabhbhhaahahabahhabhbhhahabbbabahhbaaa hhbbbaaahaabbhahbhhahhaahahaahhhahaahaahabhhaaaahbahhhaahahh *D13M233 a-ahhhb-ahhaabahbbbahhhahhbh-aa-ahabahhabhbhhahabbbab--ab--a h-bbbaaahaahbhahbhh--ha-h-haahhhahaahaahabhbaaahhbahhhaa-ahh *D13M106 ahhhhhbaahhhabahbbhahhhahhbhhaahahabahhabhbhhhhabbhabahabaaa hhbbbaaahaahbhahbhhahhaahahaaaahahaahaahabhbaaahhbahhhahhhhh *D13M147 ahhhhhbaahhhabahbbhahhhahhbhhaahahabahhabhbhhhbabbhabahabhaa hhhbbaaahaahbhahbhhahhaahahaaaahahaahaahabhbaaahhbahhaahhhhh *D13M226 ahhhhhb-ahh-hbhhb-hahhhahhbhhaaaah---h--b-bhh-habbhabahab--- hh-bb-aa--ahbhahbhhahha-h--aaa-hahaah--habh---ahhbahh-hh-hhh *D13M290 ahhhhhbaahhhhbhhbbhahhhahhbhhaahahabahaabhbhhhhabbhabahabhaa hhhbbhaahaahbhahbhhahhaahahaaaahahaa-aahabhbaaahhbahhahhhhhh *D13M151 ahhhhhbhahhhhbhhbbhahahahhbhhabaahhbahaahhbbhhhabbhabaaabhhh hhhbbhaabaahbhahbhhahhaahahaaa-aabahhhaaabhbaaahhbahhhbhhhhh *D14M14 babhhaahbabbbhabahabbhahhbbbhhhhhhhhahahhahhh-bhahbbhhbhhbba hahahhhhhabb-hhbbbahhhhhaahhab------------------------------ *D14M115 bbbahahbbhhbbhaaahhhhaahhhhhhhhbhhhhahaa-a-a----hhbbhhbahhbb hahahhh---bbhbbbbbhhahahhahhabahbhbhahbahhhhbhbaahahhhhaahhb *D14M265 hbbahahhbbhbbhhaahhaaaahhhhhhhhbhbhhahaahabhbhaahabhhhbahhhb hahaahbbhhhbhbbhbbhhahahhahaabahbhbhah--hh-hbhhaa-ahhhhhahhb *D14M266 hbhhhahhbhhbbhhhhhbaahahhhhhhhhhhbhhahhahbbhbhhahabhhhhahhhb haahhhbbhhhhabbhbhbaahaahahaab------------------------------ *D15M226 abbhahabhhahah--hhhbbhhbhabhbhhahahbhh--hhhaaabhhhahahhahhhb hb--hhhhhbbaahhbahahhhbbba--hbabaahhhhhbhhhhabhabhabbbbahahh *D15M100 hhbbahabahahab-aahabbhabhhbbhhhahahhhhbbh-------hhabhhhaahhb abhb------bhhhhbahabhhh-ba---babaahhhhhbhahbabhhbhabbhbaaahh *D15M209 hhbhababahahabaaahabbhabhhbbhhhhhahhahbbhahaaahhhhhbhahaahbb abhbahbhhbb-hhhbhhabhhhbbahbhbhbaahhhhhbaahbabhhbhhbbhbaaahh *D15M144 hhbhababahahabaaahabbhabhhbbhhhhhahhahbbhahaaahhhhhbhahaahbb abhbahbhhbbhahhbhhabhhhbbahbhb------------------------------ *D15M68 hhbhabahahahabaaahabhhabhhbbhhhhhahhahbb-ahhaa-bhhhbhahaahbb abhbabbahbbhahhbhhabhahbbahbhbhbaaahhhhbaahbhb-hbhhbhhbaaahb *D15M239 hhbhabahahahhbaaahabhhabhhbbhhhhhahhahbbhahhaahbhhhbhahaahbb abhbabhahbbhahhbhhabhahbbahbhbhbaaahhhhbaahbhbhhbhhhhhbaaahb *D15M241 hhbaabahahahhbaaahahhhabbhbbhbhbhahhahbbhahhhah-aahbhahaahbb abhhabhaa-b-ahhbhhabhah-hah-hhhbhaahhhhbaaabhbhhbhhhhhbaaahb *D15M34 hhbaab-hahhhhb-a-h-hhhabbhhbbbhhhahaahhb-abbh-hbaab-babaahb- ahb-abbaa-b-ahhbhh-bbaahhahbah------------------------------ *D16M154 ahbaahbbhbbbbhhbbahhhaaahbahhaahhahhabhaahbbaabhhaahbabhhbbh hhbaaabhbhhahhbbaahhbhbahhbbaa------------------------------ *D16M4 ahbhbhhbbbbbhahbhabhhaaahbabhahhhaahhbhaahbbaabhbaahbabh-hhh hhbhaahbbhhhbabhahahhhbhbhbbaaahbaahhbaaahahbhbhhhbbhaahhhhh *D16M139 ahbhbhhbbbbbhahbhabaahaabbabhabhhaahbbhaahbbaabhbahhhahhhhhh hhbhahhbbhhh-ahaahahhabhhhabaa------------------------------ *D16M86 aabbbhhbbhhhhahhaabaahhabbabhhbhhaahbbhahhhbahbhbahahahahbhh habbhhhbbhhhhhhahhhabahhhhahaa------------------------------ *D17M260 hhaaahhhaahhhbhbaabhbhhhhhabhhhahhbahhbhabhhahhaabhhhaababhh hbhhhhabbhhhhhhhbbhabbbahbaabb------------------------------ *D17M66 hhahahahaabhhbhbahbhbhhhhhabhhhahabahhbhabhhhahaabhhhaahabbb hbhhhhhbhhhbhhhhhbbabhbbhbabhb------------------------------ *D17M88 hhahahahhabhhbhhah-hbhhhhbabhhhhhhbahhbhabhh-a-aabhbhhahabbb hhhhhahbhhhbhhhhhbbabhhbhbabhb---b---a-ah-h-hhabh-hahaa--bbh *D17M129 hhabhbhhhabhhbhhahhbh-hbbhahabbhahbahhbhabh-hahhahhbhhahahbb hhahbahbahhbbahahhbhhahbbbabhhbbhbaabhbahhabhhaa-hahhhahhhbb *D18M94 bbhaahhabaahhabbahhahhaahhaaahaabhbhhbhaabbaabbbhhhhahhbahbb hhhhbhbahahahhahhhbbaaahhhahah------------------------------ *D18M58 bbhaahhabaahhab--hhah-aahhhaahaabhbhhb-aabba-bbbhhhhahhbahbb hhhhbhbahahahhahhhbbaaahhhabahhbahh-aaahhhbbhhbhbhb-ahhbhhhh *D18M106 bhbaahb--aahh-b---------------------------------ahhbhhbbahah ahhh--bahahhh-ahhaabaaahhhabhhahabhbhaahh-hbhbbhhhbhhahbahhh *D18M186 bhbaahbhbaahhhbbabhahhaabhhaahaahhbhabhahbbhabbhahhbbhhbahah ahhhbhbahahhhaahbaabahahhhabhbahabhbhaahbhhbhbbhhhbhaahbabhh *D19M68 hahbhabhhhbaahhbhhbbhaahhbhbhhhhhbhhhhahbbbaahhhhbahahhhbaha babbhbhaahbhbhaahhabhhabaahhah------------------------------ *D19M117 hhhhaabhhbhaabhb--bbhaahbbhhhhhhhhahhhaa--baaahhabahahhhhahh haabhhaaahbhbbahhhabhhhbabhhaa------------------------------ *D19M65 hhahaaahhbhha-hbahbbhahhhhbhhhhhhhaahhbahbbahah-ahhaahahhahh haabahhaabbhhbabahabhhbbabbhah------------------------------ *D19M10 ccacccacacccacacaa-ccacccccccccccccaaccacccacaccaccaacaccacc caacaccaccccccccacaccaccaccccc------------------------------ *DXM186 hhhhaahaahahhaaaaaaahhaha-ahahhhhhaaaahaaaahhaahaaahhaahhaaa aahhhaahhhhhahhaahhhhhaaaaahaaaaaaaahhhaaaaahhaaaahhaaaaahha *DXM64 hhhaahh-hahhhhaahahhaaahahaahhhhhhhahahhhahhha--haahahahhhaa a-hhhaahhaahhhhhaahhahaaaahaahahaahhh-aaaaahhhhaaahhahaahhaa *T264 118.317 264 194.917 264 145.417 177.233 264 76.667 90.75 76.167 104.083 194.5 75.917 75.833 90.25 103.667 128.4 122.25 264 72.6 264 264 264 81.717 264 264 116.483 87.467 264 - 74.417 264 264 174.567 88.583 264 95 264 86.05 71.517 112.767 264 264 117.817 185.3 85.367 264 70.883 98.45 85.1 216.367 94.65 111.817 90.9 264 170.517 111.717 264 75.383 84.35 97.667 97.783 264 90.433 264 90.05 90.083 90.117 264 71.967 264 - 264 264 74.267 - - 264 264 264 109.867 264 264 96.017 136.417 168.25 120.7 114.55 94.033 67.683 93.833 93.867 139.867 117.933 77.8 117.833 264 77.733 93.183 77.633 77.55 264 117.433 93.067 99.867 82.333 163.75 82.017 264 264 91.283 140.767 81.733 75.667 76.483 116.467 116.517 139.55 264 116.2 qtl/tests/test_tidyIO.R0000644000175100001440000000051312424176622014622 0ustar hornikuserslibrary(qtl) data(hyper) # write to tidy format write.cross(hyper, "tidy", "hyper_tidy") # read back in x <- read.cross("tidy", "", genfile="hyper_tidy_gen.csv", mapfile="hyper_tidy_map.csv", phefile="hyper_tidy_phe.csv", genotypes=c("BB", "BA", "AA")) # compare results comparecrosses(x, hyper) qtl/tests/test_scanonevar.Rout.save0000644000175100001440000000576512424414457017264 0ustar hornikusers R version 3.1.1 (2014-07-10) -- "Sock it to Me" Copyright (C) 2014 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin13.1.0 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(qtl) > data(map10) > map10 <- map10[1:2] > set.seed(8789993) > simcross <- sim.cross(map10, n.ind=125, type="bc", + model=rbind(c(1, 50, 1.5), c(2, 50, 0))) > simcross$pheno[,1] <- simcross$pheno[,1] + rnorm(nind(simcross), 0, 2*simcross$qtlgeno[,2]) > simcross <- calc.genoprob(simcross) > out <- scanonevar(simcross, + tol=0.01) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 58.6 1.65 107.5 0.841 2 2 62.2 1.17 51.8 5.817 > > #### > > data(fake.bc) > fake.bc <- fake.bc[1:2,1:150] # only chr 1 and 2, and first 100 individuals > fake.bc <- calc.genoprob(fake.bc, step=5) > out <- scanonevar(fake.bc, + tol=0.01) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 9.8 0.995 20 0.514 2 2 30.0 2.200 50 1.702 > covar <- fake.bc$pheno[,c("sex", "age")] > out <- scanonevar(fake.bc, mean_covar=covar, var_covar=covar, + tol=0.01) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 5 0.725 37.1 2.31 2 2 30 5.202 30.0 2.28 > > #########Simulate a vQTL on Chromosome 1######## > > chromo=1 > qtl.position=14 # 50 cM > N=nind(fake.bc) > a1<-fake.bc$geno[[chromo]]$prob[,,1] > y <- fake.bc$pheno$pheno1 > y <- y + rnorm(N,0,exp(a1[,qtl.position])) > out <- scanonevar(fake.bc, y, mean_covar=covar, var_covar=covar) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 45 0.784 70.0 5.781 2 2 0 0.368 21.8 0.672 > > out <- scanonevar(fake.bc, y, mean_covar=covar, + tol=0.01) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 45 0.746 70.0 6.012 2 2 0 0.380 72.1 0.617 > > out <- scanonevar(fake.bc, y, var_covar=covar, + tol=0.01) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 45.0 0.896 70.0 3.41 2 2 72.1 0.645 21.8 0.53 > > out <- scanonevar(fake.bc, y, + tol=0.01) > summary(out, format="allpeaks") chr pos neglogP_mean pos neglogP_disp 1 1 45.0 0.838 70.0 3.486 2 2 72.1 0.654 21.8 0.486 > > proc.time() user system elapsed 8.548 0.071 8.682 qtl/src/0000755000175100001440000000000012566656320011672 5ustar hornikusersqtl/src/hmm_main.h0000644000175100001440000003603612566656321013641 0ustar hornikusers/********************************************************************** * * hmm_main.h * * copyright (c) 2001-2010, Karl W Broman * * last modified Jul, 2010 * first written Feb, 2001 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * These functions are for the main HMM engine * * Contains: calc_genoprob, calc_genoprob_special, sim_geno, est_map, argmax_geno, * calc_errorlod, est_rf, calc_pairprob, calc_pairprob_condindep, * R_calc_pairprob_condindep, marker_loglik * **********************************************************************/ #define TOL 1.0e-12 /********************************************************************** * * calc_genoprob * * This function uses the Lander-Green algorithm to calculate the * genotype probabilities at each of marker and (optionally) at points * in-between markers, conditional on all marker data for a chromosome. * This assumes data on a single chromosome * * n_ind Number of individuals * * n_pos Number of markers (or really positions at which to * calculate the genotype probabilities) * * n_gen Number of different genotypes * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * rf Recombination fractions * * rf2 A second set of recombination fractions, in case of * sex-specific maps (may be ignored) * * error_prob Genotyping error probability * * genoprob Genotype probabilities (the output); a single vector * stored by columns (ind moves fastest, then mar, then * genotype * * initf Function returning log Pr(g_i) * * emitf Function returning log Pr(O_i | g_i) * * stepf Function returning log Pr(g_2 | g_1) * **********************************************************************/ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ void calc_genoprob(int n_ind, int n_pos, int n_gen, int *geno, double *rf, double *rf2, double error_prob, double *genoprob, double initf(int, int *), double emitf(int, int, double, int *), double stepf(int, int, double, double, int *)); /********************************************************************** * * calc_genoprob_special * * This is a special version of calc_genoprob, rerun specially for * each individual at each marker, assuming that that genotype may * be in error but others are without error * **********************************************************************/ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ void calc_genoprob_special(int n_ind, int n_pos, int n_gen, int *geno, double *rf, double *rf2, double error_prob, double *genoprob, double initf(int, int *), double emitf(int, int, double, int *), double stepf(int, int, double, double, int *)); /********************************************************************** * * sim_geno * * This function simulates from the joint distribution Pr(g | O) * * n_ind Number of individuals * * n_pos Number of markers (or really positions at which to * simulate genotypes) * * n_gen Number of different genotypes * * n_draws Number of simulation replicates * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * rf Recombination fractions * * rf2 A second set of recombination fractions, in case of * sex-specific maps * * error_prob Genotyping error probability * * draws Simulated genotypes (the output), a single vector * stored by columns (ind moves fastest, then mar, then * draws * * initf Function returning log Pr(g_i) * * emitf Function returning log Pr(O_i | g_i) * * stepf Function returning log Pr(g_2 | g_1) * **********************************************************************/ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ void sim_geno(int n_ind, int n_pos, int n_gen, int n_draws, int *geno, double *rf, double *rf2, double error_prob, int *draws, double initf(int, int *), double emitf(int, int, double, int *), double stepf(int, int, double, double, int *)); /********************************************************************** * * est_map * * This function re-estimates the genetic map for a chromosome * * n_ind Number of individuals * * n_mar Number of markers * * n_gen Number of different genotypes * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * rf Recombination fractions * * rf2 Second set of recombination fractions (may not be needed) * * error_prob Genotyping error probability * * initf Function returning log Pr(g_i) * * emitf Function returning log Pr(O_i | g_i) * * stepf Function returning log Pr(g_2 | g_1) * * nrecf1 Function returning number of recombinations associated * with (g_1, g_2) * * nrecf2 Another such function, used only in the case of a sex- * specific map * * loglik Value of loglik at final estimates of rec fracs. * * maxit Maximum number of iterations to perform * * tol Tolerance for determining convergence * * sexsp Indicates whether sex-specific maps should be estimated * * verbose Indicates whether to print initial and final rec fracs * **********************************************************************/ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ void est_map(int n_ind, int n_mar, int n_gen, int *geno, double *rf, double *rf2, double error_prob, double initf(int, int *), double emitf(int, int, double, int *), double stepf(int, int, double, double, int *), double nrecf1(int, int, double, int*), double nrecf2(int, int, double, int*), double *loglik, int maxit, double tol, int sexsp, int verbose); /********************************************************************** * * argmax_geno * * This function uses the Viterbi algorithm to calculate the most * likely sequence of underlying genotypes, given the observed marker * data for a chromosome. * This assumes data on a single chromosome * * n_ind Number of individuals * * n_pos Number of markers (or really positions at which to * find most likely genotypes * * n_gen Number of different genotypes * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * rf Recombination fractions * * rf2 A second set of recombination fractions, in case of * sex-specific maps (may be ignored) * * error_prob Genotyping error probability * * argmax Matrix of most likely genotypes (the output); a single * vector stored by columns (ind moves fastest, then pos) * * initf Function returning log Pr(g_i) * * emitf Function returning log Pr(O_i | g_i) * * stepf Function returning log Pr(g_2 | g_1) * **********************************************************************/ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ void argmax_geno(int n_ind, int n_pos, int n_gen, int *geno, double *rf, double *rf2, double error_prob, int *argmax, double initf(int, int *), double emitf(int, int, double, int*), double stepf(int, int, double, double, int *)); /********************************************************************** * * calc_errorlod * * Uses the results of calc_genoprob to calculate a LOD score for * each genotype, indicating whether it is likely to be in error. * * n_ind, n_mar, n_gen, geno These are all as in the above funcs * error_prob, genoprob * * errlod The output, as a single vector stored by columns, * of size n_ind x n_mar * * errorlod Function taking observed genotype, genotype probs, * and error probability, and returning the error LOD * **********************************************************************/ void calc_errorlod(int n_ind, int n_mar, int n_gen, int *geno, double error_prob, double *genoprob, double *errlod, double errorlod(int, double *, double)); /********************************************************************** * * est_rf * * Estimate sex-averaged recombination fractions for all pairs of loci * * This is for f2 and 4way crosses; backcrosses don't need the EM * algorithm, since there is no partially missing data. * * n_ind Number of individuals * * n_mar Number of markers * * geno Matrix of genotype data (n_ind x n_mar), stored as a * single vector (by columns) * * rf The output: matrix of doubles (n_mar x n_mar), stored * as a single vector (by columns). The diagonal will * contain the number of meioses, the upper triangle will * contain the est'd rec fracs, and