Qtl Manual
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Package ‘qtl’ February 16, 2018 Version 1.42-7 Date 2018-02-16 Title Tools for Analyzing QTL Experiments Author Karl W Bromanand Hao Wu, with ideas from Gary Churchill and Saunak Sen and contributions from Danny Arends, Robert Corty, Timothee Flutre, Ritsert Jansen, Pjotr Prins, Lars Ronnegard, Rohan Shah, Laura Shannon, Quoc Tran, Aaron Wolen, and Brian Yandell Maintainer Karl W Broman Description Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits. Depends R (>= 2.14.0) Imports parallel, graphics, stats, utils, grDevices Suggests testthat License GPL-3 URL https://rqtl.org, https://github.com/kbroman/qtl BugReports https://github.com/kbroman/qtl/issues Encoding UTF-8 R topics documented: A starting point add.cim.covar . add.threshold . addcovarint . . addint . . . . . addloctocross . addmarker . . . addpair . . . . addqtl . . . . . addtoqtl . . . . allchrsplits . . . argmax.geno . . arithscan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 10 11 12 14 16 17 18 20 22 24 25 26 R topics documented: 2 arithscanperm . . . badorder . . . . . . bayesint . . . . . . bristle3 . . . . . . bristleX . . . . . . c.cross . . . . . . . c.scanone . . . . . c.scanoneperm . . c.scantwo . . . . . c.scantwoperm . . calc.errorlod . . . . calc.genoprob . . . calc.penalties . . . cbind.scanoneperm cbind.scantwoperm checkAlleles . . . . chrlen . . . . . . . chrnames . . . . . cim . . . . . . . . clean.cross . . . . . clean.scantwo . . . cleanGeno . . . . . comparecrosses . . comparegeno . . . compareorder . . . condense.scantwo . convert.map . . . . convert.scanone . . convert.scantwo . . convert2riself . . . convert2risib . . . convert2sa . . . . . countXO . . . . . . drop.dupmarkers . drop.markers . . . drop.nullmarkers . dropfromqtl . . . . droponemarker . . effectplot . . . . . effectscan . . . . . est.map . . . . . . est.rf . . . . . . . . fake.4way . . . . . fake.bc . . . . . . . fake.f2 . . . . . . . fill.geno . . . . . . find.flanking . . . . find.marker . . . . find.markerindex . find.markerpos . . find.pheno . . . . . find.pseudomarker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 28 29 30 31 32 33 34 35 36 37 38 40 41 42 43 44 45 45 47 48 49 50 50 51 52 53 54 55 56 57 58 59 60 60 61 62 63 64 67 69 71 72 73 74 75 76 77 78 78 79 80 R topics documented: findDupMarkers . . . . . . fitqtl . . . . . . . . . . . . fitstahl . . . . . . . . . . . flip.order . . . . . . . . . . formLinkageGroups . . . . formMarkerCovar . . . . . geno.crosstab . . . . . . . geno.image . . . . . . . . geno.table . . . . . . . . . getid . . . . . . . . . . . . groupclusteredheatmap . . hyper . . . . . . . . . . . inferFounderHap . . . . . inferredpartitions . . . . . interpPositions . . . . . . jittermap . . . . . . . . . . listeria . . . . . . . . . . . locateXO . . . . . . . . . locations . . . . . . . . . . lodint . . . . . . . . . . . makeqtl . . . . . . . . . . map10 . . . . . . . . . . . map2table . . . . . . . . . mapthis . . . . . . . . . . markerlrt . . . . . . . . . markernames . . . . . . . max.scanone . . . . . . . . max.scanPhyloQTL . . . . max.scantwo . . . . . . . movemarker . . . . . . . . MQM . . . . . . . . . . . mqmaugment . . . . . . . mqmautocofactors . . . . . mqmextractmarkers . . . . mqmfind.marker . . . . . . mqmgetmodel . . . . . . . mqmpermutation . . . . . mqmplot.circle . . . . . . mqmplot.cistrans . . . . . mqmplot.clusteredheatmap mqmplot.cofactors . . . . mqmplot.directedqtl . . . . mqmplot.heatmap . . . . . mqmplot.multitrait . . . . mqmplot.permutations . . mqmplot.singletrait . . . . mqmprocesspermutation . mqmscan . . . . . . . . . mqmscanall . . . . . . . . mqmscanfdr . . . . . . . . mqmsetcofactors . . . . . mqmtestnormal . . . . . . 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 82 85 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 108 109 110 112 113 114 116 118 119 120 121 122 124 126 127 129 130 131 132 133 134 135 136 138 140 141 143 R topics documented: 4 multitrait . . . . . nchr . . . . . . . . nind . . . . . . . . nmar . . . . . . . . nmissing . . . . . . nphe . . . . . . . . nqrank . . . . . . . nqtl . . . . . . . . ntyped . . . . . . . nullmarkers . . . . orderMarkers . . . phenames . . . . . pickMarkerSubset . plot.cross . . . . . plot.qtl . . . . . . . plot.rfmatrix . . . . plot.scanone . . . . plot.scanoneboot . plot.scanoneperm . plot.scanPhyloQTL plot.scantwo . . . . plot.scantwoperm . plotErrorlod . . . . plotGeno . . . . . plotInfo . . . . . . plotLodProfile . . . plotMap . . . . . . plotMissing . . . . plotModel . . . . . plotPheno . . . . . plotPXG . . . . . . plotRF . . . . . . . pull.argmaxgeno . pull.draws . . . . . pull.geno . . . . . pull.genoprob . . . pull.map . . . . . . pull.markers . . . . pull.pheno . . . . . pull.rf . . . . . . . qtlversion . . . . . read.cross . . . . . readMWril . . . . . reduce2grid . . . . refineqtl . . . . . . reorderqtl . . . . . replace.map . . . . replacemap.scanone replacemap.scantwo replaceqtl . . . . . rescalemap . . . . ripple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 145 146 146 147 148 148 149 150 151 151 153 154 155 156 157 158 160 161 162 163 166 167 168 170 171 173 175 176 177 178 180 181 182 183 184 185 186 186 187 188 188 195 196 197 199 200 201 202 203 204 205 R topics documented: scanone . . . . . . . . . scanoneboot . . . . . . . scanonevar . . . . . . . . scanonevar.meanperm . . scanonevar.varperm . . . scanPhyloQTL . . . . . scanqtl . . . . . . . . . . scantwo . . . . . . . . . scantwopermhk . . . . . shiftmap . . . . . . . . . sim.cross . . . . . . . . sim.geno . . . . . . . . . sim.map . . . . . . . . . simFounderSnps . . . . simPhyloQTL . . . . . . simulatemissingdata . . . stepwiseqtl . . . . . . . strip.partials . . . . . . . subset.cross . . . . . . . subset.map . . . . . . . subset.scanone . . . . . subset.scanoneperm . . . subset.scantwo . . . . . subset.scantwoperm . . . summary.cross . . . . . summary.fitqtl . . . . . . summary.qtl . . . . . . . summary.ripple . . . . . summary.scanone . . . . summary.scanoneboot . . summary.scanoneperm . summary.scanPhyloQTL summary.scantwo . . . . summary.scantwoperm . summaryMap . . . . . . summaryScantwoOld . . switch.order . . . . . . . switchAlleles . . . . . . table2map . . . . . . . . top.errorlod . . . . . . . totmar . . . . . . . . . . transformPheno . . . . . tryallpositions . . . . . . typingGap . . . . . . . . write.cross . . . . . . . . xaxisloc.scanone . . . . Index 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 214 216 217 218 219 221 223 227 229 230 233 234 235 236 238 239 243 244 245 246 247 248 249 250 250 251 252 253 256 257 259 261 264 265 266 267 268 269 270 271 272 273 274 275 277 279 6 A starting point A starting point Introductory comments on R/qtl Description A brief introduction to the R/qtl package, with a walk-through of an analysis. New to R and/or R/qtl? • In order to use the R/qtl package, you must type (within R) library(qtl). You may wish to include this in a .Rprofile file. • Documention and several tutorials are available at the R archive (https://cran.r-project. org). • Use the help.start function to start the html version of the R help. • Type library(help=qtl) to get a list of the functions in R/qtl. • Use the example function to run examples of the various functions in R/qtl. • A tutorial on the use of R/qtl is distributed with the package and is also available at https: //rqtl.org/rqtltour.pdf. • Download the latest version of R/qtl from the R archive or from https://rqtl.org. Walk-through of an analysis Here we briefly describe the use of R/qtl to analyze an experimental cross. A more extensive tutorial on its use is distributed with the package and is also available at https://rqtl.org/rqtltour. pdf. 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 read.cross. The internal data structure used by R/qtl is rather complicated, and is described in the help file for read.cross. We won’t discuss data import any further here, except to say that the comma-delimited format ("csv") is recommended. If you have trouble importing data, send an email to Karl Broman, , perhaps attaching examples of your data files. (Such data will be kept confidential.) Also see the sample data files and code at https://rqtl.org/sampledata. We consider the example data hyper, an experiment on hypertension in the mouse, kindly provided by Bev Paigen and Gary Churchill. Use the data function to load the data. data(hyper) The hyper data set has class "cross". The function summary.cross gives summary information on the data, and checks the data for internal consistency. A number of other utility functions are available; hopefully these are self-explanatory. summary(hyper) nind(hyper) nphe(hyper) nchr(hyper) nmar(hyper) totmar(hyper) The function plot.cross gives a graphical summary of the data; it calls plotMissing (to plot a matrix displaying missing genotypes) and plotMap (to plot the genetic maps), and also displays histograms or barplots of the phenotypes. The plotMissing function can plot individuals ordered A starting point 7 by their phenotypes; you can see that for most markers, only individuals with extreme phenotypes were genotyped. plot(hyper) plotMissing(hyper) plotMissing(hyper, reorder=TRUE) plotMap(hyper) Note that one marker (on chromosome 14) has no genotype data. The function drop.nullmarkers removes such markers from the data. hyper <- drop.nullmarkers(hyper) totmar(hyper) The function est.rf estimates the recombination fraction between each pair of markers, and calculates a LOD score for the test of r = 1/2. This is useful for identifying markers that are placed on the wrong chromosome. Note that since, for these data, many markers were typed only on recombinant individuals, the pairwise recombination fractions show rather odd patterns. hyper <- est.rf(hyper) plotRF(hyper) plotRF(hyper, chr=c(1,4)) To re-estimate the genetic map for an experimental cross, use the function est.map. The function plotMap, in addition to plotting a single map, can plot the comparison of two genetic maps (as long as they are composed of the same numbers of chromosomes and markers per chromosome). The function replace.map map be used to replace the genetic map in a cross with a new one. newmap <- est.map(hyper, error.prob=0.01, verbose=TRUE) plotMap(hyper, newmap) hyper <- replace.map(hyper, newmap) The function calc.errorlod may be used to assist in identifying possible genotyping errors; it calculates the error LOD scores described by Lincoln and Lander (1992). The calc.errorlod function return a modified version of the input cross, with error LOD scores included. The function top.errorlod prints the genotypes with values above a cutoff (by default, the cutoff is 4.0). hyper <- calc.errorlod(hyper, error.prob=0.01) top.errorlod(hyper) The function plotGeno may be used to inspect the observed genotypes for a chromosome, with likely genotyping errors flagged. plotGeno(hyper, chr=16, ind=c(24:34, 71:81)) Before doing QTL analyses, some intermediate calculations need to be performed. The function calc.genoprob calculates conditional genotype probabilities given the multipoint marker data. sim.geno simulates sequences of genotypes from their joint distribution, given the observed marker data. As with calc.errorlod, these functions return a modified version of the input cross, with the intermediate calculations included. The step argument indicates the density of the grid on which the calculations will be performed, and determines the density at which LOD scores will be calculated. hyper <- calc.genoprob(hyper, step=2.5, error.prob=0.01) hyper <- sim.geno(hyper, step=2.5, n.draws=64, error.prob=0.01) The function scanone performs a genome scan with a single QTL model. By default, it performs standard interval mapping (Lander and Botstein 1989): use of a normal model and the EM algorithm. If one specifies method="hk", Haley-Knott regression is performed (Haley and Knott 1992). These two methods require the results from calc.genoprob. out.em <- scanone(hyper) out.hk <- scanone(hyper, method="hk") 8 A starting point If one specifies method="imp", a genome scan is performed by the multiple imputation method of Sen and Churchill (2001). This method requires the results from sim.geno. out.imp <- scanone(hyper, method="imp") The output of scanone is a data.frame with class "scanone". The function plot.scanone may be used to plot the results, and may plot up to three sets of results against each other, as long as they conform appropriately. plot(out.em) plot(out.hk, col="blue", add=TRUE) plot(out.imp, col="red", add=TRUE) plot(out.hk, out.imp, out.em, chr=c(1,4), lty=1, col=c("blue","red","black")) The function summary.scanone may be used to list information on the peak LOD for each chromosome for which the LOD exceeds a specified threshold. summary(out.em) summary(out.em, threshold=3) summary(out.hk, threshold=3) summary(out.imp, threshold=3) The function max.scanone returns the maximum LOD score, genome-wide. max(out.em) max(out.hk) max(out.imp) One may also use scanone to perform a permutation test to get a genome-wide LOD significance threshold. operm.hk <- scanone(hyper, method="hk", n.perm=1000) The result has class "scanoneperm". The summary.scanoneperm function may be used to calculate LOD thresholds. summary(operm.hk, alpha=0.05) The permutation results may also be used in the summary.scanone function to calculate LOD thresholds and genome-scan-adjusted p-values. summary(out.hk, perms=operm.hk, alpha=0.05, pvalues=TRUE) We should say at this point that the function save.image will save your workspace to disk. You’ll wish you had used this if R crashes. save.image() The function scantwo performs a two-dimensional genome scan with a two-QTL model. Methods "em", "hk" and "imp" are all available. scantwo is considerably slower than scanone, and can require a great deal of memory. Thus, you may wish to re-run calc.genoprob and/or sim.geno with a more coarse grid. hyper <- calc.genoprob(hyper, step=10, err=0.01) hyper <- sim.geno(hyper, step=10, n.draws=64, err=0.01) out2.hk <- scantwo(hyper, method="hk") out2.em <- scantwo(hyper) out2.imp <- scantwo(hyper, method="imp") The output is an object with class scantwo. The function plot.scantwo may be used to plot the results. The upper triangle contains LOD scores for tests of epistasis, while the lower triangle contains LOD scores for the full model. A starting point 9 plot(out2.hk) plot(out2.em) plot(out2.imp) The function summary.scantwo lists the interesting aspects of the output. For each pair of chromosomes (k, l), it calculates the maximum LOD score for the full model, Mf (k, l); a LOD score indicating evidence for a second QTL, allowing for epistasis), Mf v1 (k, l); a LOD score indicating evidence for epistasis, Mi (k, l); the LOD score for the additive QTL model, Ma (k, l); and a LOD score indicating evidence for a second QTL, assuming no epistasis, Mav1 (k, l). You must provide five LOD thresholds, corresponding to the above five LOD scores, and in that order. A chromosome pair is printed if either (a) Mf (k, l) ≥ Tf and (Mf v1 (k, l) ≥ Tf v1 or Mi (k, l) ≥ Ti ), or (b) Ma (k, l) ≥ Ta and Mav1 (k, l) ≥ Tav1 . summary(out2.em, thresholds=c(6.2, 5.0, 4.6, 4.5, 2.3)) summary(out2.em, thresholds=c(6.2, 5.0, Inf, 4.5, 2.3)) In the latter case, the interaction LOD score will be ignored. The function max.scantwo returns the maximum joint and additive LODs for a two-dimensional genome scan. max(out2.em) Permutation tests may also performed with scantwo; it may take a few days of CPU time. The output is a list containing the maxima of the above five LOD scores for each of the imputations. operm2 <- scantwo(hyper, method="hk", n.perm=100) summary(operm2, alpha=0.05) Citing R/qtl To cite R/qtl in publications, use the Broman et al. (2003) reference listed below. Author(s) Karl W Broman, References Broman, K. W. and Sen, Ś. (2009) A guide to QTL mapping with R/qtl. Springer. https://rqtl. org/book Broman, K. W., Wu, H., Sen, Ś. and Churchill, G. A. (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889–890. Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Lander, E. S. and Botstein, D. (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199. Lincoln, S. E. and Lander, E. S. (1992) Systematic detection of errors in genetic linkage data. Genomics 14, 604–610. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. 10 add.cim.covar add.cim.covar Indicate marker covariates from composite interval mapping Description Add dots at the locations of the selected marker covariates, for a plot of composite interval mapping results. Usage add.cim.covar(cimresult, chr, gap=25, ...) Arguments cimresult Composite interval mapping results, as output from cim. chr Optional vector specifying which chromosomes to plot. (The chromosomes must be specified by name.) This should be identical to that used in the call to plot.scanone. gap Gap separating chromosomes (in cM). This should be identical to that used in the call to plot.scanone. ... Additional plot arguments, passed to the function points. Details One must first have used the function plot.scanone to plot the composite interval mapping results. The arguments chr and gap must be identical to the values used in the call to plot.scanone. Dots indicating the locations of the selected marker covariates are displayed on the x-axis. (By default, solid red circles are plotted; this may be modified by specifying the graphics parameters pch and col.) Value A data frame indicating the marker covariates that were plotted. Author(s) Karl W Broman, See Also cim, plot.scanone Examples ## Not run: data(hyper) hyper <- calc.genoprob(hyper, step=2.5) out <- scanone(hyper) out.cim <- cim(hyper, n.marcovar=3) plot(out, out.cim, chr=c(1,4,6,15), col=c("blue", "red")) add.threshold 11 add.cim.covar(out.cim, chr=c(1,4,6,15)) ## End(Not run) add.threshold Add significance threshold to plot Description Add a significance threshold to a plot created by plot.scanone), using the permutation results. Usage add.threshold(out, chr, perms, alpha=0.05, lodcolumn=1, gap=25, ...) Arguments out An object of class "scanone", as output by scanone. This must be identical to what was used in the call to plot.scanone. chr Optional vector specifying which chromosomes to plot. If a selected subset of chromosomes were plotted, they must be specified here. perms Permutation results from scanone, used to calculate the significance threshold. alpha Significance level of the threshold. lodcolumn An integer indicating which of column in the permutation results should be used. gap Gap separating chromosomes (in cM). This must be identical to what was used in the call to plot.scanone. ... Passed to the function abline when it is called. Details This function allows you to add a horizontal line at the significance threshold to genome scan results plotted by plot.scanone. The arguments out, chr, and gap must match what was used in the call to plot.scanone. The argument perms must be specified. If X-chromosome-specific permutations were performed (via the argument perm.Xsp in the call to scanone), separate thresholds will be plotted for the autosomes and the X chromosome. These are calculated via the summary.scanoneperm function. Value None. Author(s) Karl W Broman, See Also scanone, plot.scanone, summary.scanoneperm, xaxisloc.scanone 12 addcovarint Examples data(hyper) hyper <- calc.genoprob(hyper) out <- scanone(hyper, method="hk") operm <- scanone(hyper, method="hk", n.perm=100, perm.Xsp=TRUE) plot(out, chr=c(1,4,6,15,"X")) add.threshold(out, chr=c(1,4,6,15,"X"), perms=operm, alpha=0.05) add.threshold(out, chr=c(1,4,6,15,"X"), perms=operm, alpha=0.1, col="green", lty=2) addcovarint Add QTL x covariate interaction to a multiple-QTL model Description Try adding all QTL x covariate interactions, one at a time, to a multiple QTL model, for a given set of covariates. Usage addcovarint(cross, pheno.col=1, qtl, covar=NULL, icovar, formula, method=c("imp","hk"), model=c("normal", "binary"), verbose=TRUE, pvalues=TRUE, simple=FALSE, tol=1e-4, maxit=1000, require.fullrank=FALSE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either nonintegers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl An object of class qtl, as output from makeqtl. covar A matrix or data.frame of covariates. These must be strictly numeric. icovar Vector of character strings indicating the columns in covar to be considered for QTL x covariate interactions. formula An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as Q1, Q2, etc. Covariates are referred to by their names in the data frame covar. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits verbose If TRUE, will print a message if there are no interactions to test. pvalues If FALSE, p-values will not be included in the results. simple If TRUE, don’t include p-values or sums of squares in the summary. tol Tolerance for convergence for the binary trait model. addcovarint 13 maxit Maximum number of iterations for fitting the binary trait model. require.fullrank If TRUE, give LOD=0 when covariate matrix in the linear regression is not of full rank. Details The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included. Value An object of class addcovarint, with results as in the drop-one-term analysis from fitqtl. This is a data frame (given class "addcovarint", with the following columns: degrees of freedom (df), Type III sum of squares (Type III SS), LOD score(LOD), percentage of variance explained (%var), F statistics (F value), and P values for chi square (Pvalue(chi2)) and F distribution (Pvalue(F)). Note that the degree of freedom, Type III sum of squares, the LOD score and the percentage of variance explained are the values comparing the full to the sub-model with the term dropped. Also note that for imputation method, the percentage of variance explained, the the F values and the P values are approximations calculated from the LOD score. QTL x covariate interactions already included in the input formula are not tested. Author(s) Karl W Broman, References Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also addint, fitqtl, makeqtl, scanqtl, refineqtl, addqtl, addpair Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 8, 13) qp <- c(26, 56, 28) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") # use the sex phenotype as the covariate covar <- data.frame(sex=fake.f2$pheno$sex) 14 addint # try all possible QTL x sex interactions, one at a time addcovarint(fake.f2, pheno.col=1, qtl, covar, "sex", y~Q1+Q2+Q3, method="hk") addint Add pairwise interaction to a multiple-QTL model Description Try adding all possible pairwise interactions, one at a time, to a multiple QTL model. Usage addint(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"), model=c("normal", "binary"), qtl.only=FALSE, verbose=TRUE, pvalues=TRUE, simple=FALSE, tol=1e-4, maxit=1000, require.fullrank=FALSE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl An object of class qtl, as output from makeqtl. covar A matrix or data.frame of covariates. These must be strictly numeric. formula An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as Q1, Q2, etc. Covariates are referred to by their names in the data frame covar. If the new QTL is not included in the formula, its main effect is added. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits qtl.only If TRUE, only test QTL:QTL interactions (and not interactions with covariates). verbose If TRUE, will print a message if there are no interactions to test. pvalues If FALSE, p-values will not be included in the results. simple If TRUE, don’t include p-values or sums of squares in the summary. tol Tolerance for convergence for the binary trait model. maxit Maximum number of iterations for fitting the binary trait model. require.fullrank If TRUE, give LOD=0 when covariate matrix in the linear regression is not of full rank. addint 15 Details The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included. Value An object of class addint, with results as in the drop-one-term analysis from fitqtl. This is a data frame (given class "addint", with the following columns: degrees of freedom (df), Type III sum of squares (Type III SS), LOD score(LOD), percentage of variance explained (%var), F statistics (F value), and P values for chi square (Pvalue(chi2)) and F distribution (Pvalue(F)). Note that the degree of freedom, Type III sum of squares, the LOD score and the percentage of variance explained are the values comparing the full to the sub-model with the term dropped. Also note that for imputation method, the percentage of variance explained, the the F values and the P values are approximations calculated from the LOD score. Pairwise interactions already included in the input formula are not tested. Author(s) Karl W Broman, References Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also addcovarint, fitqtl, makeqtl, scanqtl, refineqtl, addqtl, addpair Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 8, 13) qp <- c(26, 56, 28) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") # try all possible pairwise interactions, one at a time addint(fake.f2, pheno.col=1, qtl, formula=y~Q1+Q2+Q3, method="hk") 16 addloctocross addloctocross Add phenotype location into a cross object Description Add phenotype location(s) into a cross object (with eQTL/pQTL studies) Usage addloctocross(cross, locations=NULL, locfile="locations.txt", verbose=FALSE) Arguments cross An object of class cross. See read.cross for details. locations R variable holding location information locfile load from a file, see the details section for the layout of the file. verbose If TRUE, give verbose output Details inputfile layout: Num Name Chr cM 1 X3.Hydroxypropyl 4 50.0 Num is the number of the phenotype in the cross object Name is the name of the phenotype (will be checked against the name already in the cross object at position num Chr Chromosome cM postion from start of chromosome in cM Value The input cross object, with the locations added as an aditional component locations Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • mqmplot.cistrans - Cis/trans plot • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning addmarker 17 Examples ## Not run: data(multitrait) data(locations) multiloc <- addloctocross(multitrait,locations) results <- scanall(multiloc) mqmplot.cistrans(results, multiloc, 5, FALSE, TRUE) ## End(Not run) addmarker Add a marker to a cross Description Add a marker to a cross object. Usage addmarker(cross, genotypes, markername, chr, pos) Arguments cross An object of class cross. See read.cross for details. genotypes Vector of numeric genotypes. markername Marker name as character string. chr Chromosome ID as character string. pos Position of marker, as numeric value. Details Use this function with caution. It would be best to incorporate new data into a single file to be imported with read.cross. But if you have genotypes on one or two additional markers that you want to add, you might load them with read.csv and incorporate them with this function. Value The input cross object with the single marker added. Author(s) Karl W Broman, See Also pull.markers, drop.markers 18 addpair Examples data(fake.f2) # genotypes for new marker gi <- pull.geno(fill.geno(fake.f2))[,"D5M197"] # add marker to cross fake.f2 <- addmarker(fake.f2, gi, "D5M197imp", "5", 11) addpair Scan for an additional pair of QTL in a multiple-QTL model Description Scan for an additional pair of QTL in the context of a multiple QTL model. Usage addpair(cross, chr, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"), model=c("normal", "binary"), incl.markers=FALSE, verbose=TRUE, tol=1e-4, maxit=1000, forceXcovar=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to be scanned. If missing, all chromosomes are scanned. Refer to chromosomes by name. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl An object of class qtl, as output from makeqtl. covar A matrix or data.frame of covariates. These must be strictly numeric. formula An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as Q1, Q2, etc. Covariates are referred to by their names in the data frame covar. If the new QTL are not included in the formula, a two-dimensional scan as in scantwo is performed. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits incl.markers If FALSE, do calculations only at points on an evenly spaced grid. If calc.genoprob or sim.geno were run with stepwidth="variable" or stepwidth="max", we force incl.markers=TRUE. addpair 19 verbose If TRUE, display information about the progress of calculations. If verbose is an integer > 1, further messages from scanqtl are also displayed. tol Tolerance for convergence for the binary trait model. maxit Maximum number of iterations for fitting the binary trait model. forceXcovar If TRUE, force inclusion of X-chr-related covariates (like sex and cross direction). Details The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included. If neither of the two new QTL are indicated in the formula, we perform a two-dimensional scan as in scantwo. That is, for each pair of QTL positions, we fit two models: two additive QTL added to the formula, and two interacting QTL added to the formula. If the both of the new QTL are indicated in the formula, that particular model is fit, with the positions of the new QTL allowed to vary across the genome. If just one of the QTL is indicated in the formula, a main effect for the other is added, and that particular model is fit, again with the positions of both QTL varying. Note that in this case the LOD scores are not analogous to those produced by scantwo. Thus, there slightly modified forms for the plots (produced by plot.scantwo) and summaries (produced by summary.scantwo and max.scantwo). In the plot, the x-axis is to be interpreted as the position of the first of the new QTL, and the y-axis is to be interpreted as the position of the second of the new QTL. In the summaries, we give the single best pair of positions on each pair of chromosomes, and give LOD scores comparing that pair of positions to the base model (without each of these QTL), and to the base model plus one additional QTL on one or the other of the chromosomes. Value An object of class scantwo, as produced by scantwo. If neither of the new QTL were indicated in the formula, the result is just as in scantwo, though with LOD scores relative to the base model (omitting the new QTL). Otherwise, the results are contained in what would ordinarily be in the full and additive LOD scores, with the additive LOD scores corresponding to the case that the first of the new QTL is to the left of the second of the new QTL, and the full LOD scores corresponding to the case that the first of the new QTL is to the right of the second of the new QTL. Because the structure of the LOD scores in this case is different from those output by scantwo, we include, in this case, an attribute "addpair"=TRUE. (We also require results of single-dimensional scans, omitting each of the two new QTL from the formula, one at a time; these are included as attributes "lod.minus1" and "lod.minus2".) The results are then treated somewhat differently by summary.scantwo, max.scantwo, and plot.scantwo. See the Details section. Author(s) Karl W Broman, References Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. 20 addqtl Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also addint, addqtl, fitqtl, makeqtl, scanqtl, refineqtl, makeqtl, scantwo, addtoqtl Examples # A totally contrived example to show some of what you can do # simulate backcross data with 3 chromosomes (names "17", "18", "19") # one QTL on chr 17 at 40 cM # one QTL on chr 18 at 30 cM # two QTL on chr 19, at 10 and 40 cM data(map10) model <- rbind(c(1,40,0), c(2,30,0), c(3,10,0), c(3,40,0)) ## Not run: fakebc <- sim.cross(map10[17:19], model=model, type="bc", n.ind=250) # het at QTL on 17 and 1st QTL on 19 increases phenotype by 1 unit # het at QTL on 18 and 2nd QTL on 19 decreases phenotype by 1 unit qtlgeno <- fakebc$qtlgeno phe <- rnorm(nind(fakebc)) w <- qtlgeno[,1]==2 & qtlgeno[,3]==2 phe[w] <- phe[w] + 1 w <- qtlgeno[,2]==2 & qtlgeno[,4]==2 phe[w] <- phe[w] - 1 fakebc$pheno[,1] <- phe ## Not run: fakebc <- calc.genoprob(fakebc, step=2, err=0.001) # base model has QTLs on chr 17 and 18 qtl <- makeqtl(fakebc, chr=c("17", "18"), pos=c(40,30), what="prob") # scan for an additional pair of QTL, one interacting with the locus # on 17 and one interacting with the locus on 18 out.ap <- addpair(fakebc, qtl=qtl, formula = y~Q1*Q3 + Q2*Q4, method="hk") max(out.ap) summary(out.ap) plot(out.ap) addqtl Scan for an additional QTL in a multiple-QTL model Description Scan for an additional QTL in the context of a multiple QTL model. addqtl 21 Usage addqtl(cross, chr, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"), model=c("normal", "binary"), incl.markers=TRUE, verbose=FALSE, tol=1e-4, maxit=1000, forceXcovar=FALSE, require.fullrank=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to be scanned. If missing, all chromosomes are scanned. Refer to chromosomes by name. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl An object of class qtl, as output from makeqtl. covar A matrix or data.frame of covariates. These must be strictly numeric. formula An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as Q1, Q2, etc. Covariates are referred to by their names in the data frame covar. If the new QTL is not included in the formula, its main effect is added. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits incl.markers If FALSE, do calculations only at points on an evenly spaced grid. If calc.genoprob or sim.geno were run with stepwidth="variable" or stepwidth="max", we force incl.markers=TRUE. verbose If TRUE, display information about the progress of calculations. If verbose is an integer > 1, further messages from scanqtl are also displayed. tol Tolerance for convergence for the binary trait model. maxit Maximum number of iterations for fitting the binary trait model. forceXcovar If TRUE, force inclusion of X-chr-related covariates (like sex and cross direction). require.fullrank If TRUE, give LOD=0 when covariate matrix in the linear regression is not of full rank. Details The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included. If one wishes to scan for QTL that interact with another QTL, include it in the formula (with an index of one more than the number of QTL in the input qtl object). 22 addtoqtl Value An object of class scanone, as produced by the scanone function. LOD scores are relative to the base model (with any terms that include the new QTL omitted). Author(s) Karl W Broman, References Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also scanone, fitqtl, scanqtl, refineqtl, makeqtl, addtoqtl, addpair, addint Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 8, 13) qp <- c(26, 56, 28) fake.f2 <- subset(fake.f2, chr=c(1,2,3,8,13)) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") # scan for an additional QTL out1 <- addqtl(fake.f2, qtl=qtl, formula=y~Q1+Q2+Q3, method="hk") max(out1) # scan for an additional QTL that interacts with the locus on chr 1 out2 <- addqtl(fake.f2, qtl=qtl, formula=y~Q1*Q4+Q2+Q3, method="hk") max(out2) # plot interaction LOD scores plot(out2-out1) addtoqtl Add to a qtl object Description Add a QTL or multiple QTL to a qtl object. addtoqtl 23 Usage addtoqtl(cross, qtl, chr, pos, qtl.name, drop.lod.profile=TRUE) Arguments cross An object of class cross. See read.cross for details. qtl The qtl object to which additional QTL are to be added. chr Vector indicating the chromosome for each new QTL. (These should be character strings referring to the chromosomes by name.) pos Vector (of same length as chr) indicating the positions on the chromosome for each new QTL. If there is no marker or pseudomarker at a position, the nearest position is used. qtl.name Optional user-specified name for each new QTL, used in the drop-one-term ANOVA table in fitqtl. If unspecified, the names will be of the form "Chr1@10" for a QTL on Chromsome 1 at 10 cM. drop.lod.profile If TRUE, remove any LOD profiles from the object. Value An object of class qtl, just like the input qtl object, but with additional QTL added. See makeqtl for details. Author(s) Karl W Broman, See Also makeqtl, fitqtl, dropfromqtl, replaceqtl, reorderqtl Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 6, 13) qp <- c(25.8, 33.6, 18.63) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") qtl <- addtoqtl(fake.f2, qtl, 14, 35) 24 allchrsplits allchrsplits Test all possible splits of a chromosome into two pieces Description In order to assess the support for a linkage group, this function splits the linkage groups into two pieces at each interval and in each case calculates a LOD score comparing the combined linkage group to the two pieces. Usage allchrsplits(cross, chr, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), m=0, p=0, maxit=4000, tol=1e-6, sex.sp=TRUE, verbose=TRUE) Arguments cross An object of class cross. See read.cross for details. chr A vector specifying which chromosomes to study. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.) m Interference parameter for the chi-square model for interference; a non-negative integer, with m=0 corresponding to no interference. This may be used only for a backcross or intercross. p Proportion of chiasmata from the NI mechanism, in the Stahl model; p=0 gives a pure chi-square model. This may be used only for a backcross or intercross. maxit Maximum number of EM iterations to perform. tol Tolerance for determining convergence. sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. verbose If TRUE, print information on progress. Value A data frame (actually, an object of class "scanone", so that one may use plot.scanone, summary.scanone, etc.) with each row being an interval at which a split is made. The first two columns are the chromosome ID and midpoint of the interval. The third column is a LOD score comparing the combined linkage group to the split into two linkage groups. A fourth column (gap) indicates the length of each interval. The row names indicate the flanking markers for each interval. argmax.geno 25 Author(s) Karl W Broman, See Also est.map, ripple, est.rf, switch.order, movemarker Examples data(fake.bc) allchrsplits(fake.bc, 7, error.prob=0, verbose=FALSE) argmax.geno Reconstruct underlying genotypes Description Uses the Viterbi algorithm to identify the most likely sequence of underlying genotypes, given the observed multipoint marker data, with possible allowance for genotyping errors. Usage argmax.geno(cross, step=0, off.end=0, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), stepwidth=c("fixed", "variable", "max")) Arguments cross An object of class cross. See read.cross for details. step Maximum distance (in cM) between positions at which the genotypes are reconstructed, though for step=0, genotypes are reconstructed only at the marker locations. off.end Distance (in cM) past the terminal markers on each chromosome to which the genotype reconstructions will be carried. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer or Morgan map function when converting genetic distances into recombination fractions. stepwidth Indicates whether the intermediate points should with fixed or variable step sizes. We recommend using "fixed"; "variable" is included for the qtlbim package (http://www.ssg.uab.edu/qtlbim). The "max" option inserts the minimal number of intermediate points so that the maximum distance between points is step. Details We use the Viterbi algorithm to calculate arg maxv Pr(g = v|O) where g is the underlying sequence of genotypes and O is the observed marker genotypes. This is done by calculating γk (vk ) = maxv1 ,...,vk−1 Pr(g1 = v1 , . . . , gk = vk , O1 , . . . , Ok ) for k = 1, . . . , n and then tracing back through the sequence. 26 arithscan Value The input cross object is returned with a component, argmax, added to each component of cross$geno. The argmax component is a matrix of size [n.ind x n.pos], where n.pos is the number of positions at which the reconstructed genotypes were obtained, containing the most likely sequences of underlying genotypes. Attributes "error.prob", "step", and "off.end" are set to the values of the corresponding arguments, for later reference. Warning The Viterbi algorithm can behave badly when step is small but positive. One may observe quite different results for different values of step. The problem is that, in the presence of data like A----H, the sequences AAAAAA and HHHHHH may be more likely than any one of the sequences AAAAAH, AAAAHH, AAAHHH, AAHHHH, AHHHHH, AAAAAH. The Viterbi algorithm produces a single "most likely" sequence of underlying genotypes. Author(s) Karl W Broman, References Lange, K. (1999) Numerical analysis for statisticians. Springer-Verlag. Sec 23.3. Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–286. See Also sim.geno, calc.genoprob, fill.geno Examples data(fake.f2) fake.f2 <- argmax.geno(fake.f2, step=2, off.end=5, err=0.01) arithscan Arithmetic operators for scanone and scantwo results Description Add or subtract LOD scores in results from scanone or scantwo. Usage scan1+scan2 scan1-scan2 Arguments scan1, scan2 Genome scan results on the same set of chromosomes and markers, as output by scanone or scantwo. arithscanperm 27 Details This is used to calculate the sum or difference of LOD scores of two genome scan results. It is particularly useful for calculating the LOD scores for QTL-by-covariate interactions (see the example, below). Note that the degrees of freedom are also added or subtracted. Value The same type of data structure as the input objects, with LOD scores added or subtracted. Author(s) Karl W Broman, Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2.5) # covariates ac <- pull.pheno(fake.bc, c("sex","age")) ic <- pull.pheno(fake.bc, "sex") # scan with additive but not the interactive covariate out.acovar <- scanone(fake.bc, addcovar=ac) # scan with interactive covariate out.icovar <- scanone(fake.bc, addcovar=ac, intcovar=ic) # plot the difference of with and without the interactive covariate # This is a LOD score for a test of QTL x covariate interaction plot(out.icovar-out.acovar) arithscanperm Arithmetic Operators for permutation results Description Add or subtract LOD scores in permutation results from scanone or scantwo. Usage perm1+perm2 perm1-perm2 Arguments perm1, perm2 Permutation results from scanone or scantwo, on the same set of chromosomes and markers. 28 badorder Details This is used to calculate the sum or difference of LOD scores of two sets of permutation results from scanone or scantwo. One must be careful to ensure that the permutations are perfectly linked, which will require the use of set.seed. Value The same data structure as the input objects, with LOD scores added or subtracted. Author(s) Karl W Broman, Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2.5) # covariates ac <- pull.pheno(fake.bc, c("sex","age")) ic <- pull.pheno(fake.bc, "sex") # set seed theseed <- round(runif(1, 1, 10^8)) set.seed(theseed) # permutations with additive but not the interactive covariate ## Not run: operm.acovar <- scanone(fake.bc, addcovar=ac, n.perm=1000) # re-set the seed set.seed(theseed) # permutations with interactive covariate ## Not run: operm.icovar <- scanone(fake.bc, addcovar=ac, intcovar=ic, n.perm=1000) ## End(Not run) # permutation results for the QTL x covariate interaction operm.gxc <- operm.icovar - operm.acovar # LOD thresholds summary(operm.gxc) badorder An intercross with misplaced markers Description Simulated data for an intercross with some markers out of order. bayesint 29 Usage data(badorder) Format An object of class cross. See read.cross for details. Details There are 250 F2 individuals typed at a total of 36 markers on four chromosomes. The data were simulated with QTLs at the center of chromosomes 1 and 3. The order of several markers on chromosome 1 is incorrect. Markers on chromosomes 2 and 3 are switched. Author(s) Karl W Broman, See Also est.rf, ripple, est.map, sim.cross Examples data(badorder) # estimate recombination fractions badorder <- est.rf(badorder) plotRF(badorder) # re-estimate map newmap <- est.map(badorder) plotMap(badorder, newmap) # assess marker order on chr 1 rip3 <- ripple(badorder, chr=1, window=3) summary(rip3) bayesint Bayesian credible interval Description Calculate an approximate Bayesian credible interval for a particular chromosome, using output from scanone. Usage bayesint(results, chr, qtl.index, prob=0.95, lodcolumn=1, expandtomarkers=FALSE) 30 bristle3 Arguments results Output from scanone, or a qtl object as output from refineqtl. chr A chromosome ID (if input results are from scanone (should have length 1). qtl.index Numeric index for a QTL (if input results are from refineqtl (should have length 1). prob Probability coverage of the interval. lodcolumn An integer indicating which of the LOD score columns should be considered (if input results are from scanone). expandtomarkers If TRUE, the interval is expanded to the nearest flanking markers. Details We take 10LOD , rescale it to have area 1, and then calculate the connected interval with density above some threshold and having coverage matching the target probability. Value An object of class scanone indicating the estimated QTL position and the approximate endpoints for the Bayesian credible interval. Author(s) Karl W Broman, See Also scanone, lodint Examples data(hyper) hyper <- calc.genoprob(hyper, step=0.5) out <- scanone(hyper, method="hk") bayesint(out, chr=1) bayesint(out, chr=4) bayesint(out, chr=4, prob=0.99) bayesint(out, chr=4, expandtomarkers=TRUE) bristle3 Data on bristle number in Drosophila Description Data from bristle number in chromosome 3 recombinant isogenic lines of Drosophila melanogaster. Usage data(bristle3) bristleX 31 Format An object of class cross. See read.cross for details. Details There are 66 chromosome 3 recombinant isogenic lines, derived from inbred lines that were selected for low (A) and high (B) abdominal bristle numbers. A recombinant chromosome 3 was placed in an isogenic low background. There are eight phenotypes: the average and SD of the number of abdominal and sternopleural bristles in males and females for each line. Each line is typed at 29 genetic markers on chromosome 3. References Long, A. D., Mullaney, S. L., Reid, L. A., Fry, J. D., Langley, C. H. and MacKay, T. F. C. (1995) High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics 139, 1273–1291. See Also bristleX, listeria, fake.bc, fake.f2, fake.4way, hyper Examples data(bristle3) # Summaries summary(bristle3) plot(bristle3) # genome scan for each of the average phenotypes bristle3 <- calc.genoprob(bristle3, step=2) out <- scanone(bristle3, pheno.col=c(1,3,5,7)) # Plot the results # maximum LOD score among four phenotypes ym <- max(apply(out[,-(1:2)], 2, max)) plot(out, lod=1:3, ylim=c(0,ym)) plot(out, lod=4, add=TRUE, col="green") bristleX Data on bristle number in Drosophila Description Data from bristle number in chromosome X recombinant isogenic lines of Drosophila melanogaster. Usage data(bristleX) Format An object of class cross. See read.cross for details. 32 c.cross Details There are 92 chromosome X recombinant isogenic lines, derived from inbred lines that were selected for low (A) and high (B) abdominal bristle numbers. A recombinant chromosome X was placed in an isogenic low background. There are eight phenotypes: the average and SD of the number of abdominal and sternopleural bristles in males and females for each line. Each line is typed at 17 genetic markers on chromosome 3. References Long, A. D., Mullaney, S. L., Reid, L. A., Fry, J. D., Langley, C. H. and MacKay, T. F. C. (1995) High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics 139, 1273–1291. See Also bristleX, listeria, fake.bc, fake.f2, fake.4way, hyper Examples data(bristleX) # Summaries summary(bristleX) plot(bristleX) # genome scan for each of the average phenotypes bristleX <- calc.genoprob(bristleX, step=2) out <- scanone(bristleX, pheno.col=c(1,3,5,7)) # Plot the results # maximum LOD score among four phenotypes ym <- max(apply(out[,-(1:2)], 2, max)) plot(out, lod=1:3, ylim=c(0,ym)) plot(out, lod=4, add=TRUE, col="green") c.cross Combine data for QTL experiments Description Concatenate the data for multiple QTL experiments. Usage ## S3 method for class 'cross' c(...) c.scanone 33 Arguments ... A set of objects of class cross. See read.cross for details. These must all either be of the same cross type or be a combination of backcrosses and intercrosses. All crosses must have the same number of chromosomes and chromosome names, and the same marker orders and positions, though the set of markers need not be precisely the same. Value The concatenated input, as a cross object. Additional columns are added to the phenotype data indicating which cross an individual comes from; another column indicates cross type (0=BC, 1=intercross), if there are crosses of different types. The crosses are not required to have exactly the same set of phenotypes; phenotypes with the same names are assumed to be the same. If the crosses have different sets of markers, we interpolate marker order, but the cM positions of markers that are in common between crosses must be precisely the same in the different crosses. Author(s) Karl W Broman, See Also subset.cross Examples data(fake.f2) junk <- fake.f2 junk <- c(fake.f2,junk) c.scanone Combine columns from multiple scanone results Description Concatenate the columns from different runs of scanone. Usage ## S3 method for class 'scanone' c(..., labels) ## S3 method for class 'scanone' cbind(..., labels) Arguments ... labels A set of objects of class scanone. (This can also be a list of scanone objects.) These are the results from scanone (with n.perm=0), generally run with different phenotypes or methods. All must conform with each other, meaning that calc.genoprob and/or sim.geno were run with the same values for step and off.end and with data having the same genetic map. A vector of character strings, of length 1 or of the same length as the input, to be appended to the column names in the output. 34 c.scanoneperm Details The aim of this function is to concatenate the results from multiple runs scanone, generally for different phenotypes and/or methods, to be used in parallel with summary.scanone. Value The concatenated input, as a scanone object. Author(s) Karl W Broman, See Also summary.scanone, scanone, cbind.scanoneperm Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2) out.hk <- scanone(fake.f2, method="hk") out.np <- scanone(fake.f2, model="np") out <- c(out.hk, out.np, labels=c("hk","np")) plot(out, lod=1:2, col=c("blue", "red")) c.scanoneperm Combine data from scanone permutations Description Concatenate the data for multiple runs of scanone with n.perm > 0. Usage ## S3 method for class 'scanoneperm' c(...) ## S3 method for class 'scanoneperm' rbind(...) Arguments ... A set of objects of class scanoneperm. (This can also be a list of scanoneperm objects.) These are the permutation results from scanone (that is, when n.perm > 0). These must all have the same number of columns. (That is, they must have been created with the same number of phenotypes, and it is assumed that they were generated in precisely the same way.) Details The aim of this function is to concatenate the results from multiple runs of a permutation test scanone, to assist with the case that such permutations are done on multiple processors in parallel. c.scantwo 35 Value The concatenated input, as a scanoneperm object. Author(s) Karl W Broman, See Also summary.scanoneperm, scanone, cbind.scanoneperm, c.scantwoperm Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2) operm1 <- scanone(fake.f2, method="hk", n.perm=100, perm.Xsp=TRUE) operm2 <- scanone(fake.f2, method="hk", n.perm=50, perm.Xsp=TRUE) operm <- c(operm1, operm2) c.scantwo Combine columns from multiple scantwo results Description Concatenate the columns from different runs of scantwo. Usage ## S3 method for class 'scantwo' c(...) ## S3 method for class 'scantwo' cbind(...) Arguments ... A set of objects of class scantwo. (This can also be a list of scantwo objects.) These are the results from scantwo (with n.perm=0), generally run with different phenotypes or methods. All must conform with each other, meaning that calc.genoprob and/or sim.geno were run with the same values for step and off.end and with data having the same genetic map. Details The aim of this function is to concatenate the results from multiple runs scantwo, generally for different phenotypes and/or methods. Value The concatenated input, as a scantwo object. 36 c.scantwoperm Author(s) Karl W Broman, See Also summary.scantwo, scantwo, c.scanone Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc) out2a <- scantwo(fake.bc, method="hk") out2b <- scantwo(fake.bc, pheno.col=2, method="hk") out2 <- c(out2a, out2b) c.scantwoperm Combine data from scantwo permutations Description Concatenate the data for multiple runs of scantwo with n.perm > 0. Usage ## S3 method for class 'scantwoperm' c(...) ## S3 method for class 'scantwoperm' rbind(...) Arguments ... A set of objects of class scantwoperm. (This can also be a list of scantwoperm objects.) These are the permutation results from scantwo (that is, when n.perm > 0). These must all concern the same number of LOD columns. (That is, they must have been created with the same number of phenotypes, and it is assumed that they were generated in precisely the same way.) Details The aim of this function is to concatenate the results from multiple runs of a permutation test scantwo, to assist with the case that such permutations are done on multiple processors in parallel. Value The concatenated input, as a scantwoperm object. Author(s) Karl W Broman, calc.errorlod 37 See Also summary.scantwoperm, scantwo, cbind.scantwoperm Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2) ## Not run: operm1 <- scantwo(fake.f2, method="hk", n.perm=50) operm2 <- scantwo(fake.f2, method="hk", n.perm=50) ## End(Not run) operm <- c(operm1, operm2) calc.errorlod Identify likely genotyping errors Description Calculates a LOD score for each genotype, measuring the evidence for genotyping errors. Usage calc.errorlod(cross, error.prob=0.01, map.function=c("haldane","kosambi","c-f","morgan"), version=c("new","old")) Arguments cross error.prob map.function version An object of class cross. See read.cross for details. Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype) Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. Specifies whether to use the original version of this function or the current (preferred) version. Details Calculates, for each individual at each marker, a LOD score measuring the strength of evidence for a genotyping error, as described by Lincoln and Lander (1992). In the latest version, evidence for a genotype being in error is considered assuming that all other genotypes (for that individual, on that chromosome) are correct. The argument version allows one to specify whether this new version is used, or whether the original (old) version of the calculation is performed. Note that values below 4 are generally not interesting. Also note that if markers are extremely tightly linked, recombination events can give large error LOD scores. The error LOD scores should not be trusted blindly, but should be viewed as a tool for identifying genotypes deserving further study. Use top.errorlod to print all genotypes with error LOD scores above a specified threshold, plotErrorlod to plot the error LOD scores for specified chromosomes, and plotGeno to view the observed genotype data with likely errors flagged. 