the lower triangle * will contain the LOD scores (testing rf=0.5) * * erec Function returning the expected number of recombination * events given observed marker genotypes * * logprec Function returning the log probability of a pair of * observed genotypes, given the recombination fraction * (for calculating the LOD score) * * maxit Maximum number of iterations in the EM algorithm * * tol Tolerance for determining convergence of the EM * * meioses_per No. meioses per individual * **********************************************************************/ void est_rf(int n_ind, int n_mar, int *geno, double *rf, double erec(int, int, double, int *), double logprec(int, int, double, int *), int maxit, double tol, int meioses_per); /********************************************************************** * * calc_pairprob * * This function uses the hidden Markov model technology to calculate * the joint genotype probabilities for all pairs of putative QTLs. * This assumes data on a single chromosome * * n_ind Number of individuals * * n_pos Number of markers (or really positions at which to * calculate the genotype probabilities) * * n_gen Number of different genotypes * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * rf Recombination fractions * * rf2 A second set of recombination fractions, in case of * sex-specific maps (may be ignored) * * error_prob Genotyping error probability * * genoprob Genotype probabilities (the output); a single vector, * of length n_ind x n_pos x n_gen, stored by columns * (ind moves fastest, then mar, then genotype * * pairprob Joint genotype probabilities for pairs of positions. * A single vector of length n_ind x n_pos x (n_pos-1)/2 x * n_gen^2. We only calculate probabilities for * pairs (i,j) with i < j. * * initf Function returning log Pr(g_i) * * emitf Function returning log Pr(O_i | g_i) * * stepf Function returning log Pr(g_2 | g_1) * **********************************************************************/ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ void calc_pairprob(int n_ind, int n_pos, int n_gen, int *geno, double *rf, double *rf2, double error_prob, double *genoprob, double *pairprob, double initf(int, int *), double emitf(int, int, double, int *), double stepf(int, int, double, double, int *)); /********************************************************************** * * calc_pairprob_condindep * * This function calculates the joint genotype probabilities assuming * conditional independence of QTL genotypes given the marker data * * n_ind Number of individuals * * n_pos Number of markers (or really positions at which to * calculate the genotype probabilities) * * n_gen Number of different genotypes * * genoprob QTL genotype probabilities given the marker data * * pairprob Joint genotype probabilities for pairs of positions. * A single vector of length n_ind x n_pos x (n_pos-1)/2 x * n_gen^2. We only calculate probabilities for * pairs (i,j) with i < j. * **********************************************************************/ void calc_pairprob_condindep(int n_ind, int n_pos, int n_gen, double ***Genoprob, double *****Pairprob); /* wrapper for calc_pairprob_condindep */ void R_calc_pairprob_condindep(int *n_ind, int *n_pos, int *n_gen, double *genoprob, double *pairprob); /********************************************************************** * * marker_loglik * * This function calculates the log likelihood for a fixed marker * * n_ind Number of individuals * * n_gen Number of different genotypes * * geno Genotype data, as a single vector * * error_prob Genotyping error probability * * initf Function returning log Pr(g_i) * * emitf Function returning log Pr(O_i | g_i) * * loglik Loglik at return * **********************************************************************/ /* Note: true genotypes coded as 1, 2 */ void marker_loglik(int n_ind, int n_gen, int *geno, double error_prob, double initf(int, int *), double emitf(int, int, double, int *), double *loglik); /* end of hmm_main.h */ qtl/src/mqmprob.cpp0000644000175100001440000003211612566656321014057 0ustar hornikusers/********************************************************************** * * mqmprob.cpp * * Copyright (c) 1996-2011 by * Ritsert C Jansen, Danny Arends, Pjotr Prins and Karl W Broman * * initial MQM C code written between 1996-2002 by Ritsert C. Jansen * improved for the R-language by Danny Arends, Pjotr Prins and Karl W. Broman * * Modified by Danny Arends and Pjotr Prins * last modified Feb 2011 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * **********************************************************************/ #include "mqm.h" #include /* This function walks the marker list and determins for every position whether the marker is in the Middle, Left,Right of the chromosome When there is only 1 marker on a chromosome it is defined Unlinked*/ cvector relative_marker_position(const unsigned int nmark,const ivector chr){ cvector position = newcvector(nmark); // info("Calculating relative genomepositions of the markers"); for(unsigned int j=0; j < nmark; j++) { if(j==0) { if (chr[j]==chr[j+1]) position[j]=MLEFT; else position[j]=MUNLINKED; } else if (j==nmark-1) { if (chr[j]==chr[j-1]) position[j]=MRIGHT; else position[j]=MUNLINKED; } else if (chr[j]==chr[j-1]) { if (chr[j]==chr[j+1]) position[j]=MMIDDLE; else position[j]=MRIGHT; } else { if (chr[j]==chr[j+1]) position[j]=MLEFT; else position[j]=MUNLINKED; } } return position; } /*Using haldane we calculate Recombination frequencies. Using relative marker positions and distances Return array of rec. frequencies (unknown = 999.0)*/ //NOTE checking for r[j] <0 (marker ordering) can ahppen at contract vector recombination_frequencies(const unsigned int nmark, const cvector position, const vector mapdistance){ vector r = newvector(nmark); // info("Estimating recombinant frequencies"); for(unsigned int j=0; j0.0001)) { iem+=1; rdelta= 0.0; /* calculate weights = conditional genotype probabilities */ for (i=0; i %f\n", j, r[j], (*mapdistance)[j]); } } if (verbose==1) Rprintf("INFO: Re-estimation of the genetic map took %d iterations, to reach a rdelta of %f\n", iem, rdelta); return maximum; } /* ML estimation of parameters in mixture model via EM; maximum-likelihood * estimation of parameters in the mixture model via the EM algorithm, using * multilocus information, but assuming known recombination frequencies */ double QTLmixture(MQMMarkerMatrix loci, cvector cofactor, vector r, cvector position, vector y, ivector ind, int Nind, int Naug, int Nloci, double *variance, int em, vector *weight, const bool useREML,const bool fitQTL,const bool dominance, MQMCrossType crosstype, bool* warned, int verbose) { //debug_trace("QTLmixture called Nloci=%d Nind=%d Naug=%d, REML=%d em=%d fit=%d domi=%d cross=%c\n",Nloci,Nind,Naug,useREML,em,fitQTL,dominance,crosstype); //for (int i=0; i 0 en calc_i > 0 then we want to assert Ploci[] != 0 } } if ((position[j]==MLEFT)||(position[j]==MMIDDLE)) { for (i=0; itrait,indweight weight Ploci\n"); //for (int j=0; j%f,%f %f %f\n", j, y[j],indweight[i], (*weight)[j], Ploci[j]); //} double logL = 0; vector indL = newvector(Nind); while ((iem1.0e-5)) { iem+=1; if (!varknown) *variance=-1.0; logL = regression(Nind, Nloci, cofactor, loci, y, weight, ind, Naug, variance, Fy, biasadj, fitQTL, dominance, verbose); logL = 0.0; for (i=0; i #include #include #include #include #include #include #include #include #include "util.h" #include "fitqtl_imp_binary.h" #include "fitqtl_hk_binary.h" #define TOL 1e-12 #define IDXINTQ 15 /* maximum no. QTLs in an interaction */ #define IDXINTC 10 /* maximum no. covariates in an interaction */ void R_fitqtl_imp_binary(int *n_ind, int *n_qtl, int *n_gen, int *n_draws, int *draws, int *n_cov, double *cov, int *model, int *n_int, double *pheno, int *get_ests, /* return variables */ double *lod, int *df, double *ests, double *ests_covar, double *design_mat, /* convergence */ double *tol, int *maxit, int *matrix_rank) { int ***Draws; double **Cov=0; /* reorganize draws */ reorg_draws(*n_ind, *n_qtl, *n_draws, draws, &Draws); /* reorganize cov (if they are not empty) */ /* currently reorg_errlod function is used to reorganize the data */ if(*n_cov != 0) reorg_errlod(*n_ind, *n_cov, cov, &Cov); fitqtl_imp_binary(*n_ind, *n_qtl, n_gen, *n_draws, Draws, Cov, *n_cov, model, *n_int, pheno, *get_ests, lod, df, ests, ests_covar, design_mat, *tol, *maxit, matrix_rank); } /********************************************************************** * * fitqtl_imp_binary * * Fits a fixed multiple-QTL model by multiple imputation. * * n_ind Number of individuals * * n_qtl Number of QTLs in the model * * n_gen Number of different genotypes * * n_draws Number of impiutations * * Draws Array of genotype imputations, indexed as * Draws[draw][mar][ind] * * Cov covariates matrix, Cov[mar][ind] * * n_cov Number of covariates * * model Model matrix * * n_int Number of interactions in the model * * pheno Phenotype data, as a vector * * get_ests 0/1: If 1, return estimated effects and their variances * * lod Return LOD score * * df Return degree of freedom * * ests Return ests (vector of length sizefull) * * ests_covar Return covariance matrix of ests (sizefull^2 matrix) * * tol Tolerance for convergence * * maxit Maximum number of iterations in IRLS * * matrix_rank Return min (across imputations) of rank of design matrix * **********************************************************************/ void fitqtl_imp_binary(int n_ind, int n_qtl, int *n_gen, int n_draws, int ***Draws, double **Cov, int n_cov, int *model, int n_int, double *pheno, int get_ests, double *lod, int *df, double *ests, double *ests_covar, double *design_mat, double tol, int maxit, int *matrix_rank) { /* create local variables */ int i, j, ii, jj, n_qc, itmp; /* loop variants and temp variables */ double llik, llik0, *LOD_array; double *the_ests, *the_covar, **TheEsts, ***TheCovar; double *dwork, **Ests_covar, tot_wt=0.0, *wts; double **Covar_mean, **Mean_covar, *mean_ests; /* for ests and cov matrix */ int *iwork, sizefull, n_trim, *index; /* number to trim from each end of the imputations */ n_trim = (int) floor( 0.5*log(n_draws)/log(2.0) ); /* initialization */ sizefull = 1; /* calculate the dimension of the design matrix for full model */ n_qc = n_qtl+n_cov; /* total number of QTLs and covariates */ /* for additive QTLs and covariates*/ for(i=0; i 0; 2 -> 1. For F2, genotype 1 -> [0 0]; 2 -> [1 0]; 3 ->[0 1]. For 4-way, 1 -> [0 0 0], 2 -> [1 0 0], 3 -> [0 1 0], 4 -> [0 0 1] and so on */ for(i=0; i=0; k--) { itmp1 = idx_int_q[k]; tmp_idx += (Draws[itmp1][j]-2)*itmp2; itmp2 *= n_gen[idx_int_q[k]]; } if(tmp_idx != 0) kk = 0; x[(idx_col+tmp_idx)*n_ind+j] = 1; /* interaction with covariates */ for(k=0; k kk) *matrix_rank = kk; /* get ests; need to permute back */ for(i=0; i /* #include */ #include "util.h" #define THRESH 200.0 /********************************************************************** * * addlog * * Calculate addlog(a,b) = log[exp(a) + exp(b)] * * This makes use of the function log1p(x) = log(1+x) provided * in R's math library. * **********************************************************************/ double addlog(double a, double b) { if(b > a + THRESH) return(b); else if(a > b + THRESH) return(a); else return(a + log1p(exp(b-a))); } /********************************************************************** * * subtrlog * * Calculate subtrlog(a,b) = log[exp(a) - exp(b)] * * This makes use of the function log1p(x) = log(1+x) provided * in R's math library. * **********************************************************************/ double subtrlog(double a, double b) { if(a > b + THRESH) return(a); else return(a + log1p(-exp(b-a))); } /********************************************************************** * * reorg_geno * * Reorganize the genotype data so that it is a doubly indexed array * rather than a single long vector * * Afterwards, geno indexed like Geno[mar][ind] * * Allocation done by R_alloc, so that R does the cleanup. * **********************************************************************/ void reorg_geno(int n_ind, int n_pos, int *geno, int ***Geno) { int i; *Geno = (int **)R_alloc(n_pos, sizeof(int *)); (*Geno)[0] = geno; for(i=1; i< n_pos; i++) (*Geno)[i] = (*Geno)[i-1] + n_ind; } /********************************************************************** * * reorg_genoprob * * Reorganize the genotype probability data so that it is a triply * indexed array rather than a single long vector * * Afterwards, genoprob indexed like Genoprob[gen][mar][ind] * * Allocation done by R_alloc, so that R does the cleanup. * **********************************************************************/ void reorg_genoprob(int n_ind, int n_pos, int n_gen, double *genoprob, double ****Genoprob) { int i, j; double **a; *Genoprob = (double ***)R_alloc(n_gen, sizeof(double **)); a = (double **)R_alloc(n_pos*n_gen, sizeof(double *)); (*Genoprob)[0] = a; for(i=1; i< n_gen; i++) (*Genoprob)[i] = (*Genoprob)[i-1]+n_pos; for(i=0; i pos1 * * You *must* refer to cases with pos2 > pos1, as cases with * pos2 <= pos1 point off into the ether. * * Allocation done by R_alloc, so that R does the cleanup. * **********************************************************************/ void reorg_pairprob(int n_ind, int n_pos, int n_gen, double *pairprob, double ******Pairprob) { int i, j, k, s, n_pairs; double ****ptr1, ***ptr2, **ptr3; /* note: n_pos must be at least 2 */ n_pairs = n_pos*(n_pos-1)/2; *Pairprob = (double *****)R_alloc(n_gen, sizeof(double ****)); ptr1 = (double ****)R_alloc(n_gen*n_gen, sizeof(double ***)); (*Pairprob)[0] = ptr1; for(i=1; i 1) { for(k=0,sums=0.0; k= Peaks[i][j]) count++; Pval[i][j] = (double)count/(double)n_perms; } } } /* end of util.c */ qtl/src/scanone_em.h0000644000175100001440000000644112566656321014160 0ustar hornikusers/********************************************************************** * * scanone_em.h * * copyright (c) 2001-2010, Karl W Broman * * last modified Jul, 2010 * first written Nov, 2001 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * These functions are for performing a genome scan with a * single QTL model by interval mapping (the EM algorithm). * * Contains: R_scanone_em, scanone_em * **********************************************************************/ /********************************************************************** * * R_scanone_em * * Wrapper for call from R; reorganizes genotype prob and result matrix * and calls scanone_em. * **********************************************************************/ void R_scanone_em(int *n_ind, int *n_pos, int *n_gen, double *genoprob, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, double *weights, double *result, int *std_start, double *start, int *maxit, double *tol, int *verbose, int *ind_noqtl); /********************************************************************** * * scanone_em * * Performs genome scan using interval mapping. (The multipoint * genotype probabilities have already been calculated in * calc.genoprob) * * n_ind Number of individuals * * n_pos Number of marker positions * * n_gen Number of different genotypes * * Genoprob Array of conditional genotype probabilities * * pheno Phenotype data, as a vector * * weights Vector of positive weights, of length n_ind * * result Upon exit, the LOD scores * * std_start If 1, use the usual starting points [initial weights as * Pr(QTL geno | marker genotypes)] * If -1, use iid Bernoulli(1/2) for initial weights. * If 0, use the specified values for the means and SD as * the starting point. * * start If std_start = 0, use these as initial estimates of the * genotype-specific means and the residual SD. * * maxit Maximum number of iterations in the EM algorithm * * tol Tolerance for determining convergence in EM * * work Workspace of dimension 4 x n_gen * * means Space for the phenotype means at each genotype * **********************************************************************/ void scanone_em(int n_ind, int n_pos, int n_gen, double ***Genoprob, double *pheno, double *weights, double *result, int std_start, double *start, int maxit, double tol, double **work, double *means); /* end of scanone_em.h */ qtl/src/markerlrt.h0000644000175100001440000000237512566656321014056 0ustar hornikusers/********************************************************************** * * markerlrt.h * * copyright (c) 2010, Karl W Broman * * last modified Jul, 2010 * first written Jul, 2010 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * These functions are for performing a general likelihood ratio test for * each pair of markers, to assess their association. * * Contains: R_markerlrt, markerlrt * **********************************************************************/ void R_markerlrt(int *n_ind, int *n_mar, int *geno, int *maxg, double *lod); void markerlrt(int n_ind, int n_mar, int **Geno, int maxg, double **Lod); /* end of markerlrt.h */ qtl/src/scantwo_binary_em.h0000644000175100001440000001013512566656321015547 0ustar hornikusers/********************************************************************** * * scantwo_binary_em.h * * copyright (c) 2004-6, Karl W Broman * * last modified Oct, 2006 * first written Dec, 2004 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * These functions are for performing a 2-dimensional genome scan * with a 2-QTL model by interval mapping (the EM algorithm) for * a binary trait. * * Contains: R_scantwo_1chr_binary_em, scantwo_1chr_binary_em, * R_scantwo_2chr_binary_em, scantwo_2chr_binary_em, * scantwo_binary_em_estep, scantwo_binary_em_mstep * **********************************************************************/ void R_scantwo_1chr_binary_em(int *n_ind, int *n_pos, int *n_gen, double *pairprob, double *addcov, int *n_addcov, double *intcov, int *n_intcov, int *pheno, double *start, double *result, int *maxit, double *tol, int *verbose, int *n_col2drop, int *col2drop); void scantwo_1chr_binary_em(int n_ind, int n_pos, int n_gen, double *****Pairprob, double **Addcov, int n_addcov, double **Intcov, int n_intcov, int *pheno, double *start, double **Result, int maxit, double tol, int verbose, int n_col2drop, int *col2drop); void R_scantwo_2chr_binary_em(int *n_ind, int *n_pos1, int *n_pos2, int *n_gen1, int *n_gen2, double *genoprob1, double *genoprob2, double *addcov, int *n_addcov, double *intcov, int *n_intcov, int *pheno, double *start, double *result_full, double *result_add, int *maxit, double *tol, int *verbose); void scantwo_2chr_binary_em(int n_ind, int n_pos1, int n_pos2, int n_gen1, int n_gen2, double ***Genoprob1, double ***Genoprob2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, int *pheno, double *start, double **Result_full, double **Result_add, int maxit, double tol, int verbose); void scantwo_binary_em_mstep(int n_ind, int n_gen1, int n_gen2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, int *pheno, double ***Wts12, double *param, int full_model, int n_col, int *error_flag, int n_col2drop, int *allcol2drop, int verbose); void scantwo_binary_em_estep(int n_ind, int n_gen1, int n_gen2, double ***Probs, double ***Wts12, double **Addcov, int n_addcov, double **Intcov, int n_intcov, int *pheno, double *param, int full_model, int rescale, int n_col2drop, int *allcol2drop); double scantwo_binary_em_loglik(int n_ind, int n_gen1, int n_gen2, double ***Probs, double **Addcov, int n_addcov, double **Intcov, int n_intcov, int *pheno, double *param, int full_model, int n_col2drop, int *allcol2drop); /* end of scantwo_binary_em.h */ qtl/src/mqmdatatypes.cpp0000644000175100001440000001417212566656321015115 0ustar hornikusers/********************************************************************** * * mqmdatatypes.cpp * * Copyright (c) 1996-2009 by * Ritsert C Jansen, Danny Arends, Pjotr Prins and Karl W Broman * * initial MQM C code written between 1996-2002 by Ritsert C. Jansen * improved for the R-language by Danny Arends, Pjotr Prins and Karl W. Broman * * Modified by Danny Arends and Pjotr Prins * last modified November 2009 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * **********************************************************************/ #include "mqm.h" /* * Determine the experimental cross type from the R/qtl dataset. Returns the * type. */ MQMCrossType determine_MQMCross(const int Nmark, const int Nind, const int **Geno, const RqtlCrossType rqtlcrosstype) { MQMCrossType crosstype = CUNKNOWN; switch(rqtlcrosstype) { case RC_F2: crosstype = CF2; break; case RC_RIL: crosstype = CRIL; break; case RC_BC: crosstype = CBC; break; } for (int j=0; j < Nmark; j++) { for (int i=0; i < Nind; i++) { //Some checks to see if the cross really is the cross we got (So BC can't contain 3's (BB) and RILS can't contain 2's (AB) if (Geno[j][i] != 9 && Geno[j][i] > 3 && (rqtlcrosstype) != 1) { Rprintf("INFO: ind = %d marker = %d Geno = %d\n", i+1, j+1, Geno[j][i]); Rprintf("INFO: Unexpected genotype pattern, switching to F2\n"); crosstype = CF2; break; } if (Geno[j][i] == 3 && (rqtlcrosstype) == 2) { Rprintf("INFO: Unexpected genotype pattern, switching from BC to F2\n"); crosstype = CF2; break; } //IF we have a RIL and find AB then the set is messed up; we have a BC genotype if (Geno[j][i] == 2 && (rqtlcrosstype) == 3) { Rprintf("INFO: Unexpected genotype pattern, switching from RIL to BC\n"); crosstype = CBC; break; } } } return crosstype; } /* * Change all the genotypes from default R/qtl format to MQM internal. R/qtl * uses internally { 'AA' => 1, 'H' => 2, 'BB' => 3, 'NOTBB' => 4, 'NOTAA' => 5, * '-' => 'NA' } * */ void change_coding(int *Nmark, int *Nind, int **Geno, MQMMarkerMatrix markers, const MQMCrossType crosstype) { //info("Convert codes R/qtl -> MQM"); for (int j=0; j < *Nmark; j++) { for (int i=0; i < *Nind; i++) { switch (Geno[j][i]) { case 1: markers[j][i] = MAA; break; case 2: markers[j][i] = MH; if (crosstype == CRIL) markers[j][i] = MBB; // FIXME test break; case 3: markers[j][i] = MBB; break; case 4: markers[j][i] = MNOTBB; break; case 5: markers[j][i] = MNOTAA; break; case 9: markers[j][i] = MMISSING; break; default: error("Can not convert R/qtl genotype with value %d",Geno[j][i]); } } } } /* * Allocate a memory block using the 'safe' R method calloc_init, but with * guarantee all data has been zeroed */ void *calloc_init(size_t num, size_t size) { void *buf; buf = S_alloc(num,(int)size); return buf; } vector newvector(int dim) { vector v = (double *)calloc_init(dim, sizeof(double)); if(!v){ warning("Not enough memory for new vector of dimension %d", (dim+1)); } return v; } ivector newivector(int dim) { ivector v = (int *)calloc_init(dim, sizeof(int)); if(!v){ warning("Not enough memory for new vector of dimension %d", (dim+1)); } return v; } cvector newcvector(int dim) { cvector v = (char *)calloc_init(dim, sizeof(char)); if(!v){ warning("Not enough memory for new vector of dimension %d", (dim+1)); } return v; } MQMMarkerVector newMQMMarkerVector(int dim) { MQMMarkerVector v = (MQMMarker *)calloc_init(dim, sizeof(MQMMarker)); if(!v){ warning("Not enough memory for new mqm marker vector of dimension %d", (dim+1)); } return v; } relmarkerarray newRelMarkerPos(int dim){ relmarkerarray v = (MQMRelMarkerPos *)calloc_init(dim, sizeof(char)); if(!v){ warning("Not enough memory for the relative marker position vector with dimension %d",(dim+1)); } return v; } matrix newmatrix(int rows, int cols) { matrix m = (double **)calloc_init(rows, sizeof(double*)); if(!m){ warning("Not enough memory for new double matrix"); } for (int i=0; i j+1 with steps of stepsize=20 cM, starting from -20 cM up to 220 cM 2. all marker-cofactors in the neighborhood of the QTL are dropped by using cM='windows' as criterium */ nextinterval= 'n'; #ifndef STANDALONE R_CheckUserInterrupt(); /* check for ^C */ R_FlushConsole(); #endif while (nextinterval=='n') { // step 1: // Rprintf("DEBUG testing STEP 1"); if (position[j]==MLEFT) { if (moveQTL<=mapdistance[j]) { QTLposition[j]= position[j]; QTLposition[j+1]= MMIDDLE; QTLr[j]= recombination_frequentie((mapdistance[j]-moveQTL)); QTLr[j+1]= r[j]; QTLloci[j+1]= marker[j]; QTLloci[j]= marker[Nloci-1]; QTLmapdistance[j]= moveQTL; QTLmapdistance[j+1]= mapdistance[j]; if (firsttime=='y') weight[0]= -1.0; moveQTL+= stepsize; } else if (moveQTL<=mapdistance[j+1]) { QTLposition[j]= position[j]; QTLposition[j+1]= MMIDDLE; QTLr[j]= recombination_frequentie((moveQTL-mapdistance[j])); QTLr[j+1]= recombination_frequentie((mapdistance[j+1]-moveQTL)); //r[j]; QTLloci[j]= marker[j]; QTLloci[j+1]= marker[Nloci-1]; QTLmapdistance[j]= mapdistance[j]; QTLmapdistance[j+1]= moveQTL; moveQTL+= stepsize; } else nextinterval= 'y'; } else if (position[j]==MMIDDLE) { if (moveQTL<=mapdistance[j+1]) { QTLposition[j]= position[j]; QTLposition[j+1]= MMIDDLE; QTLr[j]= recombination_frequentie((moveQTL-mapdistance[j])); //0.0; QTLr[j+1]= recombination_frequentie((mapdistance[j+1]-moveQTL)); //r[j]; QTLloci[j]= marker[j]; QTLloci[j+1]= marker[Nloci-1]; QTLmapdistance[j]= mapdistance[j]; QTLmapdistance[j+1]= moveQTL; moveQTL+= stepsize; } else nextinterval= 'y'; } else if (position[j]==MRIGHT) { if (moveQTL<=stepmax) { QTLposition[j]= MMIDDLE; QTLposition[j+1]= MRIGHT; QTLr[j]= recombination_frequentie((moveQTL-mapdistance[j])); //0.0; QTLr[j+1]= r[j]; // note r[j]=999.0 QTLloci[j]= marker[j]; QTLloci[j+1]= marker[Nloci-1]; QTLmapdistance[j]= mapdistance[j]; QTLmapdistance[j+1]= moveQTL; moveQTL+= stepsize; } else { nextinterval= 'y'; moveQTL= stepmin; } } else if (position[j]==MUNLINKED) { QTLposition[j]= MLEFT; QTLposition[j+1]= MRIGHT; //position[j] ?? MRIGHT ? QTLr[j]= 0.0; QTLr[j+1]= r[j]; QTLloci[j+1]= marker[j]; QTLloci[j]= marker[Nloci-1]; QTLmapdistance[j]= mapdistance[j]; QTLmapdistance[j+1]= mapdistance[j]; if (firsttime=='y') weight[0]= -1.0; nextinterval= 'y'; moveQTL= stepmin; } if (nextinterval=='n') { // QTLcofactor[j]= MAA; // QTLcofactor[j+1]= MAA; for (jj=0; jj0) (*Frun)[step][run]+= QTLlikelihood; else (*Frun)[step][0]+= QTLlikelihood; /* Each individual has condition multilocus probabilities for being 0, 1 or 2 at the QTL. Calculate the maximum per individu. Calculate the mean of this maximum, averaging over all individuals This is the information content plotted. */ infocontent= 0.0; for (int i=0; i #include #include #include #include #include #include #include #include "util.h" #include "lapackutil.h" #include "scantwo_imp.h" #include "scanone_imp.h" #define TOL 1.0e-12 /********************************************************************** * * R_scantwo_imp * * Wrapper for call from R; reorganizes genotype prob, additive and * interactive covariates and result matrix. Then calls scantwo_imp. * **********************************************************************/ void R_scantwo_imp(int *n_ind, int *same_chr, int *n_pos1, int *n_pos2, int *n_gen1, int *n_gen2, int *n_draws, int *draws1, int *draws2, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, int *nphe, double *weights, double *result, int *n_col2drop, int *col2drop) { int ***Draws1, ***Draws2=0; double **Addcov=0, **Intcov=0; /* reorganize draws */ reorg_draws(*n_ind, *n_pos1, *n_draws, draws1, &Draws1); if(!(*same_chr)) reorg_draws(*n_ind, *n_pos2, *n_draws, draws2, &Draws2); /* reorganize addcov and intcov (if they are not empty) */ /* currently reorg_geno function is used to reorganized the data */ if(*n_addcov != 0) reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); if(*n_intcov != 0) reorg_errlod(*n_ind, *n_intcov, intcov, &Intcov); /* call the engine function scantwo_imp */ scantwo_imp(*n_ind, *same_chr, *n_pos1, *n_pos2, *n_gen1, *n_gen2, *n_draws, Draws1, Draws2, Addcov, *n_addcov, Intcov, *n_intcov, pheno, *nphe, weights, result, *n_col2drop, col2drop); } /********************************************************************** * * scantwo_imp * * Performs genotype pair scan using the pseudomarker algorithm * (imputation) method of Sen and Churchill (2001). * * n_ind Number of individuals * * same_chr If = 1, work only with Draws1 and do 2-QTL model with * QTLs on the same chromosome. * * chr2 Chromesome id 2 * * n_pos1 Number of marker positions in chromesome 1 * * n_pos2 Number of marker positions in chromesome 2 * * n_gen1 Number of different genotypes on chr 1 * * n_gen2 Number of different genotypes on chr 2 * * n_draws Number of impiutations * * Draws1 Array of genotype imputations in chromesome 1, * indexed as Draws1[repl][mar][ind] * * Draws2 Array of genotype imputations in chromesome 2, * indexed as Draws2[repl][mar][ind] * * addcov Additive covariates matrix, addcov[mar][ind] * * n_addcov Number of additive covariates * * intcov Interacting covariates matrix, intcov[mar][ind] * * n_intcov Number of interacting covariates * * pheno Phenotype data, as a vector * * nphe Number of phenotypes * * weights Vector of positive weights, of length n_ind * * result Result vector of length [n_pos1*n_pos2]; * * n_col2drop For X chromosome, number of columns to drop * * col2drop For X chromosome, indicates which columns to drop * **********************************************************************/ void scantwo_imp(int n_ind, int same_chr, int n_pos1, int n_pos2, int n_gen1, int n_gen2, int n_draws, int ***Draws1, int ***Draws2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, int nphe, double *weights, double *result, int n_col2drop, int *col2drop) { /* create local variables */ int i, i1, i2, j, k; /* loop variants */ double **lrss0, **lrss1, **LODfull, **LODadd,*lod_tmp; double *dwork_null, *dwork_add, *dwork_full, *tmppheno, dtmp; int nrss, n_col_null, n_col_a, n_col_f, n_gen_sq; int lwork, nlod_per_draw, multivar=0; int *allcol2drop; /* if number of pheno is 1 or do multivariate model, we have only one rss at each position. Otherwise, we have one rss for each phenotype */ if( (nphe==1) || (multivar==1) ) nrss = 1; else nrss = nphe; /* constants */ n_gen_sq = n_gen1*n_gen2; /* number of columns of X for null model */ n_col_null = 1 + n_addcov; /* number of columns of X for additive model */ n_col_a = (n_gen1+n_gen2-1) + n_addcov + n_intcov*(n_gen1+n_gen2-2); /* number of columns of X for full model */ n_col_f = n_gen_sq + n_addcov + n_intcov*(n_gen_sq-1); /* expand col2drop */ if(n_col2drop) { allocate_int(n_col_f, &allcol2drop); expand_col2drop(n_gen1, n_addcov, n_intcov, col2drop, allcol2drop); } /********************* * allocate memory *********************/ tmppheno = (double *)R_alloc(n_ind*nphe, sizeof(double)); /* for rss' and lod scores - we might not need all of this memory */ lrss0 = (double **)R_alloc(n_draws, sizeof(double*)); lrss1 = (double **)R_alloc(n_draws, sizeof(double*)); LODadd = (double **)R_alloc(n_draws, sizeof(double*)); LODfull = (double **)R_alloc(n_draws, sizeof(double*)); for(i=0; i 1) { for(k=0; k 1) { for(k=0; k #include #include #include #include #include #include #include #include #include "util.h" #include "fitqtl_imp.h" #define TOL 1e-12 #define IDXINTQ 15 /* maximum no. QTLs in an interaction */ #define IDXINTC 10 /* maximum no. covariates in an interaction */ void R_fitqtl_imp(int *n_ind, int *n_qtl, int *n_gen, int *n_draws, int *draws, int *n_cov, double *cov, int *model, int *n_int, double *pheno, int *get_ests, /* return variables */ double *lod, int *df, double *ests, double *ests_covar, double *design_mat, int *matrix_rank, double *residuals) { int ***Draws; double **Cov=0; /* reorganize draws */ reorg_draws(*n_ind, *n_qtl, *n_draws, draws, &Draws); /* reorganize cov (if they are not empty) */ /* currently reorg_errlod function is used to reorganize the data */ if(*n_cov != 0) reorg_errlod(*n_ind, *n_cov, cov, &Cov); fitqtl_imp(*n_ind, *n_qtl, n_gen, *n_draws, Draws, Cov, *n_cov, model, *n_int, pheno, *get_ests, lod, df, ests, ests_covar, design_mat, matrix_rank, residuals); } /********************************************************************** * * fitqtl_imp * * Fits a fixed multiple-QTL model by multiple imputation. * * n_ind Number of individuals * * n_qtl Number of QTLs in the model * * n_gen Number of different genotypes * * n_draws Number of impiutations * * Draws Array of genotype imputations, indexed as * Draws[draw][mar][ind] * * Cov covariates matrix, Cov[mar][ind] * * n_cov Number of covariates * * model Model matrix * * n_int Number of interactions in the model * * pheno Phenotype data, as a vector * * get_ests 0/1: If 