38 calc.genoprob Value The input cross object is returned with a component, errorlod, added to each component of cross$geno. The errorlod component is a matrix of size (n.ind x n.mar). An attribute "error.prob" is set to the value of the corresponding argument, for later reference. Author(s) Karl W Broman, References Lincoln, S. E. and Lander, E. S. (1992) Systematic detection of errors in genetic linkage data. Genomics 14, 604–610. See Also plotErrorlod, top.errorlod, cleanGeno Examples data(hyper) hyper <- calc.errorlod(hyper,error.prob=0.01) # print those above a specified cutoff top.errorlod(hyper, cutoff=4) # plot genotype data, flagging genotypes with error LOD > cutoff plotGeno(hyper, chr=1, ind=160:200, cutoff=7, min.sep=2) calc.genoprob Calculate conditional genotype probabilities Description Uses the hidden Markov model technology to calculate the probabilities of the true underlying genotypes given the observed multipoint marker data, with possible allowance for genotyping errors. Usage calc.genoprob(cross, step=0, off.end=0, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), stepwidth=c("fixed", "variable", "max")) Arguments cross An object of class cross. See read.cross for details. step Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations. off.end Distance (in cM) past the terminal markers on each chromosome to which the genotype probability calculations will be carried. calc.genoprob 39 error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi or Carter-Falconer map function when converting genetic distances into recombination fractions. stepwidth Indicates whether the intermediate points should with fixed or variable step sizes. We recommend using "fixed"; "variable" is included for the qtlbim package (http://www.ssg.uab.edu/qtlbim). The "max" option inserts the minimal number of intermediate points so that the maximum distance between points is step. Details Let Ok denote the observed marker genotype at position k, and gk denote the corresponding true underlying genotype. We use the forward-backward equations to calculate αkv = log P r(O1 , . . . , Ok , gk = v) and βkv = log P r(Ok+1 , . . . , On |gk = v) P We then obtain P r(gk |O1 , . . . , On ) = exp(αkv + βkv )/s where s = v exp(αkv + βkv ) In the case of the 4-way cross, with a sex-specific map, we assume a constant ratio of female:male recombination rates within the inter-marker intervals. Value The input cross object is returned with a component, prob, added to each component of cross$geno. prob is an array of size [n.ind x n.pos x n.gen] where n.pos is the number of positions at which the probabilities were calculated and n.gen = 3 for an intercross, = 2 for a backcross, and = 4 for a 4-way cross. Attributes "error.prob", "step", "off.end", and "map.function" are set to the values of the corresponding arguments, for later reference (especially by the function calc.errorlod). Author(s) Karl W Broman, References Lange, K. (1999) Numerical analysis for statisticians. Springer-Verlag. Sec 23.3. Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–286. See Also sim.geno, argmax.geno, calc.errorlod Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=2, off.end=5) data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=0, off.end=0, err=0.01) 40 calc.penalties calc.penalties Calculate LOD penalties Description Derive penalties for the penalized LOD scores (used by stepwiseqtl) on the basis of permutation results from a two-dimensional, two-QTL scan (obtained by scantwo). Usage calc.penalties(perms, alpha=0.05, lodcolumn) Arguments perms Permutation results from scantwo. alpha Significance level. lodcolumn If the scantwo permutation results contain LOD scores for multiple phenotypes, this argument indicates which to use in the summary. This may be a vector. If missing, penalties for all phenotypes are calculated. Details Thresholds derived from scantwo permutations (that is, for a two-dimensional, two-QTL genome scan) are used to calculate penalties on main effects and interactions. The main effect penalty is the 1-alpha quantile of the null distribution of the genome-wide maximum LOD score from a single-QTL genome scan (as with scanone). The "heavy" interaction penalty is the 1-alpha quantile of the null distribution of the maximum interaction LOD score (that is, the log10 likelihood ratio comparing the best model with two interacting QTL to the best model with two additive QTL) from a two-dimensional, two-QTL genome scan (as with scantwo). The "light" interaction penality is the difference between the "fv1" threshold from the scantwo permutations (that is, the 1-alpha quantile of the LOD score comparing the best model with two interacting QTL to the best single-QTL model) and the main effect penalty. If the permutations results were obtained with perm.Xsp=TRUE, to give X-chr-specific results, six penalties are calculated: main effect for autosomes, main effect for X chr, heavy penalty on A:A interactions, light penalty on A:A interactions, penalty on A:X interactions, and penalty on X:X interactions. Value Vector of three values indicating the penalty on main effects and heavy and light penalties on interactions, or a matrix of such results, with each row corresponding to a different phenotype. If the input permutations are X-chromosome-specific, the result has six values: main effect for autosomes, main effect for X chr, heavy penalty on A:A interactions, light penalty on A:A interactions, penalty on A:X interactions, and penalty on X:X interactions. Author(s) Karl W Broman, cbind.scanoneperm 41 References Manichaikul, A., Moon, J. Y., Sen, Ś, Yandell, B. S. and Broman, K. W. (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. Genetics, 181, 1077–1086. See Also scantwo, stepwiseqtl Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=5) out.2dim <- scantwo(fake.f2, method="hk") # permutations ## Not run: permo.2dim <- scantwo(fake.f2, method="hk", n.perm=1000) summary(permo.2dim, alpha=0.05) # penalties calc.penalties(permo.2dim) cbind.scanoneperm Combine columns from multiple scanone permutation results Description Concatenate the columns from different runs of scanone with n.perm > 0. Usage ## S3 method for class 'scanoneperm' cbind(..., labels) Arguments ... A set of objects of class scanoneperm. These are the permutation results from scanone (that is, when n.perm > 0), generally run with different phenotypes or methods. labels A vector of character strings, of length 1 or of the same length as the input ..., to be appended to the column names in the output. Details The aim of this function is to concatenate the results from multiple runs of a permutation test scanone, generally for different phenotypes and/or methods, to be used in parallel with c.scanone. Value The concatenated input, as a scanoneperm object. If different numbers of permutation replicates were used, those columns with fewer replicates are padded with missing values (NA). 42 cbind.scantwoperm Author(s) Karl W Broman, See Also summary.scanoneperm, scanone, c.scanoneperm, c.scanone Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2) operm1 <- scanone(fake.f2, method="hk", n.perm=10, perm.Xsp=TRUE) operm2 <- scanone(fake.f2, method="em", n.perm=5, perm.Xsp=TRUE) operm <- cbind(operm1, operm2, labels=c("hk","em")) summary(operm) cbind.scantwoperm Combine scantwo permutations by column Description Column-bind permutations results from scantwo for multiple phenotypes or models. Usage ## S3 method for class 'scantwoperm' cbind(...) Arguments ... A set of objects of class scantwoperm. (This can also be a list of scantwoperm objects.) These are the permutation results from scantwo (that is, when n.perm > 0). These must all concern the same number of permutations. Value The column-binded input, as a scantwoperm object. Author(s) Karl W Broman, See Also scantwo, c.scantwoperm, summary.scantwoperm checkAlleles 43 Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc) ## Not run: operm1 <- scantwo(fake.bc, pheno.col=1, method="hk", n.perm=50) operm2 <- scantwo(fake.bc, pheno.col=2, method="hk", n.perm=50) ## End(Not run) operm <- cbind(operm1, operm2) checkAlleles Identify markers with switched alleles Description Identify markers whose alleles might have been switched by comparing the LOD score for linkage to all other autosomal markers with the original data to that when the alleles have been switched. Usage checkAlleles(cross, threshold=3, verbose) Arguments cross An object of class cross. See read.cross for details. threshold Only an increase in maximum 2-point LOD of at least this amount will lead to a marker being flagged. verbose If TRUE and there are no markers above the threshold, print a message. Details For each marker, we compare the maximum LOD score for the cases where the estimated recombination fraction > 0.5 to those where r.f. < 0.5. The function est.rf must first be run. Note: Markers that are tightly linked to a marker whose alleles are switched are likely to also be flagged by this method. The real problem markers are likely those with the biggest difference in LOD scores. Value A data frame containing the flagged markers, having four columns: the marker name, chromosome ID, numeric index within chromosome, and the difference between the maximum two-point LOD score with the alleles switched to that from the original data. Author(s) Karl W Broman, See Also est.rf, geno.crosstab, switchAlleles 44 chrlen Examples data(fake.f2) # switch homozygotes at marker D5M391 fake.f2 <- switchAlleles(fake.f2, "D5M391") fake.f2 <- est.rf(fake.f2) checkAlleles(fake.f2) chrlen Chromosome lengths in QTL experiment Description Obtain the chromosome lengths in a cross or map object. Usage chrlen(object) Arguments object An object of class map or of class cross. Value Returns a vector of chromosome lengths. If the cross has sex-specific maps, it returns a 2-row matrix with the two lengths for each chromosome. Author(s) Karl W Broman, See Also summaryMap, pull.map, summary.cross Examples data(fake.f2) chrlen(fake.f2) map <- pull.map(fake.f2) chrlen(map) chrnames 45 chrnames Pull out the chromosome names from a cross Description Pull out the chromosome names from a cross object as one big vector. Usage chrnames(cross) Arguments cross An object of class cross. See read.cross for details. Value A vector of character strings (the chromosome names). Author(s) Karl W Broman, See Also markernames, phenames Examples data(listeria) chrnames(listeria) cim Composite interval mapping Description Composite interval mapping by a scheme from QTL Cartographer: forward selection at the markers (here, with filled-in genotype data) to a fixed number, followed by interval mapping with the selected markers as covariates, dropping marker covariates if they are within some fixed window size of the location under test. Usage cim(cross, pheno.col=1, n.marcovar=3, window=10, method=c("em", "imp", "hk", "ehk"), imp.method=c("imp", "argmax"), error.prob=0.0001, map.function=c("haldane", "kosambi", "c-v", "morgan"), n.perm) 46 cim Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either nonintegers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. n.marcovar Number of marker covariates to use. window Window size, in cM. method Indicates whether to use the EM algorithm, imputation, Haley-Knott regression, or the extended Haley-Knott method. imp.method Method used to impute any missing marker genotype data. error.prob Genotyping error probability assumed when imputing the missing marker genotype data. map.function Map function used when