1, return estimated effects and their variances * * lod Return LOD score * * df Return degree of freedom * * ests Return ests (vector of length sizefull) * * ests_covar Return covariance matrix of ests (sizefull^2 matrix) * * matrix_rank Return min (across imputations) of rank of design matrix * * residuals On return, the residuals (averaged across imputations) from the fit * **********************************************************************/ void fitqtl_imp(int n_ind, int n_qtl, int *n_gen, int n_draws, int ***Draws, double **Cov, int n_cov, int *model, int n_int, double *pheno, int get_ests, double *lod, int *df, double *ests, double *ests_covar, double *design_mat, int *matrix_rank, double *residuals) { /* create local variables */ int i, j, ii, jj, n_qc, itmp; /* loop variants and temp variables */ double lrss, lrss0, *LOD_array; double *the_ests, *the_covar, **TheEsts, ***TheCovar; double *dwork, **Ests_covar, tot_wt=0.0, *wts; double **Covar_mean, **Mean_covar, *mean_ests; /* for ests and cov matrix */ int *iwork, sizefull, n_trim, *index; /* number to trim from each end of the imputations */ n_trim = (int) floor( 0.5*log(n_draws)/log(2.0) ); /* initialization */ sizefull = 1; /* calculate the dimension of the design matrix for full model */ n_qc = n_qtl+n_cov; /* total number of QTLs and covariates */ /* for additive QTLs and covariates*/ for(i=0; i 0; 2 -> 1. For F2, genotype 1 -> [0 0]; 2 -> [1 0]; 3 ->[0 1]. For 4-way, 1 -> [0 0 0], 2 -> [1 0 0], 3 -> [0 1 0], 4 -> [0 0 1] and so on */ for(i=0; i=0; k--) { itmp1 = idx_int_q[k]; tmp_idx += (Draws[itmp1][j]-2)*itmp2; itmp2 *= n_gen[idx_int_q[k]]; } x[(idx_col+tmp_idx)*n_ind+j] = 1; /* interaction with covariates */ for(k=0; k k) *matrix_rank = k; /* calculate RSS */ for(i=0; i #include #include #include #include #include #include /* for Calloc, Realloc */ #include #include "util.h" #include "simulate.h" /********************************************************************** * * R_sim_bc_ni Wrapper for sim_bc_ni * * geno is empty, of size n_mar * n_ind * **********************************************************************/ void R_sim_bc_ni(int *n_mar, int *n_ind, double *rf, int *geno) { int **Geno; reorg_geno(*n_ind, *n_mar, geno, &Geno); GetRNGstate(); sim_bc_ni(*n_mar, *n_ind, rf, Geno); PutRNGstate(); } /********************************************************************** * * sim_bc_ni Simulate backcross under no interference * * n_mar Number of markers * n_ind Number of individuals * rf recombination fractions (length n_mar-1) * Geno Matrix of size n_ind x n_mar to contain genotype data * **********************************************************************/ void sim_bc_ni(int n_mar, int n_ind, double *rf, int **Geno) { int i, j; for(i=0; i 0) * p Probability chiasma comes from no interference mechanism * Geno Matrix of size n_ind x n_mar to contain genotype data * **********************************************************************/ void sim_bc(int n_mar, int n_ind, double *pos, int m, double p, int **Geno) { int i, j, k, first; int n_chi, max_chi, n_ni_xo; double *chi, L; L = pos[n_mar-1]; /* length of chromosome in cM */ /* space to place the crossover locations */ max_chi = (int)qpois(1e-10, L/50.0*(double)(m+2), 0, 0); chi = (double *)Calloc(max_chi, double); for(i=0; i 0) n_ni_xo = (int)rpois(L/50.0*p); else n_ni_xo = 0; if(n_chi + n_ni_xo > max_chi) { /* need more space */ max_chi = n_chi + n_ni_xo; chi = (double *)Realloc(chi, max_chi, double); } /* simulate locations */ for(j=0; j= n_chi) n_chi = 0; else { for(j=first, k=0; j 0) { /* thin with probability 1/2 */ for(j=0, k=0; j #include #include #include #include #include #include "hmm_main.h" #include "util.h" void init_stepf(double *rf, double *rf2, int n_gen, int n_mar, int *cross_scheme, double stepf(int, int, double, double, int *), double **probmat) { int j,obs1,obs2,tmp1; for(j=0; j obs2) { tmp1 = obs2; obs2 = obs1; obs1 = tmp1; } tmp1 = ((obs2 * (obs2 - 1)) / 2) - 1; return(probmat[mar][obs1 + tmp1]); } void forward_prob(int i, int n_mar, int n_gen, int curpos, int *cross_scheme, double error_prob, int **Geno, double **probmat, double **alpha, double initf(int, int *), double emitf(int, int, double, int *)) { /* forward equations */ /* Note: true genotypes coded as 1, 2, ... but in the alpha's and beta's, we use 0, 1, ... */ int j,v,v2; double errortol,salpha; /* initialize alpha */ /* curpos = -1: use error_prob always */ /* curpos >= 0: use TOL except when j == curpos, then use error_prob */ errortol = error_prob; if(curpos > 0) errortol = TOL; for(v=0; v= 0: use TOL except when j2+1 == curpos, then use error_prob */ errortol = error_prob; if(curpos >= 0) errortol = TOL; for(j2=n_mar-2; j2>=0; j2--) { if(curpos == j2+1) errortol = error_prob; for(v=0; v= 0) { j0 = curpos; jmax = j0 + 1; } /* calculate genotype probabilities */ for(j=j0; j= y[1]) { x[0] = x[1]; x[1] = x[3]; y[0] = y[1]; y[1] = y[3]; } else { x[2] = x[0]; x[0] = x[3]; y[2] = y[0]; y[0] = y[3]; } } /* handle boundary situations cleanly */ if((x[0] == 0.0 && y[0] >= y[1]) || (x[2] == 0.0 && y[2] >= y[1])) return(0.0); if((x[0] == 1.0 && y[0] >= y[1]) || (x[2] == 1.0 && y[2] >= y[1])) return(1.0); x[1] = (x[2] + x[0]) / 2.0; /* make negative if does not converge */ if(iter >= maxit) x[1] = - x[1]; return(x[1]); } /* end of hmm_util.c */ qtl/src/hmm_bci.c0000644000175100001440000003516212566656321013444 0ustar hornikusers/********************************************************************** * * hmm_bci.c * * copyright (c) 2006-2012, Karl W Broman * (Some code adapted from code from Nicola Armstrong) * * last modified Aug, 2012 * first written Aug, 2006 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * Contains: est_map_bci, * R_est_map_bci, * emit_bci, nrec_bci, step_bci, * tm_bci, fms_bci, distinct_tm_bci * * These are functions for the HMM under the Stahl model * (with chiasmata coming from two mechanisms: one following a * chi-square model and one following a no interference model). * m = interference parameter in the chi-square model (m=0 == NI) * p = proportion of chiasmata from the NI model (p=1 == NI) * * Code for is for a backcross. * * BACKCROSS: * Genotype codes: 0, ..., 2(m+1) - 1, with the first (m+1) * corresponding to AA and the others to AB * Phenotype codes: 0=missing; 1=AA; 2=AB * **********************************************************************/ #include #include #include #include #include #include #include #include "util.h" #include "hmm_bci.h" #include "stahl_mf.h" /********************************************************************** * * est_map_bci * * This function re-estimates the genetic map for a chromosome * with the Stahl model, taking m and p known, for a backcross * * n_ind Number of individuals * * n_mar Number of markers * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * d inter-marker distances in cM * (on exit, contains the new estimates) * * m Interference parameter (non-negative integer) * * p Proportion of chiasmata from the NI mechanism * * error_prob Genotyping error probability * * loglik Loglik at final estimates of recombination fractions * * maxit Maximum number of iterations to perform * * tol Tolerance for determining convergence * **********************************************************************/ void est_map_bci(int n_ind, int n_mar, int *geno, double *d, int m, double p, double error_prob, double *loglik, int maxit, double tol, int verbose) { int i, j, j2, v, v2, it, flag=0, **Geno, n_states; double s, **alpha, **beta, **gamma, *cur_d, *rf; double ***tm, *temp; double curloglik; double initprob; int ndigits; double maxdif, tempdif; char pattern[100], text[200]; n_states = 2*(m+1); initprob = -log((double)n_states); /* allocate space for beta and reorganize geno */ reorg_geno(n_ind, n_mar, geno, &Geno); allocate_alpha(n_mar, n_states, &alpha); allocate_alpha(n_mar, n_states, &beta); allocate_dmatrix(n_states, n_states, &gamma); allocate_double(n_mar-1, &cur_d); allocate_double(n_mar-1, &rf); /* allocate space for the transition matrices */ /* size n_states x n_states x (n_mar-1) */ /* tm[state1][state2][interval] */ tm = (double ***)R_alloc(n_states, sizeof(double **)); tm[0] = (double **)R_alloc(n_states * n_states, sizeof(double *)); for(i=1; i 16) ndigits=16; sprintf(pattern, "%s%d.%df", "%", ndigits+3, ndigits+1); } for(j=0; j 0.5-tol/100.0) rf[j] = 0.5-tol/100.0; } /* use map function to convert back to distances */ for(j=0; j1) { /* print estimates as we go along*/ Rprintf(" %4d ", it+1); maxdif=0.0; for(j=0; j 0) tm[v1][v2][i] = (1.0-rfp)*tm[v1][v2][i] + rfp*tm_bci(v1, (v2+m+1) % (2*m+2), the_distinct_tm, m); tm[v1][v2][i] = log(tm[v1][v2][i]); } } } } /***************************************************************************** * tm_bci: this function calculates the required transition probability for the * backcross case ****************************************************************************/ double tm_bci(int i, int j, double *the_distinct_tm, int m) { int s, tempi, tempj; if ((i<=m && j<=m) || (i>m && j>m)) { s=j-i; if (s>=0) { return(the_distinct_tm[s]); } else { return(the_distinct_tm[abs(s)+2*m+1]); } } else if (i<=m && j>m) { if (j>(i+m)) { s=j-i; return(the_distinct_tm[s]); } else /* j <=i+m */ { s=j-i-(m+1); return(the_distinct_tm[abs(s)+2*m+1]); } } else /* i>m && j<=m */ { tempi=i-(m+1); tempj=j+(m+1); if (tempj>(tempi+m)) { s=tempj-tempi; return(the_distinct_tm[s]); } else /* tempj <=tempi+m */ { s=tempj-tempi-(m+1); return(the_distinct_tm[abs(s)+2*m+1]); } } } /***************************************************************************** * fms_bci: this function calculates the sum to infinity part of the * transition probabilities for a given lambda_t * * f should have length 2m+1 ****************************************************************************/ void fms_bci(double lambda, double *f, int m, double tol, int maxit) { int i,k; double diff; for (i=0; i<2*m+1; i++) { k=1; f[i]=0; if (i <= m) { f[i] = dpois((double)(k*(m+1)+i), lambda, 0); for(k=2; k #include #include #include #include #include #include "util.h" #include "stahl_mf.h" #include "zeroin.h" /*********************************************************************** * R_mf_stahl: wrapper for R * * n_d = length of vector d * d = genetic distances (in Morgans) * m = interference parameter (non-negative integer) * p = proportion of chiasmata from the NI mechanism (double in [0,1]) * result = vector of length n_d to contain the results **********************************************************************/ void R_mf_stahl(int *n_d, double *d, int *m, double *p, double *result) { int i; for(i=0; i<*n_d; i++) result[i] = mf_stahl(d[i], *m, *p); } /********************************************************************** * mf_stahl: map function for Stahl model * * d = genetic distances (in cM) * m = interference parameter (non-negative integer) * p = proportion of chiasmata from the NI mechanism (double in [0,1]) **********************************************************************/ double mf_stahl(double d, int m, double p) { int i; double result, lam1, lam2; lam1 = (double)(m+1) * d *(1.0-p) * 2.0; lam2 = d * p * 2.0; result = 0.0; for(i=0; i pos1 (for pos2 <= pos1, points to nothing) * * Addcov Matrix of additive covariates: Addcov[cov][ind] * * n_addcov Number of columns of Addcov * * Intcov Number of interactive covariates: Intcov[cov][ind] * * n_intcov Number of columns of Intcov * * pheno Phenotype data, as a vector * * weights Vector of positive weights, of length n_ind * * Result Result matrix of size [n_pos x n_pos]; the lower * triangle (row > col) contains the joint LODs while * the upper triangle (row < col) contains the LODs for * testing epistasis. * Note: indexed as Result[col][row] * * maxit Maximum number of iterations for EM * * tol Tolerance for determining convergence of EM * * verbose If >0, print any messages when errors occur * >1, print out log likelihoods at end of EM * and check that log likelihood doesn't go down * >2, print out initial and final log likelihoods * >3, print out log likelihood at each iteration * * n_col2drop For X chromosome, number of columns to drop * * col2drop For X chromosome, indicates which columns to drop * **********************************************************************/ void scantwo_1chr_em(int n_ind, int n_pos, int n_gen, double *****Pairprob, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double **Result, int maxit, double tol, int verbose, int n_col2drop, int *col2drop); /********************************************************************** * * R_scantwo_2chr_em * * Wrapper for call from R; reorganizes genotype prob and result matrix * and calls scantwo_2chr_em. * **********************************************************************/ void R_scantwo_2chr_em(int *n_ind, int *n_pos1, int *n_pos2, int *n_gen1, int *n_gen2, double *genoprob1, double *genoprob2, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, double *weights, double *result_full, double *result_add, int *maxit, double *tol, int *verbose); /********************************************************************** * * scantwo_2chr_em * * Performs a 2-dimensional genome scan using the EM algorithm * for a two-QTL model with the two QTL residing on the same * chromosome. * * n_ind Number of individuals * * n_pos1 Number of marker positions on chr 1 * * n_pos2 Number of marker positions on chr 2 * * n_gen1 Number of different genotypes on chr 1 * * n_gen2 Number of different genotypes on chr 2 * * Genoprob1 Array of genotype probabilities for chr 1 * indexed as Genoprob[gen][pos][ind] * * Genoprob2 Array of genotype probabilities for chr 2 * indexed as Genoprob[gen][pos][ind] * * Addcov Matrix of additive covariates: Addcov[cov][ind] * * n_addcov Number of columns of Addcov * * Intcov Number of interactive covariates: Intcov[cov][ind] * * n_intcov Number of columns of Intcov * * pheno Phenotype data, as a vector * * weights Vector of positive weights, of length n_ind * * Result_full Result matrix of size [n_pos1 x n_pos2] * containing the joint LODs * Note: indexed as Result[pos2][pos1] * * Result_add Result matrix of size [n_pos2 x n_pos1] * containing the LODs for add've models * also indexed as Result[pos2][pos1] * * maxit Maximum number of iterations for EM * * tol Tolerance for determining convergence of EM * * verbose If >0, print any messages when errors occur * >1, print out log likelihoods at end of EM * and check that log likelihood doesn't go down * >2, print out initial and final log likelihoods * >3, print out log likelihood at each iteration * **********************************************************************/ void scantwo_2chr_em(int n_ind, int n_pos1, int n_pos2, int n_gen1, int n_gen2, double ***Genoprob1, double ***Genoprob2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double **Result_full, double **Result_add, int maxit, double tol, int verbose); /********************************************************************** * * scantwo_em_mstep: M-step of the EM algorithm * * n_ind Number of individuals * * n_gen1 Number of possible genotypes at QTL 1 * * n_gen2 Number of possible genotypes at QTL 2 * * Addcov Additive covariates * * n_addcov Number of columns in Addcov * * Intcov Interactive covariates * * n_intcov Number of columns in Intcov * * pheno Phenotypes * * weights Vector of positive weights, of length n_ind * * Wts12 Pr(QTL1=v, QTL2=w | phenotype, model, marker data), * indexed as Wts[v][w][ind] * * Wts1 Marginal weights for QTL 1 * * Wts2 Marginal weights for QTL 2 * * param On output, the updated parameter estimates (incl resid SD) * * full_model If 1, include QTLxQTL interaction * * work1 Workspace of doubles, of length (n_par-1)*(n_par-1) * * work2 Workspace of doubles, of length (n_par-1) * * error_flag Set to 1 if X'X is singular * **********************************************************************/ void scantwo_em_mstep(int n_ind, int n_gen1, int n_gen2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double ***Wts12, double **Wts1, double **Wts2, double *param, int full_model, double *work1, double *work2, int *error_flag, int n_col2drop, int *allcol2drop, int verbose); /********************************************************************** * * scantwo_em_estep: E-step of the EM algorithm * * n_ind Number of individuals * * n_gen1 Number of possible genotypes at QTL 1 * * n_gen2 Number of possible genotypes at QTL 2 * * Probs Pr(QTL1=v, QTL2=w | multipoint marker data) * Indexed as Probs[v][w][ind] * * Wts12 The output: * Pr(QTL1=v, QTL2=w | marker data, phenotype, covar, param) * Indexed as Wts[v][w][ind] * * Wts1 Marginal weights for QTL 1 * * Wts2 Marginal weights for QTL 2 * * Addcov Additive covariates * * n_addcov Number of columns in Addcov * * Intcov Interactive covariates * * n_intcov Number of columns in Intcov * * pheno Phenotypes * * weights Vector of positive weights, of length n_ind * * param Current parameter estimates (including the resid SD) * * full_model If 1, use the full model (with QTLxQTL interaction) * * rescale If 1, rescale weights so that the sum to 1. * This is done so that by taking rescale=0, we can easily * calculate the log likelihood * **********************************************************************/ void scantwo_em_estep(int n_ind, int n_gen1, int n_gen2, double ***Probs, double ***Wts12, double **Wts1, double **Wts2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double *param, int full_model, int rescale, int n_col2drop, int *allcol2drop); double scantwo_em_loglik(int n_ind, int n_gen1, int n_gen2, double ***Probs, double ***Wts12, double **Wts1, double **Wts2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double *param, int full_model, int n_col2drop, int *allcol2drop); /* end of scantwo_em.h */ qtl/src/hmm_bgmagic16.c0000644000175100001440000001336012566656321014443 0ustar hornikusers/********************************************************************** * * hmm_bgmagic16.c * * copyright (c) 2011-2012, Karl W Broman * * last modified Jul, 2012 * first written Dec, 2011 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * Contains: init_bgmagic16, emit_bgmagic16, step_bgmagic16, * calc_genoprob_bgmagic16, calc_genoprob_special_bgmagic16, * argmax_geno_bgmagic16, sim_geno_bgmagic16, * est_map_bgmagic16, * marker_loglik_bgmagic16, calc_pairprob_bgmagic16, * errorlod_bgmagic16, calc_errorlod_bgmagic16 * * These are the init, emit, and step functions plus * all of the hmm wrappers for 8-way RIL by selfing. * * Genotype codes: 1-16 * "Phenotype" codes: 0=missing; otherwise binary 1-65536, with bit i * indicating SNP compatible with parent i * **********************************************************************/ #include #include #include #include #include #include #include "hmm_main.h" #include "hmm_bgmagic16.h" #include "hmm_bc.h" #include "util.h" double init_bgmagic16(int true_gen, int *ignored) { return(-4.0*M_LN2); /* log(1/16) */ } double emit_bgmagic16(int obs_gen, int true_gen, double error_prob, int *ignored) { if(obs_gen==0) return(0.0); if(obs_gen & (1 << (true_gen-1))) return(log(1.0-error_prob)); else return(log(error_prob)); } double step_bgmagic16(int gen1, int gen2, double rf, double junk, int *ignored) { int tempi; double p0, tempd; if(gen1 == gen2) { tempd = 1.0-rf; tempd = tempd*tempd; p0 = tempd*tempd; } else { if(gen1 > gen2) { /* order gen1 and gen2 */ tempi = gen1; gen1 = gen2; gen2 = tempi; } if((gen1 == gen2 - 1) && (gen2 % 2 == 0)) { /* 1:2 case */ p0 = rf*(1.0-rf)*(1.0-rf)*(1.0-rf); } else if((gen2 - gen1 <= 4) && ((gen2 % 4 == 3) || (gen2 % 4 == 0))) { /* 1:3 case */ p0 = rf*(1.0-rf)*(1.0-rf)/2.0; } else if(gen2 <= 8 || gen1 > 8) { /* 1:5 case */ p0 = rf*(1.0-rf)/4.0; } else { /* 1:9 case */ p0 = rf/8.0; } } return( log( (1.0-rf)*(1.0-rf)*(1.0-rf) * (p0 - 1.0/16.0) + 1.0/16.0 ) ); } void calc_genoprob_bgmagic16(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob) { calc_genoprob(*n_ind, *n_mar, 16, geno, rf, rf, *error_prob, genoprob, init_bgmagic16, emit_bgmagic16, step_bgmagic16); } void calc_genoprob_special_bgmagic16(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob) { calc_genoprob_special(*n_ind, *n_mar, 16, geno, rf, rf, *error_prob, genoprob, init_bgmagic16, emit_bgmagic16, step_bgmagic16); } void argmax_geno_bgmagic16(int *n_ind, int *n_pos, int *geno, double *rf, double *error_prob, int *argmax) { argmax_geno(*n_ind, *n_pos, 16, geno, rf, rf, *error_prob, argmax, init_bgmagic16, emit_bgmagic16, step_bgmagic16); } void sim_geno_bgmagic16(int *n_ind, int *n_pos, int *n_draws, int *geno, double *rf, double *error_prob, int *draws) { sim_geno(*n_ind, *n_pos, 16, *n_draws, geno, rf, rf, *error_prob, draws, init_bgmagic16, emit_bgmagic16, step_bgmagic16); } void est_map_bgmagic16(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *loglik, int *maxit, double *tol, int *verbose) { est_map(*n_ind, *n_mar, 16, geno, rf, rf, *error_prob, init_bgmagic16, emit_bgmagic16, step_bgmagic16, nrec_bc, nrec_bc, loglik, *maxit, *tol, 0, *verbose); } void marker_loglik_bgmagic16(int *n_ind, int *geno, double *error_prob, double *loglik) { marker_loglik(*n_ind, 16, geno, *error_prob, init_bgmagic16, emit_bgmagic16, loglik); } void calc_pairprob_bgmagic16(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob, double *pairprob) { calc_pairprob(*n_ind, *n_mar, 16, geno, rf, rf, *error_prob, genoprob, pairprob, init_bgmagic16, emit_bgmagic16, step_bgmagic16); } double errorlod_bgmagic16(int obs, double *prob, double error_prob) { double p=0.0, temp; int n=0, i; if(obs==0 || (obs == (1<<16) - 1)) return(0.0); for(i=0; i<16; i++) { if(obs & 1< #include #include #include #include #include #include #include #include "effectscan.h" #include "util.h" /* R_effectscan: wrapper for effectscan */ void R_effectscan(int *nind, int *ngen, int *ndraws, int *npos, int *draws, double *pheno, double *effectmapping, double *beta, double *se, int *getse) { int ***Draws; double **Beta, **SE; reorg_errlod(*ngen, *npos, beta, &Beta); reorg_errlod(*ngen, *npos, se, &SE); reorg_draws(*nind, *npos, *ndraws, draws, &Draws); effectscan(*nind, *ngen, *ndraws, *npos, Draws, pheno, effectmapping, Beta, SE, *getse); } /********************************************************************** * effectscan * * nind Number of individuals * ngen Number of genotypes * ndraws Number of imputations * npos Number of positions * Draws The imputed genotypes (dim nind x npos x ndraws) * pheno Phenotypes (length nind) * effectmapping Matrix of size ngen x ngen, giving the design matrix * for each possible genotype * Beta On exit, the estimated coefficients (dim npos x ngen) * SE On exit, the estimated standard errors (dim npos x ngen) * getse If 1, calculate SEs; if 0, don't * **********************************************************************/ void effectscan(int nind, int ngen, int ndraws, int npos, int ***Draws, double *pheno, double *mapping, double **Beta, double **SE, int getse) { int nphe=1, lwork, info, i, j, s, k, ntrim, *index, *ng, *flag; double *resid, *dwork, *var, sigmasq=0.0, *x; double *wbeta, *wvar, *wrss, *wts, totwt=0.0; double lrss0, mpheno; lwork = 4*nind; ntrim = (int) floor( 0.5*log(ndraws)/log(2.0) ); allocate_double(nind, &resid); allocate_double(lwork, &dwork); allocate_double(ngen*ndraws, &var); allocate_double(ngen*ndraws, &wbeta); allocate_double(ngen*ndraws, &wvar); allocate_double(ndraws, &wrss); allocate_double(ndraws, &wts); allocate_double(ngen*nind, &x); allocate_int(ndraws, &index); allocate_int(ngen, &ng); allocate_int(ndraws, &flag); /* log null RSS */ lrss0 = mpheno = 0.0; for(i=0; i0)&&(!finished)) { for (int j=0; jmaxlogL){ maxlogL= logL[j]; dropj = j; } } #ifndef STANDALONE R_CheckUserInterrupt(); /* check for ^C */ R_FlushConsole(); #endif //See which cofactor we need to drop, if we dont drop any (or have none left) we're finished if ( ((*newcofactor)[dropj]==MCOF) && ( F2> 2.0*(savelogL-maxlogL)) ) { savelogL= maxlogL; (*newcofactor)[dropj]= MNOCOF; Ncof-=1; if(verbose) Rprintf("INFO: Marker %d is dropped, resulting in reduced model logL = %.3f\n",(dropj+1),ftruncate3(savelogL)); } else if ( ((*newcofactor)[dropj]==MBB) && (F1> 2.0*(savelogL-maxlogL)) ) { savelogL= maxlogL; (*newcofactor)[dropj]= MNOCOF; Ncof-=1; if(verbose) Rprintf("INFO: Marker %d is dropped, resulting in logL of reduced model = %.3f\n",(dropj+1),ftruncate3(savelogL)); } else { // if (verbose) { info("Backward selection of markers to be used as cofactors has finished.\n"); } finished=true; } } if (verbose) { Rprintf("MODEL: ----------------------:MODEL:----------------------\n"); for (int j=0; j #include #include #include #include #include #include #include #include #include "util.h" #include "fitqtl_hk_binary.h" #define TOL 1e-12 void R_fitqtl_hk_binary(int *n_ind, int *n_qtl, int *n_gen, double *genoprob, int *n_cov, double *cov, int *model, int *n_int, double *pheno, int *get_ests, /* return variables */ double *lod, int *df, double *ests, double *ests_covar, double *design_mat, /* convergence */ double *tol, int *maxit, int *matrix_rank) { double ***Genoprob=0, **Cov=0; int tot_gen, i, j, curpos; /* reorganize genotype probabilities */ if(*n_qtl > 0) { Genoprob = (double ***)R_alloc(*n_qtl, sizeof(double **)); tot_gen = 0; for(i=0; i < *n_qtl; i++) tot_gen += (n_gen[i]+1); Genoprob[0] = (double **)R_alloc(tot_gen, sizeof(double *)); for(i=1; i < *n_qtl; i++) Genoprob[i] = Genoprob[i-1] + (n_gen[i-1]+1); for(i=0, curpos=0; i < *n_qtl; i++) for(j=0; j 0) idx_int_q = (int *)R_alloc(n_qtl, sizeof(int)); X = (double **)R_alloc(sizefull, sizeof(double *)); /* split the memory block: design matrix x will be (n_ind x sizefull), coef will be (1 x sizefull), resid will be (1 x n_ind), qty will be (1 x n_ind), pi, nu, z, and wt are each (1 x n_ind), qraux will be (1 x sizefull), work will be (2 x sizefull) */ X[0] = dwork; for(i=1; i 0; 2 -> 1. For F2, genotype 1 -> [0 0]; 2 -> [1 0]; 3 ->[0 1]. For 4-way, 1 -> [0 0 0], 2 -> [1 0 0], 3 -> [0 1 0], 4 -> [0 0 1] and so on */ for(i=0; i=0; k--) { /* go through QTL involved in this interaction */ thisidx = idx_int_q[k]; totrep = n_int_col / (n_gen[thisidx] * nrep); thecol = 0; for(outerrep=0; outerrep < totrep; outerrep++) { for(gen=0; gen #include #include #include #include #include #include #include "util.h" #include "hmm_main.h" #include "hmm_bci.h" #include "hmm_f2i.h" #include "stahl_mf.h" /********************************************************************** * * est_map_f2i * * This function re-estimates the genetic map for a chromosome * with the Stahl model, taking m and p known, for an intercross * * n_ind Number of individuals * * n_mar Number of markers * * geno Genotype data, as a single vector storing the matrix * by columns, with each column corresponding to a marker * * d inter-marker distances in cM * (on exit, contains the new estimates) * * m Interference parameter (non-negative integer) * * p Proportion of chiasmata from the NI mechanism * * error_prob Genotyping error probability * * loglik Loglik at final estimates of recombination fractions * * maxit Maximum number of iterations to perform * * tol Tolerance for determining convergence * **********************************************************************/ void est_map_f2i(int n_ind, int n_mar, int *geno, double *d, int m, double p, double error_prob, double *loglik, int maxit, double tol, int verbose) { int i, j, j2, v, v2, it, flag=0, **Geno, n_states, n_bcstates; double s, **alpha, **beta, **gamma, *cur_d, *rf; double ***tm, *temp; double curloglik; double initprob; double maxdif, tempdif; int ndigits; char pattern[100], text[200]; n_bcstates = 2*(m+1); n_states = n_bcstates*n_bcstates; initprob = -log((double)n_states); /* allocate space for beta and reorganize geno */ reorg_geno(n_ind, n_mar, geno, &Geno); allocate_alpha(n_mar, n_states, &alpha); allocate_alpha(n_mar, n_states, &beta); allocate_dmatrix(n_states, n_states, &gamma); allocate_double(n_mar-1, &cur_d); allocate_double(n_mar-1, &rf); /* allocate space for the [backcross] transition matrices */ /* size n_states x n_states x (n_mar-1) */ /* tm[state1][state2][interval] */ tm = (double ***)R_alloc(n_bcstates, sizeof(double **)); tm[0] = (double **)R_alloc(n_bcstates * n_bcstates, sizeof(double *)); for(i=1; i 16) ndigits=16; sprintf(pattern, "%s%d.%df", "%", ndigits+3, ndigits+1); } for(j=0; j 0.5-tol/100.0) rf[j] = 0.5-tol/100.0; } /* use map function to convert back to distances */ for(j=0; j1) { /* print estimates as we go along*/ Rprintf(" %4d ", it+1); maxdif=0.0; for(j=0; j #include #include #include #include #include #include #include #include "util.h" #include "lapackutil.h" #include "scanone_imp.h" #define TOL 1e-12 /********************************************************************** * * R_scanone_imp * * Wrapper for call from R; reorganizes genotype prob and result matrix * and calls scanone_imp. * **********************************************************************/ void R_scanone_imp(int *n_ind, int *n_pos, int *n_gen, int *n_draws, int *draws, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, int *nphe, double *weights, double *result, int *ind_noqtl) { /* reorganize draws */ int ***Draws; double **Addcov=0, **Intcov=0, **Result; reorg_draws(*n_ind, *n_pos, *n_draws, draws, &Draws); reorg_errlod(*n_pos, *nphe, result, &Result); /* reorganize addcov and intcov (if they are not empty) */ /* currently reorg_errlod function is used to reorganize the data */ if(*n_addcov != 0) reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); if(*n_intcov != 0) reorg_errlod(*n_ind, *n_intcov, intcov, &Intcov); scanone_imp(*n_ind, *n_pos, *n_gen, *n_draws, Draws, Addcov, *n_addcov, Intcov, *n_intcov, pheno, *nphe, weights, Result, ind_noqtl); } /********************************************************************** * * scanone_imp * * Performs genome scan using the pseudomarker algorithm (imputation) * method of Sen and Churchill (2001). * * n_ind Number of individuals * * n_pos Number of marker positions * * n_gen Number of different genotypes * * n_draws Number of impiutations * * Draws Array of genotype imputations, indexed as * Draws[repl][mar][ind] * * Addcov Additive covariates matrix, Addcov[mar][ind] * * n_addcov Number of additive covariates * * Intcov Interacting covariates matrix, Intcov[mar][ind] * * n_intcov Number of interacting covariates * * pheno Phenotype data, as a vector/matrix * * nphe Number of phenotypes * * weights Vector of positive weights, of length n_ind * * Result Matrix of size [n_pos x nphe]; upon return, contains * the "LPD" (log posterior distribution of QTL location). * * ind_noqtl Indicators (0/1) of which individuals should be excluded * from QTL effects. * **********************************************************************/ void scanone_imp(int n_ind, int n_pos, int n_gen, int n_draws, int ***Draws, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, int nphe, double *weights, double **Result, int *ind_noqtl) { /* create local variables */ int i, j, k, nrss, sizefull, sizenull, lwork, multivar=0; double **lrss0, **lrss1, *LOD, dtmp, *tmppheno, *dwork_null, *dwork_full; /* if number of pheno is 1 or do multivariate model, we have only one rss at each position. Otherwise, we have one rss for each phenotype */ if( (nphe==1) || (multivar==1) ) nrss = 1; else nrss = nphe; /* number of columns in design matrices for null and full model */ sizenull = 1 + n_addcov; sizefull = n_gen + n_addcov + n_intcov*(n_gen-1); /* allocate memory */ tmppheno = (double *) R_alloc(n_ind*nphe, sizeof(double)); /* for null model */ lwork = 3*sizenull + MAX(n_ind, nphe); if(multivar == 1) /* request to do multivariate normal model */ dwork_null = (double *)R_alloc(sizenull+lwork+2*n_ind*sizenull+n_ind*nphe+nphe*nphe+sizenull*nphe, sizeof(double)); else /* normal model, don't need to allocate memory for rss_det, which is nphe^2 */ dwork_null = (double *)R_alloc(sizenull+lwork+2*n_ind*sizenull+n_ind*nphe+sizenull*nphe, sizeof(double)); /* for full model */ lwork = 3*sizefull + MAX(n_ind, nphe); if(multivar == 1) /* request to do multivariate normal model */ dwork_full = (double *)R_alloc(sizefull+lwork+2*n_ind*sizefull+n_ind*nphe+nphe*nphe+sizefull*nphe, sizeof(double)); else /* normal model, don't need to allocate memory for