imputing the missing marker genotype data. n.perm If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates. Details We first use fill.geno to impute any missing marker genotype data, either via a simple random imputation or using the Viterbi algorithm. We then perform forward selection to a fixed number of markers. These will be used (again, with any missing data filled in) as covariates in the subsequent genome scan. Value The function returns an object of the same form as the function scanone: If n.perm is missing, the function returns the scan results as a data.frame with three columns: chromosome, position, LOD score. Attributes indicate the names and positions of the chosen marker covariates. If n.perm > 0, the function results the results of a permutation test: a vector giving the genome-wide maximum LOD score in each of the permutations. Author(s) Karl W Broman, References Jansen, R. C. (1993) Interval mapping of multiple quantitative trait loci. Genetics, 135, 205–211. Jansen, R. C. and Stam, P. (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics, 136, 1447-1455. Zeng, Z. B. (1993) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc. Natl. Acad. Sci. USA, 90, 10972–10976. Zeng, Z. B. (1994) Precision mapping of quantitative trait loci. Genetics, 136, 1457–1468. clean.cross 47 See Also add.cim.covar, scanone, summary.scanone, plot.scanone, fill.geno Examples data(hyper) hyper <- calc.genoprob(hyper, step=2.5) out <- scanone(hyper) out.cim <- cim(hyper, n.marcovar=3) plot(out, out.cim, chr=c(1,4,6,15), col=c("blue", "red")) add.cim.covar(out.cim, chr=c(1,4,6,15)) clean.cross Remove derived data Description Remove any intermediate calculations from a cross object. Usage ## S3 method for class 'cross' clean(object, ...) Arguments object An object of class cross. See read.cross for details. ... Ignored at this point. Value The input object, with any intermediate calculations (such as is produced by calc.genoprob, argmax.geno and sim.geno) removed. Author(s) Karl W Broman, See Also drop.nullmarkers, drop.markers, clean.scantwo Examples data(fake.f2) names(fake.f2$geno) fake.f2 <- calc.genoprob(fake.f2) names(fake.f2$geno) fake.f2 <- clean(fake.f2) names(fake.f2$geno) 48 clean.scantwo clean.scantwo Clean up scantwo output Description In an object output from scantwo, replaces negative and missing LOD scores with 0, and replaces LOD scores for pairs of positions that are not separated by n.mar markers, or that are less than distance cM apart, with 0. Further, if the LOD for full model is less than the LOD for the additive model, the additive LOD is pasted over the full LOD. Usage ## S3 method for class 'scantwo' clean(object, n.mar=1, distance=0, ...) Arguments object An object of class scantwo. See scantwo for details. n.mar Pairs of positions not separated by at least this many markers have LOD scores set to 0. distance Pairs of positions not separated by at least this distance have LOD scores set to 0. ... Ignored at this point. Value The input scantwo object, with any negative or missing LOD scores replaced by 0, and LOD scores for pairs of positions separated by fewer than n.mar markers, or less than distance cM, are set to 0. Also, if the LOD for the full model is less than the LOD for the additive model, the additive LOD is used in place of the full LOD. Author(s) Karl W Broman, See Also scantwo, summary.scantwo Examples data(fake.f2) fake.f2 out2 See Also locateXO, countXO, calc.errorlod Examples data(hyper) sum(ntyped(hyper)) hyperc <- cleanGeno(hyper, chr=4, maxdist=c(2.5, 10), maxmark=c(2, 1)) sum(ntyped(hyperc)) 50 comparegeno comparecrosses Compare two cross objects Description Verify that two objects of class cross have identical classes, chromosomes, markers, genotypes, genetic maps, and phenotypes. Usage comparecrosses(cross1, cross2, tol=1e-5) Arguments cross1 An object of class cross (must be an intercross). See read.cross for details. cross2 An object of class cross (must be an intercross). See read.cross for details. tol Tolerance value for comparing genetic map positions and numeric phenotypes. Value None. Author(s) Karl W Broman, See Also summary.cross Examples data(listeria) comparecrosses(listeria, listeria) comparegeno Compare individuals’ genotype data Description Count proportion of matching genotypes between all pairs of individuals, to look for unusually closely related individuals. Usage comparegeno(cross, what=c("proportion","number","both")) compareorder 51 Arguments cross An object of class cross. See read.cross for details. what Indicates whether to return the proportion or number of matching genotypes (or both). Value A matrix whose (i,j)th element is the proportion or number of matching genotypes for individuals i and j. If called with what="both", the lower triangle contains the proportion and the upper triangle contains the number. If called with what="proportion", the diagonal contains missing values. Otherwise, the diagonal contains the number of typed markers for each individual. Author(s) Karl W Broman, See Also nmissing Examples data(listeria) output <- comparegeno(listeria) # image of the proportions n.ind <- nind(listeria) image(1:n.ind, 1:n.ind, output, col=gray((0:99)/99), breaks=seq(0,1,len=101)) # histogram hist(output, breaks=150, prob=TRUE, xlab="Proportion of matching genotypes") rug(output) compareorder Compare two orderings of markers on a chromosome Description Compare the likelihood of an alternative order for markers on a chromosome to the current order. Usage compareorder(cross, chr, order, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), maxit=4000, tol=1e-6, sex.sp=TRUE) 52 condense.scantwo Arguments cross An object of class cross. See read.cross for details. chr The chromosome to investigate. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.) order The alternate order of markers on the chromosome: a numeric vector that is a permutation of the integers from 1 to the number of markers on the chromosome. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. maxit Maximum number of EM iterations to perform. tol Tolerance for determining convergence. sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. Value A data frame with two rows: the current order in the input cross object, and the revised order. The first column is the log10 likelihood of the new order relative to the original one (positive values indicate that the new order is better supported). The second column is the estimated genetic length of the chromosome for each order. In the case of sex-specific maps, there are separate columns for the female and male genetic lengths. Author(s) Karl W Broman, See Also ripple, switch.order, movemarker Examples data(badorder) compareorder(badorder, chr=1, order=c(1:8,11,10,9,12)) condense.scantwo Condense the output from a 2-d genome scan Description Produces a very condensed version of the output of scantwo. Usage ## S3 method for class 'scantwo' condense(object) convert.map 53 Arguments object An object of class scantwo, the output of the function scantwo. Details This produces a very reduced version of the output of scantwo, for which a summary may still be created via summary.scantwo, though plots can no longer be made. Value An object of class scantwocondensed, containing just the maximum full, additive and interactive LOD scores, and the positions where they occured, on each pair of chromosomes. Author(s) Karl W Broman, See Also scantwo, summary.scantwo, max.scantwo Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2) out2 <- scantwo(fake.f2, method="hk") out2c <- condense(out2) summary(out2c, allpairs=FALSE) max(out2c) convert.map Change map function for a genetic map Description Convert a genetic map from using one map function to another. Usage ## S3 method for class 'map' convert(object, old.map.function=c("haldane", "kosambi", "c-f", "morgan"), new.map.function=c("haldane", "kosambi", "c-f", "morgan"), ...) 54 convert.scanone Arguments object A genetic map object, of class "map": A list whose components are vectors of marker locations. old.map.function The map function used in forming the map in object. new.map.function The new map function to be used. ... Ignored at this point. Details The location of the first marker on each chromosome is left unchanged. Inter-marker distances are converted to recombination fractions with the inverse of the old.map.function, and then back to distances with the new.map.function. Value The same as the input, but with inter-marker distances changed to reflect a different map function. Author(s) Karl W Broman, See Also est.map, replace.map Examples data(listeria) map <- pull.map(listeria) map <- convert(map, "haldane", "kosambi") listeria <- replace.map(listeria, map) convert.scanone Convert output from scanone for R/qtl version 0.98 Description Convert the output from scanone from the format used in R/qtl version 0.97 and earlier to that used in version 0.98 and later. Usage ## S3 method for class 'scanone' convert(object, ...) Arguments object Output from the function scanone, for R/qtl version 0.97 and earlier. ... Ignored at this point. convert.scantwo 55 Details Previously, inter-marker locations were named as, for example, loc7.5.c3; these were changed to c3.loc7.5. Value The same scanone output, but revised for use with R/qtl version 0.98 and later. Author(s) Karl W Broman, See Also scanone, convert.scantwo Examples ## Not run: out.new <- convert(out.old) convert.scantwo Convert output from scantwo for R/qtl version 1.03 and earlier Description Convert the output from scantwo from the format used in R/qtl version 1.03 and earlier to that used in version 1.04 and later. Usage ## S3 method for class 'scantwo' convert(object, ...) Arguments object Output from the function scantwo, for R/qtl version 1.03 and earlier. ... Ignored at this point. Details Previously, the output from scantwo contained the full and interaction LOD scores. In R/qtl version 1.04 and later, the output contains the LOD scores from the full and additive QTL models. Value The same scanone output, but revised for use with R/qtl version 1.03 and later. Author(s) Karl W Broman, 56 convert2riself See Also scantwo, convert.scanone Examples ## Not run: out2.new <- convert(out2.old) convert2riself Convert a cross to RIL by selfing Description Convert a cross to type "riself" (RIL by selfing). Usage convert2riself(cross) Arguments cross An object of class cross. See read.cross for details. Details If there are more genotypes with code 3 (BB) than code 2 (AB), we omit the genotypes with code==2 and call those with code==3 the BB genotypes. If, instead, there are more genotypes with code 2 than code 3, we omit the genotypes with code==3 and call those with code==2 the BB genotypes. Any chromosomes with class "X" (X chromosome) are changed to class "A" (autosomal). Value The input cross object, with genotype codes possibly changed and cross type changed to "riself". Author(s) Karl W Broman, See Also convert2risib Examples data(hyper) hyper.as.riself <- convert2riself(hyper) convert2risib 57 convert2risib Convert a cross to RIL by sib mating Description Convert a cross to type "risib" (RIL by sib mating). Usage convert2risib(cross) Arguments cross An object of class