rss_det, which is nphe^2 */ dwork_full = (double *)R_alloc(sizefull+lwork+2*n_ind*sizefull+n_ind*nphe+sizefull*nphe, sizeof(double)); /* for rss' and lod scores - we might not need all of this memory */ lrss0 = (double **)R_alloc(n_draws, sizeof(double*)); lrss1 = (double **)R_alloc(n_draws, sizeof(double*)); /*LOD = (double **)R_alloc(n_draws, sizeof(double*));*/ for(i=0; i 1) { for(k=0; k #include #include #include #include #include #include "hmm_main.h" #include "hmm_ri8sib.h" #include "hmm_bc.h" double init_ri8sib(int true_gen, int *cross_scheme) { return(-3.0*M_LN2); /* log(1/8) */ } double emit_ri8sib(int obs_gen, int true_gen, double error_prob, int *cross_scheme) { if(obs_gen==0) return(0.0); if(obs_gen & (1 << (true_gen-1))) return(log(1.0-error_prob)); else return(log(error_prob)); } double step_ri8sib(int gen1, int gen2, double rf, double junk, int *cross_scheme) { if(gen1 == gen2) return(log(1.0-rf)-log(1.0+6.0*rf)); else return(log(rf)-log(1.0+6.0*rf)); } /* this is needed for est.map; estimated recombination fractions on the RIL scale */ double step_special_ri8sib(int gen1, int gen2, double rf, double junk, int *cross_scheme) { if(gen1 == gen2) return(log(1.0-rf)); else return(log(rf)-log(7.0)); } void calc_genoprob_ri8sib(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob) { calc_genoprob(*n_ind, *n_mar, 8, geno, rf, rf, *error_prob, genoprob, init_ri8sib, emit_ri8sib, step_ri8sib); } void calc_genoprob_special_ri8sib(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob) { calc_genoprob_special(*n_ind, *n_mar, 8, geno, rf, rf, *error_prob, genoprob, init_ri8sib, emit_ri8sib, step_ri8sib); } void argmax_geno_ri8sib(int *n_ind, int *n_pos, int *geno, double *rf, double *error_prob, int *argmax) { argmax_geno(*n_ind, *n_pos, 8, geno, rf, rf, *error_prob, argmax, init_ri8sib, emit_ri8sib, step_ri8sib); } void sim_geno_ri8sib(int *n_ind, int *n_pos, int *n_draws, int *geno, double *rf, double *error_prob, int *draws) { sim_geno(*n_ind, *n_pos, 8, *n_draws, geno, rf, rf, *error_prob, draws, init_ri8sib, emit_ri8sib, step_ri8sib); } /* for estimating map, must do things with recombination fractions on the RIL scale */ void est_map_ri8sib(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *loglik, int *maxit, double *tol, int *verbose) { int i; /* expand rf */ for(i=0; i< *n_mar-1; i++) rf[i] = 7.0*rf[i]/(1.0+6.0*rf[i]); est_map(*n_ind, *n_mar, 8, geno, rf, rf, *error_prob, init_ri8sib, emit_ri8sib, step_special_ri8sib, nrec_bc, nrec_bc, loglik, *maxit, *tol, 0, *verbose); /* contract rf */ for(i=0; i< *n_mar-1; i++) rf[i] = rf[i]/(7.0-6.0*rf[i]); } /* expected no. recombinants */ double nrec2_ri8sib(int obs1, int obs2, double rf, int *cross_scheme) { int n1, n2, n12, nr, and, i, nstr=8; if(obs1==0 || obs2==0) return(-999.0); /* this shouldn't happen */ n1=n2=n12=0; and = obs1 & obs2; /* count bits */ for(i=0; i /* regression of trait on multiple cofactors y=xb+e with weight w * (xtwx)b=(xtw)y * b=inv(xtwx)(xtw)y * * performs weighted regression of trait on genotype (QTL and cofactors) for * augmented data */ int designmatrixdimensions(const cvector cofactor,const unsigned int nmark,const bool dominance){ int dimx = 1; for (unsigned int j=0; j max) max=temp; if (max==0.0) fatal("Singular matrix", ""); scale[r]=1.0/max; } for (c=0; c max) { max=temp; rowmax=r; } } if (max==0.0) fatal("Singular matrix", ""); if (rowmax!=c) { swap=m[rowmax]; m[rowmax]=m[c]; m[c]=swap; scale[rowmax]=scale[c]; (*d)= -(*d); } ndx[c]=rowmax; temp=1.0/m[c][c]; for (r=c+1; r-1; r--) { sum=b[r]; for (c=r+1; c0.001)&&(count<100)) { debug_trace("INFO df1:%d df2:%d alpha:%f\n", df1, df2, alfa); count++; halfway= (maxF+minF)/2.0; prob = pbeta(df2/(df2+df1*halfway), df2/2.0, df1/2.0, 1, 0); debug_trace("(%f, %f, %f) prob=%f\n", df2/(df2+df1*halfway), df2/2.0, df1/2.0, prob); if (prob #include #include #include #include #include #include #include #include "util.h" #include "lapackutil.h" #include "scanone_hk.h" #include "scantwo_hk.h" #include "scantwopermhk.h" #define TOL 1e-12 /* R wrapper for function to perform scantwo permutations by Haley-Knott regression within a chromosome */ void R_scantwopermhk_1chr(int *n_ind, int *n_pos, int *n_gen, double *genoprob, double *pairprob, double *addcov, int *n_addcov, double *pheno, int* n_perm, int *permindex, double *weights, double *result, int *n_col2drop, int *col2drop) { double ***Genoprob, **Result, **Addcov=0, *****Pairprob; int **Permindex; reorg_genoprob(*n_ind, *n_pos, *n_gen, genoprob, &Genoprob); reorg_pairprob(*n_ind, *n_pos, *n_gen, pairprob, &Pairprob); reorg_errlod(*n_perm, 6, result, &Result); reorg_geno(*n_ind, *n_perm, permindex, &Permindex); /* reorganize addcov (if not empty) */ if(*n_addcov > 0) { reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); scantwopermhk_1chr(*n_ind, *n_pos, *n_gen, Genoprob, Pairprob, Addcov, *n_addcov, pheno, *n_perm, Permindex, weights, Result, *n_col2drop, col2drop); } else { scantwopermhk_1chr_nocovar(*n_ind, *n_pos, *n_gen, Genoprob, Pairprob, pheno, *n_perm, Permindex, weights, Result, *n_col2drop, col2drop); } } /* scantwo perms within chr; with no covariates, can do calculations in batch */ void scantwopermhk_1chr_nocovar(int n_ind, int n_pos, int n_gen, double ***Genoprob, double *****Pairprob, double *pheno, int n_perm, int **Permindex, double *weights, double **Result, int n_col2drop, int *col2drop) { int i; double *phematrix, **Phematrix, *scanone_result, **scanone_Result; double *scantwo_result, ***scantwo_Result; int *ind_noqtl; /* setup */ allocate_double(n_perm*n_ind, &phematrix); reorg_errlod(n_ind, n_perm, phematrix, &Phematrix); create_zero_vector(&ind_noqtl, n_ind); allocate_double(n_perm*n_pos, &scanone_result); reorg_errlod(n_pos, n_perm, scanone_result, &scanone_Result); allocate_double(n_perm*n_pos*n_pos, &scantwo_result); reorg_genoprob(n_pos, n_pos, n_perm, scantwo_result, &scantwo_Result); /* order phenotypes as shuffled*/ fill_phematrix(n_ind, n_perm, pheno, Permindex, Phematrix); /* scanone */ scanone_hk(n_ind, n_pos, n_gen, Genoprob, 0, 0, 0, 0, /* null covariates */ phematrix, n_perm, weights, scanone_Result, ind_noqtl); /* scantwo */ scantwo_1chr_hk(n_ind, n_pos, n_gen, Genoprob, Pairprob, 0, 0, 0, 0, /* null covariates */ phematrix, n_perm, weights, scantwo_Result, n_col2drop, col2drop); min3d_uppertri(n_pos, n_perm, scantwo_Result, Result[0]); /* full */ min3d_lowertri(n_pos, n_perm, scantwo_Result, Result[3]); /* add */ min2d(n_pos, n_perm, scanone_Result, Result[5]); /* scanone */ for(i=0; i 0) { reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); scantwopermhk_2chr(*n_ind, *n_pos1, *n_pos2, *n_gen1, *n_gen2, Genoprob1, Genoprob2, Addcov, *n_addcov, pheno, *n_perm, Permindex, weights, Result); } else { scantwopermhk_2chr_nocovar(*n_ind, *n_pos1, *n_pos2, *n_gen1, *n_gen2, Genoprob1, Genoprob2, pheno, *n_perm, Permindex, weights, Result); } } /* scantwo perms for chr pair; with no covariates, can do calculations in batch */ void scantwopermhk_2chr_nocovar(int n_ind, int n_pos1, int n_pos2, int n_gen1, int n_gen2, double ***Genoprob1, double ***Genoprob2, double *pheno, int n_perm, int **Permindex, double *weights, double **Result) { int i; double *phematrix, **Phematrix; double *scanone_result1, **scanone_Result1; double *scanone_result2, **scanone_Result2; double *scantwo_result_full, ***scantwo_Result_Full; double *scantwo_result_add, ***scantwo_Result_Add; int *ind_noqtl; /* setup */ allocate_double(n_perm*n_ind, &phematrix); reorg_errlod(n_ind, n_perm, phematrix, &Phematrix); create_zero_vector(&ind_noqtl, n_ind); allocate_double(n_perm*n_pos1, &scanone_result1); reorg_errlod(n_pos1, n_perm, scanone_result1, &scanone_Result1); allocate_double(n_perm*n_pos2, &scanone_result2); reorg_errlod(n_pos2, n_perm, scanone_result2, &scanone_Result2); allocate_double(n_perm*n_pos1*n_pos2, &scantwo_result_full); reorg_genoprob(n_pos2, n_pos1, n_perm, scantwo_result_full, &scantwo_Result_Full); allocate_double(n_perm*n_pos1*n_pos2, &scantwo_result_add); reorg_genoprob(n_pos1, n_pos2, n_perm, scantwo_result_add, &scantwo_Result_Add); /* order phenotypes as shuffled*/ fill_phematrix(n_ind, n_perm, pheno, Permindex, Phematrix); /* scanone */ scanone_hk(n_ind, n_pos1, n_gen1, Genoprob1, 0, 0, 0, 0, /* null covariates */ phematrix, n_perm, weights, scanone_Result1, ind_noqtl); scanone_hk(n_ind, n_pos2, n_gen2, Genoprob2, 0, 0, 0, 0, /* null covariates */ phematrix, n_perm, weights, scanone_Result2, ind_noqtl); /* scantwo */ scantwo_2chr_hk(n_ind, n_pos1, n_pos2, n_gen1, n_gen2, Genoprob1, Genoprob2, 0, 0, 0, 0, /* null covariates */ phematrix, n_perm, weights, scantwo_Result_Full, scantwo_Result_Add); min2d(n_pos1, n_perm, scanone_Result1, Result[0]); min2d(n_pos2, n_perm, scanone_Result2, Result[5]); for(i=0; i #include #include #include #include #include #include #include #include "util.h" #include "ripple.h" /********************************************************************** * * ripple * * This function inspects each of a set of marker orders and counts * the number of obligate crossovers for each order. * * Input: * * n_ind = no. individuals * * n_mar = no. markers * * geno = genotype data [n_ind x n_mar] * * n_orders = no. different orders * * orders = matrix of marker orders [n_orders x n_mar] * (Note: each row contains {0, 1, ..., n_mar-1} * * nxo = the output; the number of obligate crossovers for each * order (a vector of length n_orders) * * print_by = How often to print out information? * * countxo = function to count the number of obligate crossovers in * an interval and to update the current inferred genotype * (specific for backcross, intercross, and four-way cross) * **********************************************************************/ void ripple(int n_ind, int n_mar, int *geno, int n_orders, int *orders, int *nxo, int print_by, int countxo(int *curgen, int nextgen)) { int **Geno, **Orders; int i, j, k, curgen; /* reorganize genotype data and marker order matrix */ reorg_geno(n_ind, n_mar, geno, &Geno); reorg_geno(n_orders, n_mar, orders, &Orders); for(i=0; i #include #include #include #include #include #include #include "util.h" #include "simulate_ril.h" /* wrapper for sim_ril, to be called from R */ void R_sim_ril(int *n_chr, int *n_mar, int *n_ril, double *map, int *n_str, int *m, double *p, int *include_x, int *random_cross, int *selfing, int *cross, int *ril, int *origgeno, double *error_prob, double *missing_prob, int *errors) { GetRNGstate(); sim_ril(*n_chr, n_mar, *n_ril, map, *n_str, *m, *p, *include_x, *random_cross, *selfing, cross, ril, origgeno, *error_prob, *missing_prob, errors); PutRNGstate(); } /********************************************************************** * * sim_ril * * n_chr Number of chromosomes * n_mar Number of markers on each chromosome (vector of length n_chr) * n_ril Number of RILs to simulate * * map Vector of marker locations, of length sum(n_mar) * First marker on each chromosome should be at 0. * * n_str Number of parental strains (either 2, 4, or 8) * * m Interference parameter (0 is no interference) * p For Stahl model, proportion of chiasmata from the NI model * * include_x Whether the last chromosome is the X chromosome * * random_cross Indicates whether the order of the strains in the cross * should be randomized. * * selfing If 1, use selfing; if 0, use sib mating * * cross On output, the cross used for each line * (vector of length n_ril x n_str) * * ril On output, the simulated data * (vector of length sum(n_mar) x n_ril) * * origgeno Like ril, but with no missing data * * error_prob Genotyping error probability (used nly with n_str==2) * * missing_prob Rate of missing genotypes * * errors Error indicators (n_mar x n_ril) * **********************************************************************/ void sim_ril(int n_chr, int *n_mar, int n_ril, double *map, int n_str, int m, double p, int include_x, int random_cross, int selfing, int *cross, int *ril, int *origgeno, double error_prob, double missing_prob, int *errors) { int i, j, k, tot_mar, curseg; struct individual par1, par2, kid1, kid2; int **Ril, **Cross, **Errors, **OrigGeno; int maxwork, isX, flag, max_xo, *firstmarker; double **Map, maxlen, chrlen, *work; /* count total number of markers */ for(i=0, tot_mar=0; i 1e-6) { flag = 1; break; } } if(kid1.allele[0][kid1.n_xo[0]] != kid1.allele[1][kid1.n_xo[0]]) flag = 1; } else flag = 1; } else { if(kid1.n_xo[0] == kid1.n_xo[1] && kid1.n_xo[0] == kid2.n_xo[0] && kid1.n_xo[0] == kid2.n_xo[1]) { for(k=0; k 1e-6 || fabs(kid1.xoloc[0][k] - kid2.xoloc[0][k]) > 1e-6 || fabs(kid1.xoloc[0][k] - kid2.xoloc[1][k]) > 1e-6) { flag = 1; break; } } if(kid1.allele[0][kid1.n_xo[0]] != kid1.allele[1][kid1.n_xo[0]] || kid1.allele[0][kid1.n_xo[0]] != kid2.allele[0][kid1.n_xo[0]] || kid1.allele[0][kid1.n_xo[0]] != kid2.allele[1][kid1.n_xo[0]]) flag = 1; } else flag = 1; } if(!flag) break; /* done inbreeding */ /* go to next generation */ copy_individual(&kid1, &par1); if(selfing) copy_individual(&kid1, &par2); else copy_individual(&kid2, &par2); } /* end with inbreeding of this chromosome */ /* fill in alleles */ curseg = 0; for(k=0; k kid1.xoloc[0][curseg]) curseg++; OrigGeno[i][k+firstmarker[j]] = Ril[i][k+firstmarker[j]] = Cross[i][kid1.allele[0][curseg]-1]; /* simulate missing ? */ if(unif_rand() < missing_prob) { Ril[i][k+firstmarker[j]] = 0; } else if(n_str == 2 && unif_rand() < error_prob) { /* simulate error */ Ril[i][k+firstmarker[j]] = 3 - Ril[i][k+firstmarker[j]]; Errors[i][k+firstmarker[j]] = 1; } } } /* loop over chromosomes */ } /* loop over lines */ } /********************************************************************** * allocate_individual **********************************************************************/ void allocate_individual(struct individual *ind, int max_seg) { (*ind).max_segments = max_seg; (*ind).n_xo[0] = (*ind).n_xo[1] = 0; (*ind).allele = (int **)R_alloc(2, sizeof(int *)); (*ind).allele[0] = (int *)R_alloc(2*max_seg, sizeof(int)); (*ind).allele[1] = (*ind).allele[0] + max_seg; (*ind).xoloc = (double **)R_alloc(2, sizeof(double *)); (*ind).xoloc[0] = (double *)R_alloc(2*(max_seg-1), sizeof(double)); (*ind).xoloc[1] = (*ind).xoloc[0] + (max_seg-1); } /********************************************************************** * reallocate_individual **********************************************************************/ void reallocate_individual(struct individual *ind, int old_max_seg, int new_max_seg) { int j; (*ind).max_segments = new_max_seg; (*ind).allele[0] = (int *)S_realloc((char *)(*ind).allele[0], 2*new_max_seg, 2*old_max_seg, sizeof(int)); (*ind).allele[1] = (*ind).allele[0] + new_max_seg; for(j=0; j (*to).max_segments) reallocate_individual(to, (*to).max_segments, (*from).max_segments); for(j=0; j<2; j++) { n_xo = (*to).n_xo[j] = (*from).n_xo[j]; for(i=0; i 0 && p < 1.0) { /* crossover interference */ /* simulate number of XOs and intermediates */ n = (int)rpois(L*(double)(m+1)/50.0*(1.0-p)); if(n > *maxwork) { /* need a bigger workspace */ *work = (double *)S_realloc((char *)*work, n*2, *maxwork, sizeof(double)); *maxwork = n*2; } for(i=0; i *maxwork) { /* need a bigger workspace */ *work = (double *)S_realloc((char *)*work, (n+nn)*2, *maxwork, sizeof(double)); *maxwork = (n+nn)*2; } for(i=0; i *maxwork) { /* need a bigger workspace */ *work = (double *)S_realloc((char *)*work, n*2, *maxwork, sizeof(double)); *maxwork = n*2; } for(i=0; i n_str) { if(Geno[j][i] > n_str) warning("Error in RIL genotype (%d): line %d at marker %d\n", Geno[j][i], i+1, j+1); Geno[j][i] = 0; } else { temp = Parents[Geno[j][i]-1][j]; /* SNP genotype of RIL i at marker j */ if(all_snps && unif_rand() < error_prob) { /* make it an error */ temp = 1 - temp; Errors[j][i] = 1; } Geno[j][i] = 0; for(k=0; k #include #include #include #include #include #include #include #include #include "util.h" #include "scanone_ehk.h" #define TOL 1e-12 /********************************************************************** * * R_scanone_ehk * * Wrapper for call from R; reorganizes genotype prob and result matrix * and calls scanone_ehk. * **********************************************************************/ void R_scanone_ehk(int *n_ind, int *n_pos, int *n_gen, double *genoprob, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, double *weights, double *result, int *maxit, double *tol) { double ***Genoprob, **Addcov=0, **Intcov=0; reorg_genoprob(*n_ind, *n_pos, *n_gen, genoprob, &Genoprob); /* reorganize addcov and intcov (if they are not empty) */ if(*n_addcov > 0) reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); if(*n_intcov > 0) reorg_errlod(*n_ind, *n_intcov, intcov, &Intcov); scanone_ehk(*n_ind, *n_pos, *n_gen, Genoprob, Addcov, *n_addcov, Intcov, *n_intcov, pheno, weights, result, *maxit, *tol); } /********************************************************************** * * scanone_ehk * * Performs genome scan using the extended Haley-Knott method * * n_ind Number of individuals * * n_pos Number of marker positions * * n_gen Number of different genotypes * * Genoprob Array of conditional genotype probabilities * Indexed as Genoprob[gen][pos][ind] * * Addcov Matrix of additive covariates: Addcov[cov][ind] * * n_addcov Number of columns of Addcov * * Intcov Number of interactive covariates: Intcov[cov][ind] * * n_intcov Number of columns of Intcov * * pheno Phenotype data, as a vector * * weights Vector of positive weights, of length n_ind * * result Vector of length n_pos, to contain the LOD scores * * maxit Maximum number of iterations in the EM algorithm * * tol Tolerance for determining convergence in EM * **********************************************************************/ void scanone_ehk(int n_ind, int n_pos, int n_gen, double ***Genoprob, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double *result, int maxit, double tol) { int ny, *jpvt, k, i, j, ncol, k2, k3, k4, s; int info, error_flag, flag; double *work, *x, *qty, *qraux, *coef, *resid, tol2; double sigsq, *coef_cur, sigsq_cur, loglik=0.0, loglik_cur; double *m, *v, *z, *y, *wtsq; double *rhs, *lhs, **LHS, rcond; /* tolerance for linear regression */ tol2 = TOL; ncol = n_gen + (n_gen-1)*n_intcov+n_addcov; /* allocate space and set things up*/ x = (double *)R_alloc(n_ind*ncol, sizeof(double)); coef = (double *)R_alloc(ncol, sizeof(double)); coef_cur = (double *)R_alloc(ncol, sizeof(double)); resid = (double *)R_alloc(n_ind, sizeof(double)); qty = (double *)R_alloc(n_ind, sizeof(double)); jpvt = (int *)R_alloc(ncol, sizeof(int)); qraux = (double *)R_alloc(ncol, sizeof(double)); work = (double *)R_alloc(2 * ncol, sizeof(double)); m = (double *)R_alloc(n_ind, sizeof(double)); v = (double *)R_alloc(n_ind, sizeof(double)); z = (double *)R_alloc(n_ind, sizeof(double)); y = (double *)R_alloc(n_ind, sizeof(double)); wtsq = (double *)R_alloc(n_ind, sizeof(double)); rhs = (double *)R_alloc(ncol, sizeof(double)); lhs = (double *)R_alloc(ncol*ncol, sizeof(double)); reorg_errlod(ncol, ncol, lhs, &LHS); ny = 1; for(j=0; j #include #include #include #include #include #include #include #include "util.h" #include "scantwo_mr.h" #define TOL 1e-12 /********************************************************************** * * R_scantwo_1chr_mr * * Wrapper for call from R; reorganizes genotype prob and result matrix * and calls scantwo_1chr_mr. * **********************************************************************/ void R_scantwo_1chr_mr(int *n_ind, int *n_pos, int *n_gen, int *geno, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, double *weights, double *result, int *n_col2drop, int *col2drop) { int **Geno; double **Result, **Addcov=0, **Intcov=0; reorg_geno(*n_ind, *n_pos, geno, &Geno); reorg_errlod(*n_pos, *n_pos, result, &Result); /* reorganize addcov and intcov (if they are not empty) */ if(*n_addcov > 0) reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); if(*n_intcov > 0) reorg_errlod(*n_ind, *n_intcov, intcov, &Intcov); scantwo_1chr_mr(*n_ind, *n_pos, *n_gen, Geno, Addcov, *n_addcov, Intcov, *n_intcov, pheno, weights, Result, *n_col2drop, col2drop); } /********************************************************************** * * scantwo_1chr_mr * * Performs a 2-dimensional genome scan using the Haley-Knott * regression method (regressing phenotypes on conditional genotype * probabilities) for a two-QTL model with the two QTL residing on * the same chromosome. * * n_ind Number of individuals * * n_pos Number of marker positions * * n_gen Number of different genotypes * * Geno Array of marker genotype data, indexed as * Geno[pos][ind] * * Addcov Matrix of additive covariates: Addcov[cov][ind] * * n_addcov Number of columns of Addcov * * Intcov Number of interactive covariates: Intcov[cov][ind] * * n_intcov Number of columns of Intcov * * pheno Phenotype data, as a vector * * weights Vector of positive weights, of length n_ind * * Result Result matrix of size [n_pos x n_pos]; the lower * triangle (row > col) contains the joint LODs while * the upper triangle (row < col) contains the LODs for * additve models. * Note: indexed as Result[col][row] * * n_col2drop For X chromosome, number of columns to drop * * col2drop For X chromosome, indicates which columns to drop * **********************************************************************/ void scantwo_1chr_mr(int n_ind, int n_pos, int n_gen, int **Geno, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double **Result, int n_col2drop, int *col2drop) { int ny, *jpvt, i, i2, j, k, s, this_n_ind, done_allind=0; int n_col_0, n_col_a, n_col_f, n_col_a_temp, n_col_f_temp, n_gen_sq, *which_ind; double *work, *x, *qty, *qraux, *coef, *resid, tol, lrss0, *y; double lrss0_allind=0.0; int *allcol2drop; /* tolerance for linear regression */ tol = TOL; n_gen_sq = n_gen*n_gen; /* no. param in null model */ n_col_0 = n_addcov+1; /* no. param in additive QTL model */ n_col_a = (n_gen*2-1)+n_addcov+n_intcov*(n_gen-1)*2; /* no. param full model */ n_col_f = n_gen_sq+n_addcov+n_intcov*(n_gen_sq-1); /* expand col2drop */ if(n_col2drop) { allocate_int(n_col_f, &allcol2drop); expand_col2drop(n_gen, n_addcov, n_intcov, col2drop, allcol2drop); } /* allocate space and set things up*/ which_ind = (int *)R_alloc(n_ind, sizeof(int)); y = (double *)R_alloc(n_ind, sizeof(double)); x = (double *)R_alloc(n_ind*n_col_f, sizeof(double)); coef = (double *)R_alloc(n_col_f, sizeof(double)); resid = (double *)R_alloc(n_ind, sizeof(double)); qty = (double *)R_alloc(n_ind, sizeof(double)); jpvt = (int *)R_alloc(n_col_f, sizeof(int)); qraux = (double *)R_alloc(n_col_f, sizeof(double)); work = (double *)R_alloc(2 * n_col_f, sizeof(double)); ny = 1; /* modify pheno, Addcov and Intcov with weights */ for(j=0; j 0 && Geno[i2][j] > 0) { which_ind[this_n_ind] = j; y[this_n_ind] = pheno[j]; this_n_ind++; } } if(this_n_ind > 0) { if((this_n_ind < n_ind) || !done_allind) { /* the above is to avoid repeatedly doing the null model regression in the case of complete marker data */ /* NULL MODEL */ /* fill up X matrix */ for(j=0; j 0 individuals with available data */ } /* end loop over positions */ } } /********************************************************************** * * R_scantwo_2chr_mr * * Wrapper for call from R; reorganizes genotype prob and result matrix * and calls scantwo_2chr_mr. * **********************************************************************/ void R_scantwo_2chr_mr(int *n_ind, int *n_pos1, int *n_pos2, int *n_gen1, int *n_gen2, int *geno1, int *geno2, double *addcov, int *n_addcov, double *intcov, int *n_intcov, double *pheno, double *weights, double *result_full, double *result_add) { int **Geno1, **Geno2; double **Result_full, **Result_add, **Addcov=0, **Intcov=0; reorg_geno(*n_ind, *n_pos1, geno1, &Geno1); reorg_geno(*n_ind, *n_pos2, geno2, &Geno2); reorg_errlod(*n_pos1, *n_pos2, result_full, &Result_full); reorg_errlod(*n_pos1, *n_pos2, result_add, &Result_add); /* reorganize addcov and intcov (if they are not empty) */ if(*n_addcov > 0) reorg_errlod(*n_ind, *n_addcov, addcov, &Addcov); if(*n_intcov > 0) reorg_errlod(*n_ind, *n_intcov, intcov, &Intcov); scantwo_2chr_mr(*n_ind, *n_pos1, *n_pos2, *n_gen1, *n_gen2, Geno1, Geno2, Addcov, *n_addcov, Intcov, *n_intcov, pheno, weights, Result_full, Result_add); } /********************************************************************** * * scantwo_2chr_mr * * Performs a 2-dimensional genome scan using the Haley-Knott * regression method (regressing phenotypes on conditional genotype * probabilities) for a two-QTL model with the two QTL residing on * the different chromosomes. * * n_ind Number of individuals * * n_pos1 Number of marker positions on first chromosome * * n_pos2 Number of marker positions on second chromosome * * n_gen1 Number of different genotypes for first chromosome * * n_gen2 Number of different genotypes for second chromosome * * Geno1 Matrix of marker genotype data for chr 1, * indexed as Geno1[pos][ind] * * Geno2 Matrix of marker genotype data for chr 2 * * Addcov Matrix of additive covariates: Addcov[cov][ind] * * n_addcov Number of columns of Addcov * * Intcov Number of interactive covariates: Intcov[cov][ind] * * n_intcov Number of columns of Intcov * * pheno Phenotype data, as a vector * * weights Vector of positive weights, of length n_ind * * Result_full Result matrix of size [n_pos1 x n_pos2] * containing the joint LODs * Note: indexed as Result[pos2][pos1] * * Result_add Result matrix of size [n_pos2 x n_pos1] * containing the LODs for add've models * also indexed as Result[pos2][pos1] * **********************************************************************/ void scantwo_2chr_mr(int n_ind, int n_pos1, int n_pos2, int n_gen1, int n_gen2, int **Geno1, int **Geno2, double **Addcov, int n_addcov, double **Intcov, int n_intcov, double *pheno, double *weights, double **Result_full, double **Result_add) { int ny, *jpvt, i, i2, j, k, s, this_n_ind, done_allind=0; int n_col_0, n_col_a, n_col_f, n_gen_sq, *which_ind; double *work, *x, *qty, *qraux, *coef, *resid, tol, lrss0, *y; double lrss0_allind=0.0; /* tolerance for linear regression */ tol = TOL; n_gen_sq = n_gen1*n_gen2; /* no. param in null model */ n_col_0 = n_addcov+1; /* no. param in additive QTL model */ n_col_a = (n_gen1+n_gen2-1)+n_addcov+n_intcov*(n_gen1+n_gen2-2); /* no. param full model */ n_col_f = n_gen_sq+n_addcov+n_intcov*(n_gen_sq-1); /* allocate space and set things up*/ which_ind = (int *)R_alloc(n_ind, sizeof(int)); y = (double *)R_alloc(n_ind, sizeof(double)); x = (double *)R_alloc(n_ind*n_col_f, sizeof(double)); coef = (double *)R_alloc(n_col_f, sizeof(double)); resid = (double *)R_alloc(n_ind, sizeof(double)); qty = (double *)R_alloc(n_ind, sizeof(double)); jpvt = (int *)R_alloc(n_col_f, sizeof(int)); qraux = (double *)R_alloc(n_col_f, sizeof(double)); work = (double *)R_alloc(2 * n_col_f, sizeof(double)); ny = 1; /* modify pheno, Addcov and Intcov with weights */ for(j=0; j 0 && Geno2[i2][j] > 0) { which_ind[this_n_ind] = j; y[this_n_ind] = pheno[j]; this_n_ind++; } } if(this_n_ind > 0) { if((this_n_ind < n_ind) || !done_allind) { /* the above is to avoid repeatedly doing the null model regression in the case of complete marker data */ /* NULL MODEL */ /* fill up X matrix */ for(j=0; j 0 individuals with available data */ } /* end loop over positions */ } } /* end of scantwo_mr.c */ qtl/src/standalone.h0000644000175100001440000000253112566656321014175 0ustar hornikusers/********************************************************************** * * standalone.h * * Remap R methods for standalone/biolib version of R/qtl * * copyright (c) 2009 Ritsert Jansen, Danny Arends, Pjotr Prins and Karl W Broman * * last modified Apr, 2009 * first written Mrt, 2009 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * Defines if we build a Rpackage or a standalone app, and any other functions etc we want to have changed AFTER reading all the libraries & dependancies * Contains: * **********************************************************************/ #ifndef __STANDALONE_HPP #define __STANDALONE_HPP // #define STANDALONE - should be defined in the build system #ifdef STANDALONE #undef Rprintf #define Rprintf(args...) printf(args) #endif #endif // __STANDALONE_HPP qtl/src/test_bcsft.c0000644000175100001440000001152212566656321014200 0ustar hornikusers/********************************************************************** * * hmm_bcsft.c * * copyright (c) 2001-9, Karl W Broman * modified from hmm_f2.c by Brian S Yandell and Laura M Shannon (c) 2010 * * modified Jun, 2010 * last modified Apr, 2009 * first written Feb, 2001 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * Contains: init_bcsft, emit_bcsft, step_bcsft, init_bcsftb, emit_bcsftb, step_bcsftb, * calc_genoprob_bcsft, calc_genoprob_special_bcsft, sim_geno_bcsft, est_map_bcsft, * argmax_geno_bcsft, errorlod_bcsft, calc_errorlod_bcsft, nrec2_bcsft, * logprec_bcsft, est_rf_bcsft, calc_pairprob_bcsft, marker_loglik_bcsft * * These are the init, emit, and step functions plus * all of the hmm wrappers for the F2 intercross. * * Genotype codes: 0=AA; 1=AB; 2=BB * Phenotype codes: 0=missing; 1=AA; 2=AB; 3=BB; 4=not BB; 5=not AA * **********************************************************************/ #include #include #include #include #include #include #include "hmm_main.h" #include "hmm_bcsft.h" #include "hmm_f2.h" #include "hmm_bc.h" /* ref: Jiang and Zeng (1997 Genetics) */ void prob_bcsft(double rf, int s, int t, double *transpr); void count_bcsft(double rf, int s, int t, double *transct); void expect_bcsft(double rf, int s, int t, double *transexp); void step_wrap(int *gen1, int *gen2, double *rf, int *cross_scheme, double *ret, double *transpr) { prob_bcsft(*rf, cross_scheme[0], cross_scheme[1], transpr); ret[0] = step_bcsftb(*gen1, *gen2, *rf, *rf, cross_scheme); ret[1] = step_bcsft(*gen1, *gen2, *rf, *rf, cross_scheme); ret[2] = step_bc(*gen1, *gen2, *rf, *rf, cross_scheme); ret[3] = step_f2b(*gen1, *gen2, *rf, *rf, cross_scheme); ret[4] = step_f2(*gen1, *gen2, *rf, *rf, cross_scheme); return; } void init_wrap(int *gen, int *cross_scheme, double *ret) { ret[0] = init_bcsftb(*gen, cross_scheme); ret[1] = init_f2b(*gen, cross_scheme); if(*gen < 4) { if(*gen < 4 || cross_scheme[1] > 0) ret[2] = init_bcsft(*gen, cross_scheme); ret[3] = init_f2(*gen, cross_scheme); if(*gen < 3) ret[4] = init_bc(*gen, cross_scheme); } return; } void nrec_wrap(int *gen1, int *gen2, double *rf, int *cross_scheme, double *ret) { ret[0] = nrec_bcsftb(*gen1, *gen2, *rf, cross_scheme); ret[1] = nrec_f2b(*gen1, *gen2, *rf, cross_scheme); if(*gen1 < 3 && *gen2 < 3) ret[2] = nrec_bc(*gen1, *gen2, *rf, cross_scheme); return; } void rf_wrap(int *gen1, int *gen2, double *rf, int *cross_scheme, double *ret) { ret[0] = nrec2_bcsft(*gen1, *gen2, *rf, cross_scheme); ret[1] = nrec2_f2(*gen1, *gen2, *rf, cross_scheme); ret[2] = logprec_bcsft(*gen1, *gen2, *rf, cross_scheme); ret[3] = logprec_f2(*gen1, *gen2, *rf, cross_scheme); return; } void bcsft_wrap(double *rf, int *cross_scheme, double *init, double *emit, double *step, double *stepb, double *nrec, double *transpr, double *transexp) { int gen1,gen2; static double error_prob = 0.0001; prob_bcsft(*rf, cross_scheme[0], cross_scheme[1], transpr); expect_bcsft(*rf, cross_scheme[0], cross_scheme[1], transexp); for(gen1=0; gen1<4; gen1++) { if(gen1 < 3) { init[gen1] = init_bcsft(gen1+1, cross_scheme); init[gen1+3] = init_bc(gen1+1, cross_scheme); } for(gen2=0; gen2<3; gen2++) { if((gen1 < 3) && (gen2 < 3)) { emit[gen1 + 3 * gen2] = emit_bcsft(gen1+1, gen2+1, error_prob, cross_scheme); emit[gen1 + 3 * gen2 + 9] = emit_bc(gen1+1, gen2+1, error_prob, cross_scheme); step[gen1 + 3 * gen2] = step_bcsft(gen1+1, gen2+1, *rf, *rf, cross_scheme); step[gen1 + 3 * gen2 + 9] = step_bc(gen1+1, gen2+1, *rf, *rf, cross_scheme); } nrec[gen1 + 4 * gen2] = nrec_bcsftb(gen1+1, gen2+1, *rf, cross_scheme); nrec[gen1 + 4 * gen2 + 16] = nrec_bc(gen1+1, gen2+1, *rf, cross_scheme); stepb[gen1 + 4 * gen2] = step_bcsftb(gen1+1, gen2+1, *rf, *rf, cross_scheme); stepb[gen1 + 4 * gen2 + 16] = step_bc(gen1+1, gen2+1, *rf, *rf, cross_scheme); } } return; } /* end of test_bcsft.c */ qtl/src/countXO.c0000644000175100001440000001021612566656321013436 0ustar hornikusers/********************************************************************** * * countXO.c * * copyright (c) 2008-9, Karl W Broman * * last modified Apr, 2009 * first written Feb, 2008 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * These functions are for comparing marker orders by counts of * obligate crossovers * * Contains: R_countXO_bc, R_countXO_f2, R_countXO_4way, countXO * R_countXO_ril48 * **********************************************************************/ #include #include #include #include #include #include #include #include // #include "util.h" #include "ripple.h" #include "util.h" #include "countXO.h" /********************************************************************** * * countXO * * This function counts the number of obligate crossovers for each * individual on a chromosome * * Input: * * n_ind = no. individuals * * n_mar = no. markers * * n_gen = no. possible genotypes * * geno = genotype data [n_ind x n_mar] * * nxo = the output; the number of obligate crossovers for each * individual * * countxo = function to count the number of obligate crossovers in * an interval and to update the current inferred genotype * (specific for backcross, intercross, and four-way cross) * **********************************************************************/ void countXO(int n_ind, int n_mar, int n_gen, int *geno, int *nxo, int countxo(int *curgen, int nextgen)) { int **Geno; int j, k, curgen; /* reorganize genotype data and marker order matrix */ reorg_geno(n_ind, n_mar, geno, &Geno); for(j=0; j #include #include "standalone.h" #include "util.h" #include "mqmdatatypes.h" #include "mqmprob.h" #include "mqmmixture.h" #include "mqmregression.h" #include "mqmaugment.h" #include "mqmeliminate.h" #include "mqmmapqtl.h" #include "mqmscan.h" #ifdef STANDALONE // Running mqm stand alone (without R) extern FILE* redirect_info; // Redirect output for testing extern int debuglevel; // Redirect output for testing #define message(type, format, ...) { \ fprintf(redirect_info,"%s: ",type); \ fprintf(redirect_info, format, ## __VA_ARGS__); \ fprintf(redirect_info,"\n"); } #define fatal(s, ...) { message("FATAL",s, ## __VA_ARGS__); exit(127); } #define debug_trace(format, ...) { \ if(debuglevel > 0){ \ fprintf(redirect_info,"TRACE "); \ fprintf(redirect_info,"%s %d:",__FILE__,__LINE__); \ fprintf(redirect_info,format, ## __VA_ARGS__); \ }\ } #else #ifdef ENABLE_C99_MACROS #define message(type, format, ...) { Rprintf(format, ## __VA_ARGS__);Rprintf("\n");} #define fatal(s, ...) { message("FATAL",s, ## __VA_ARGS__); Rf_error(s); } #define debug_trace(format, ...) { } #else void message(const char*, ...); void fatal(const char*, ...); void debug_trace(const char*, ...); #endif // ENABLE_C99_MACROS #endif // !STANDALONE #ifdef ENABLE_C99_MACROS #define info(format, ...) { message("INFO",format, ## __VA_ARGS__); } #define verbose(format, ...) if (verbose) { info(format, ## __VA_ARGS__); } #else void info(const char*, ...); void verbose(const char*, ...); #endif // !ENABLE_C99_MACROS #endif // MQM_H qtl/src/hmm_bcsft.c0000644000175100001440000015255712566656321014020 0ustar hornikusers/********************************************************************** * * hmm_bcsft.c * * copyright (c) 2001-9, Karl W Broman * modified from hmm_f2.c by Brian S Yandell and Laura M Shannon (c) 2010 * * modified Jun, 2010 * last modified Apr, 2009 * first written Feb, 2001 * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License, * version 3, as published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but without any warranty; without even the implied warranty of * merchantability or fitness for a particular purpose. See the GNU * General Public License, version 3, for more details. * * A copy of the GNU General Public License, version 3, is available * at http://www.r-project.org/Licenses/GPL-3 * * C functions for the R/qtl package * * Contains: init_bcsft, emit_bcsft, step_bcsft, init_bcsftb, emit_bcsftb, step_bcsftb, * calc_genoprob_bcsft, calc_genoprob_special_bcsft, sim_geno_bcsft, est_map_bcsft, * argmax_geno_bcsft, errorlod_bcsft, calc_errorlod_bcsft, nrec2_bcsft, * logprec_bcsft, est_rf_bcsft, calc_pairprob_bcsft, marker_loglik_bcsft * * These are the init, emit, and step functions plus * all of the hmm wrappers for the F2 intercross. * * Genotype codes: 0=AA; 1=AB; 2=BB * Phenotype codes: 0=missing; 1=AA; 2=AB; 3=BB; 4=not BB; 5=not AA * **********************************************************************/ #include #include #include #include #include #include #include "hmm_main.h" #include "hmm_bcsft.h" #include "hmm_f2.h" #include "hmm_bc.h" #include "hmm_util.h" #include "util.h" /* ref: Jiang and Zeng (1997 Genetics) */ void prob_bcsft(double rf, int s, int t, double *transpr); void count_bcsft(double rf, int s, int t, double *transct); void expect_bcsft(double rf, int s, int t, double *transexp); /* assign probabilities or counts based on vector of precomputed values */ /* in transpr and transct, which are re-computed only when rf,s or t changes */ double assign_bcsft(int gen1, int gen2, double *transpr) { /* joint probability with known genotype, phase unknown */ switch(gen1) { case 1: case 3: { /* AA and aa for gen1 */ if(gen2 == gen1) { if(gen1 == 1) return(transpr[0]); /* 1,1: A1 */ return(transpr[5]); /* 3,3: A0 */ } if(gen2 + gen1 == 4) return(transpr[2]); /* 1,3: C */ break; } case 2: { /* Aa and aA for gen1 */ if(gen2 == gen1) return(transpr[3]); /* 2,2: D or E */ } } if((gen1 == 1) || (gen2 == 1)) return(transpr[1]); /* 1,2: B1 */ return(transpr[6]); /* 2,3: B0 */ } double assign_bcsftb(int gen1, int gen2, double *transpr) { /* joint probability with known genotype and unknown */ switch(gen1) { case 1: case 4: /* AA and aa for gen1 */ { if(gen2 == gen1) { if(gen1 == 1) return(transpr[0]); /* 1,1: A1 */ return(transpr[5]); /* 4,4: A0 */ } if(gen2 + gen1 == 5) return(transpr[2]); /* 1,4: C */ break; } case 2: case 3: /* Aa and aA for gen1 */ { if(gen2 == gen1) return(transpr[3]); /* 2,2: D */ if(gen2 + gen1 == 5) return(transpr[4]); /* 2,3: E */ } } if((gen1 == 1) || (gen2 == 1)) return(transpr[1]); /* 1,2|3: B1 */ return(transpr[6]); /* 2|3,4: B0 */ } double assign_bcsftc(int obs1, int obs2, double *transval) { /* joint probability of obs2 and obs1, allowing for partially informative genos */ if((obs1 == 0) || (obs2 == 0)) return(0.0); /* shouldn't get here */ int temp; /* make obs1 <= obs2 */ if(obs1 > obs2) { temp = obs2; obs2 = obs1; obs1 = temp; } switch(obs1) { case 1: case 3: { /* AA and aa for obs1 */ if(obs2 == obs1) { if(obs1 == 1) return(transval[0]); /* 1,1: A1 */ return(transval[5]); /* 3,3: A0 */ } if(obs2 + obs1 == 4) return(transval[2]); /* 1,3: C */ if(obs1 == 1) { /* B1 */ if(obs1 + obs2 == 3) return(transval[1]); /* 1,2: B1 */ if(obs1 + obs2 == 5) return(transval[0] + transval[1]); /* 1,4: A1 or B1 */ return(transval[2] + transval[1]); /* 1,5: B1 or C */ } { /* B0 */ if(obs1 + obs2 == 7) return(transval[2] + transval[6]); /* 3,4: A1 or B0 */ return(transval[5] + transval[6]); /* 3,5: A0 or B0 */ } } case 2: /* Aa and aA for obs1 */ { if(obs2 == obs1) return(transval[3]); /* 2,2: D or E */ if(obs1 + obs2 == 5) return(transval[6]); /* 2,3: B0 */ if(obs1 + obs2 == 6) return(transval[1] + transval[3]); /* 2,4: A1 or B0 */ return(transval[6] + transval[3]); /* 2,5: A0 or B0 */ } case 4: /* AA or Aa for obs1 */ { if(obs1 == obs2) return(transval[0] + 2 * transval[1] + transval[3]); /* 4,4: 1 or 2 */ break; } case 5: /* Aa or aa for obs1 */ { if(obs1 == obs2) return(transval[3] + 2 * transval[6] + transval[5]); /* 5,5: 2 or 3 */ } } return(transval[1] + transval[2] + transval[3] + transval[6]); /* 4,5: 1 or 2 x 2 or 3 */ } /* end of assign functions */ /* init, emit and step when genotype known, phase unknown geno = 1,2,3 for AA,Aa,aa */ double init_bcsft(int true_gen, int *cross_scheme) { static double init1 = 0; static double init2 = 0; static double init3 = 0; static int s = -1; static int t = -1; if(s != cross_scheme[0] || t != cross_scheme[1] || init1 == 0) { s = cross_scheme[0]; t = cross_scheme[1]; /* static variables used frequently */ if(s == 0) { /* Ft */ init2 = (1 - t) * M_LN2; /* Aa: log(2 ^ (1-t)) */ init1 = log1p(-exp(init2)) - M_LN2; /* AA: log((1 - 2^(1-t)) / 2) */ init3 = init1; /* aa: */ } if(s > 0) { if(t == 0) { /* BCs */ init2 = -s * M_LN2; /* Aa: log(2 ^ -s) */ init1 = log1p(-exp(init2)); /* AA: log(1 - 2^-s) */ } if(t > 0) { /* BCsFt */ double sm2,tm2; sm2 = -s * M_LN2; tm2 = -t * M_LN2; init2 = sm2 + tm2; /* Aa: log(2 ^ -(s+t)) */ init3 = sm2 + log1p(-exp(tm2)) - M_LN2; /* aa: log(2^-s * (1 - 2^-t) / 2) */ init1 = log1p(exp(init3) - exp(sm2)); /* AA: log((1 - 2^-s) + 2^-s * (1 - 2^-t)) */ } } } switch(true_gen) { case 1: return(init1); case 2: return(init2); case 3: return(init3); } return(0.0); /* should not get here */ } void genotab_em_bcsft(int *cross_scheme, double *ret) { /* used by genotab.em */ ret[0] = exp(init_bcsft(1, cross_scheme)); ret[1] = exp(init_bcsft(2, cross_scheme)); ret[2] = exp(init_bcsft(3, cross_scheme)); ret[3] = ret[0] + ret[1]; ret[4] = ret[1] + ret[2]; return; } double emit_bcsft(int obs_gen, int true_gen, double error_prob, int *cross_scheme) { if(cross_scheme[1] > 0) return(emit_f2(obs_gen, true_gen, error_prob,cross_scheme)); return(emit_bc(obs_gen, true_gen, error_prob,cross_scheme)); } double step_bcsft(int gen1, int gen2, double rf, double junk, int *cross_scheme) { static double transpr[10]; static double oldrf = -1.0; static int s = -1; static int t = -1; if(s != cross_scheme[0] || t != cross_scheme[1] || fabs(rf - oldrf) > TOL) { s = cross_scheme[0]; t = cross_scheme[1]; oldrf = rf; if(rf < TOL) rf = TOL; prob_bcsft(rf, s, t, transpr); /* collapse when phase is unknown */ if(t > 0) { /* only if Ft in play */ transpr[3] += transpr[4]; /* D or E */ } /* put probabilities on log scale */ int k; for(k=0; k<7; k++) { /* if(transpr[k] > 0.0) */ transpr[k] = log(transpr[k]); } } double out; /* Find joint probability pr(gen1,gen2). */ out = assign_bcsft(gen1, gen2, transpr); /* Divide by marginal prob to get pr(gen2|gen1). */ out -= transpr[6+gen1]; return(out); } /****************************************************************************/ /* init, emit and step functions with phase-known genotypes (i.e. the 4-state chain: AA, Aa, aA, aa */ double init_bcsftb(int true_gen, int *cross_scheme) { static double init1 = 0; static double init2 = 0; static double init3 = 0; static double init4 = 0; static int s = -1; static int t = -1; /* static variables used frequently */ if(s != cross_scheme[0] || t != cross_scheme[1] || init1 == 0) { s = cross_scheme[0]; t = cross_scheme[1]; if(s == 0) { /* Ft */ init2 = - t * M_LN2; /* Aa: log(2 ^ -t) */ init1 = log1p(-exp(init2 + M_LN2)) - M_LN2; /* AA: log((1 - 2^(1-t)) / 2) */ init3 = init2; /* aA: */ init4 = init1; /* aa: */ } if(s > 0) { if(t == 0) { /* BCs */ init2 = -s * M_LN2; /* Aa: log(2 ^ -s) */ init1 = log1p(-exp(init2)); /* AA: log(1 - 2^-s) */ init3 = 0; init4 = 0; } if(t > 0) { /* BCsFt */ double sm2,t1m2; sm2 = -s * M_LN2; /* -s * log(2) = log(2 ^ -s) */ t1m2 = -(1 + t) * M_LN2; /* -2t * log(2) = log(2 ^ -(t+1)) */ init2 = sm2 + t1m2; /* Aa: log(2^-(s+t+1)) */ init3 = init2; /* aA: log(2^-(s+t+1)) */ init4 = subtrlog(sm2 - M_LN2, init2); /* aa: log(2^-(s+1) - 2^-(s+t+1)) */ init1 = addlog(log1p(-exp(sm2)), init4); /* AA: log((1-2^-s) + (2^-(s+1) - 2^-(s+t+1))) */ } } } switch(true_gen) { case 1: return(init1); case 2: return(init2); case 3: return(init3); case 4: return(init4); } return(0.0); /* should not get here */ } double emit_bcsftb(int obs_gen, int true_gen, double error_prob, int *cross_scheme) { if(cross_scheme[1] > 0) return(emit_f2b(obs_gen, true_gen, error_prob,cross_scheme)); return(emit_bc(obs_gen, true_gen, error_prob,cross_scheme)); } double step_bcsftb(int gen1, int gen2, double rf, double junk, int *cross_scheme) { static double oldrf = -1.0; static double transpr[10]; static int s = -1; static int t = -1; if(s != cross_scheme[0] || t != cross_scheme[1] || fabs(rf - oldrf) > TOL) { s = cross_scheme[0]; t = cross_scheme[1]; oldrf = rf; if(rf < TOL) rf = TOL; prob_bcsft(rf, s, t, transpr); /* expand when phase is known */ if(t > 0) { /* only if Ft in play */ transpr[1] /= 2.0; /* B1 split */ transpr[6] /= 2.0; /* B0 split */ transpr[3] /= 2.0; /* D split */ transpr[4] /= 2.0; /* E split */ transpr[8] -= M_LN2; /* log(pr(gen1=2)) = log(pr(gen2=3)) */ } /* put probabilities on log scale */ int k; for(k=0; k<7; k++) { /* if(transpr[k] > 0.0) */ transpr[k] = log(transpr[k]); } } double out; /* Find joint probability pr(gen1,gen2). */ out = assign_bcsftb(gen1, gen2, transpr); /* Divide by marginal prob to get pr(gen2|gen1). */ if(gen1 > 2) gen1--; out -= transpr[6+gen1]; return(out); } double nrec_bcsftb(int gen1, int gen2, double rf, int *cross_scheme) { static double oldrf = -1.0; static double transexp[10]; static int s = -1; static int t = -1; if(s != cross_scheme[0] || t != cross_scheme[1] || fabs(rf - oldrf) > TOL) { s = cross_scheme[0]; t = cross_scheme[1]; oldrf = rf; if(rf < TOL) rf = TOL; expect_bcsft(rf, s, t, transexp); /* reduce by half if t>0 *** NOT SURE IF THIS IS RIGHT THING TO DO? */ if(t > 0) { int k; for(k=0; k<7; k++) transexp[k] /= 2; } } /* Return expected count. */ return(assign_bcsftb(gen1, gen2, transexp)); } /* compute log likelihood for golden section search */ double assign_bcsftd(int n_gen, int obs1, int obs2, double *transval) { if(n_gen == 5) return(assign_bcsftc(obs1, obs2, transval)); return(assign_bcsftb(obs1, obs2, transval)); } double comploglik_bcsft(double rf, int n_gen, double *countmat, int *cross_scheme) { static double transpr[10]; static double probmat[15]; static double oldrf = -1.0; static int s = -1; static int t = -1; int obs1,obs2,tmp1; if(s != cross_scheme[0] || t != cross_scheme[1] || fabs(rf - oldrf) > TOL) { s = cross_scheme[0]; t = cross_scheme[1]; oldrf = rf; if(rf < TOL) rf = TOL; /* compute probabilities */ prob_bcsft(rf, s, t, transpr); transpr[3] += transpr[4]; for(obs2=1; obs2<=n_gen; obs2++) { tmp1 = ((obs2 * (obs2 - 1)) / 2) - 1; for(obs1=1; obs1<=obs2; obs1++) probmat[obs1 + tmp1] = assign_bcsftd(n_gen, obs1, obs2, transpr); } } double lod,temp; /* compute log likelihood */ lod = 0.0; for(obs2=1; obs2<=n_gen; obs2++) { tmp1 = ((obs2 * (obs2 - 1)) / 2) - 1; for(obs1=1; obs1<=obs2; obs1++) { temp = countmat[obs1 + tmp1]; if(temp > 0.0) lod += temp * log(probmat[obs1 + tmp1]); } } return(lod); } /****************************************************************************/ void calc_genoprobo_bcsft(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob) { /* cross_scheme is hidden in genoprob */ int n_gen; n_gen = 2; if(genoprob[1] > 0) n_gen = 3; calc_genoprob(*n_ind, *n_mar, n_gen, geno, rf, rf, *error_prob, genoprob, init_bcsft, emit_bcsft, step_bcsft); } void calc_genoprob_bcsft(int *n_ind, int *n_mar, int *geno, double *rf, double *error_prob, double *genoprob) { double **alpha, **beta, **probmat; int **Geno; double ***Genoprob; int i, cross_scheme[2]; /* cross scheme hidden in genoprob argument; used by hmm_bcsft */ cross_scheme[0] = (int)genoprob[0]; cross_scheme[1] = (int)genoprob[1]; genoprob[0] = 0.0; genoprob[1] = 0.0; int n_gen,j,v,sgeno; double temp; n_gen = 2; if(cross_scheme[1] > 0) n_gen = 3; /* allocate space for alpha and beta and reorganize geno and genoprob */ reorg_geno(*n_ind, *n_mar, geno, &Geno); reorg_genoprob(*n_ind, *n_mar, n_gen, genoprob, &Genoprob); allocate_alpha(*n_mar, n_gen, &alpha); allocate_alpha(*n_mar, n_gen, &beta); allocate_dmatrix(*n_mar, 6, &probmat); /* initialize stepf calculations */ init_stepf(rf, rf, n_gen, *n_mar, cross_scheme, step_bcsft, probmat); for(i=0; i<*n_ind; i++) { /* i = individual */ R_CheckUserInterrupt(); /* check for ^C */ sgeno = 0; for(j=0; j<*n_mar; j++) sgeno += Geno[j][i]; if(sgeno > 0) { /* forward-backward equations */ forward_prob(i, *n_mar, n_gen, -1, cross_scheme, *error_prob, Geno, probmat, alpha, init_bcsft, emit_bcsft); backward_prob(i, *n_mar, n_gen, -1, cross_scheme, *error_prob, Geno, probmat, beta, init_bcsft, emit_bcsft); /* calculate genotype probabilities */ calc_probfb(i, *n_mar, n_gen, -1, alpha, beta, Genoprob); } else { /* chromosome with no genotypes for this individual get init probabilities */ for(v=0; v