cross. See read.cross for details. Details If there are more genotypes with code 3 (BB) than code 2 (AB), we omit the genotypes with code==2 and call those with code==3 the BB genotypes. If, instead, there are more genotypes with code 2 than code 3, we omit the genotypes with code==3 and call those with code==2 the BB genotypes. Value The input cross object, with genotype codes possibly changed and cross type changed to "risib". Author(s) Karl W Broman, See Also convert2riself Examples data(hyper) hyper.as.risib <- convert2risib(hyper) 58 convert2sa convert2sa Convert a sex-specific map to a sex-averaged one Description Convert a sex-specific map to a sex-averaged one, assuming that the female and male maps are actually the same (that is, that the map was estimated assuming a common recombination rate in females and males). Usage convert2sa(map, tol=1e-4) Arguments map A map object with sex-specific locations (but assuming that the female and male maps are the same), as output by the function est.map for a 4-way cross, with argument sex.sp=FALSE. tol Tolerance value for inspecting the differences between the female and male maps; if they differ by more than this tolerance, a warning is issued. Details We pull out just the female marker locations, and give a warning if there are large differences between the female and male maps. Value A map object, with sex-averaged distances. Author(s) Karl W Broman, See Also est.map, plotMap Examples data(fake.4way) ## Not run: fake.4way <- subset(fake.4way, chr="-X") nm <- est.map(fake.4way, sex.sp=FALSE) plot(convert2sa(nm)) countXO 59 countXO Count number of obligate crossovers for each individual Description Count the number of obligate crossovers for each individual in a cross, either by chromosome or overall. Usage countXO(cross, chr, bychr=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to investigate. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. bychr If TRUE, return counts for each individual chromosome; if FALSE, return the overall number across the selected chromosomes. Details For each individual we count the minimal number of crossovers that explain the observed genotype data. Value If bychr=TRUE, a matrix of counts is returned, with rows corresponding to individuals and columns corresponding to chromosomes. If bychr=FALSE, a vector of counts (the total number of crossovers across all selected chromosomes) is returned. Author(s) Karl W Broman, See Also ripple, locateXO, cleanGeno Examples data(hyper) plot(countXO(hyper)) 60 drop.markers drop.dupmarkers Drop duplicate markers Description Drop markers with duplicate names; retaining the first of each set, with consensus genotyps Usage drop.dupmarkers(cross, verbose=TRUE) Arguments cross An object of class cross. See read.cross for details. verbose If TRUE, print information on the numbers of genotypes and markers omitted. If > 1, give more detailed information on genotypes omitted. Value The input cross object, with any duplicate markers omitted (except for one). The marker retained will have consensus genotypes; if multiple versions of a marker have different genotypes for an individual, they will be replaced by NA. Any derived data (such as produced by calc.genoprob) will be stripped off. Author(s) Karl W Broman, See Also drop.nullmarkers, pull.markers, drop.markers, summary.cross, clean.cross Examples data(listeria) listeria <- drop.dupmarkers(listeria) drop.markers Drop a set of markers Description Drop a vector of markers from the data matrices and genetic maps. Usage drop.markers(cross, markers) drop.nullmarkers 61 Arguments cross An object of class cross. See read.cross for details. markers A character vector of marker names. Value The input object, with any markers in the vector markers removed from the genotype data matrices, genetic maps, and, if applicable, any derived data (such as produced by calc.genoprob). (It might be a good idea to re-derive such things after using this function.) Author(s) Karl W Broman, See Also drop.nullmarkers, pull.markers, geno.table, clean.cross Examples data(listeria) listeria2 <- drop.markers(listeria, c("D10M44","D1M3","D1M75")) drop.nullmarkers Drop markers without any genotype data Description Drop markers, from the data matrices and genetic maps, that have no genotype data. Usage drop.nullmarkers(cross) Arguments cross An object of class cross. See read.cross for details. Value The input object, with any markers lacking genotype data removed from the genotype data matrices, genetic maps, and, if applicable, any derived data (such as produced by calc.genoprob). (It might be a good idea to re-derive such things after using this function.) Author(s) Karl W Broman, See Also nullmarkers, drop.markers, clean.cross, geno.table 62 dropfromqtl Examples # removes one marker from hyper data(hyper) hyper <- drop.nullmarkers(hyper) # shouldn't do anything to listeria data(listeria) listeria <- drop.nullmarkers(listeria) dropfromqtl Drop a QTL from a qtl object Description Drop a QTL or multiple QTL from a QTL object Usage dropfromqtl(qtl, index, chr, pos, qtl.name, drop.lod.profile=TRUE) Arguments qtl A qtl object, as created by makeqtl. index Vector specifying the numeric indices of the QTL to be dropped. chr Vector indicating the chromosome for each QTL to drop. pos Vector (of same length as chr) indicating the positions of the QTL to be dropped. qtl.name Vector specifying the names of the QTL to be dropped. drop.lod.profile If TRUE, remove any LOD profiles from the object. Details Provide either chr and pos, or one of qtl.name or index. Value The input qtl object with the specified QTL omitted. See makeqtl for details on the format. Author(s) Karl W Broman, kbroman@biostat.wisc.edu See Also makeqtl, fitqtl, addtoqtl, replaceqtl , reorderqtl droponemarker 63 Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 6, 13) qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") newqtl <- dropfromqtl(qtl, chr=1, pos=25.8) altqtl <- dropfromqtl(qtl, index=1) droponemarker Drop one marker at a time and determine effect on genetic map Description Drop one marker at a time from a genetic map and calculate the change in log likelihood and in the chromosome length, in order to identify problematic markers. Usage droponemarker(cross, chr, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), m=0, p=0, maxit=4000, tol=1e-6, sex.sp=TRUE, verbose=TRUE) Arguments cross An object of class cross. See read.cross for details. chr A vector specifying which chromosomes to test for the position of the marker. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.) m Interference parameter for the chi-square model for interference; a non-negative integer, with m=0 corresponding to no interference. This may be used only for a backcross or intercross. p Proportion of chiasmata from the NI mechanism, in the Stahl model; p=0 gives a pure chi-square model. This may be used only for a backcross or intercross. maxit Maximum number of EM iterations to perform. tol Tolerance for determining convergence. 64 effectplot sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. verbose If TRUE, print information on progress; if > 1, print even more information. Value A data frame (actually, an object of class "scanone", so that one may use plot.scanone, summary.scanone, etc.) with each row being a marker. The first two columns are the chromosome ID and position. The third column is a LOD score comparing the hypothesis that the marker is not linked to the hypothesis that it belongs at that position. In the case of a 4-way cross, with sex.sp=TRUE, there are two additional columns with the change in the estimated female and male genetic lengths of the respective chromosome, upon deleting that marker. With sex.sp=FALSE, or for other types of crosses, there is one additional column, with the change in estimated genetic length of the respective chromosome, when the marker is omitted. A well behaved marker will have a negative LOD score and a small change in estimated genetic length. A poorly behaved marker will have a large positive LOD score and a large change in estimated genetic length. But note that dropping the first or last marker on a chromosome could result in a large change in estimated length, even if they are not badly behaved; for these markers one should focus on the LOD scores, with a large positive LOD score being bad. Author(s) Karl W Broman, See Also tryallpositions, est.map, ripple, est.rf, switch.order, movemarker, drop.markers Examples data(fake.bc) droponemarker(fake.bc, 7, error.prob=0, verbose=FALSE) effectplot Plot phenotype means against genotypes at one or two markers Description Plot the phenotype means for each group defined by the genotypes at one or two markers (or the values at a discrete covariate). Usage effectplot(cross, pheno.col=1, mname1, mark1, geno1, mname2, mark2, geno2, main, ylim, xlab, ylab, col, add.legend=TRUE, legend.lab, draw=TRUE, var.flag=c("pooled","group")) effectplot 65 Arguments cross An object of class cross. pheno.col Column number in the phenotype matrix to be drawn in the plot. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. mname1 Name for the first marker or pseudomarker. Pseudomarkers (that is, non-marker positions on the imputation grid) may be referred to in a form like "5@30.3", for position 30.3 on chromosome 5. mark1 Genotype data for the first marker. If unspecified, genotypes will be taken from the data in the input cross object, using the name specified in mname1. geno1 Optional labels for the genotypes (or classes in a covariate). mname2 Name for the second marker or pseudomarker (optional). mark2 Like mark1 (optional). geno2 Optional labels for the genotypes (or classes in a covariate). main Optional figure title. ylim Optional y-axis limits. xlab Optional x-axis label. ylab Optional y-axis label. col Optional vector of colors for the different line segments. add.legend A logical value to indicate whether to add a legend. legend.lab Optional title for the legend. draw A logical value to indicate generate the plot or not. If FALSE, no figure will be plotted and this function can be used to calculate the group means and standard errors. var.flag The method to calculate the group variance. "pooled" means to use the pooled variance and "group" means to calculate from individual group. Details In the plot, the y-axis is the phenotype. In the case of one marker, the x-axis is the genotype for that marker. In the case of two markers, the x-axis is for different genotypes of the second marker, and the genotypes of first marker are represented by lines in different colors. Error bars are plotted at ± 1 SE. The results of sim.geno are used; if they are not available, sim.geno is run with n.draws=16. The average phenotype for each genotype group takes account of missing genotype data by averaging across the imputations. The SEs take account of both the residual phenotype variation and the imputation error. Value A data.frame containing the phenotype means and standard errors for each group. Author(s) Hao Wu; Karl W Broman, 66 effectplot See Also plotPXG, find.marker, effectscan, find.pseudomarker Examples data(fake.f2) # impute genotype data ## Not run: fake.f2 <- sim.geno(fake.f2, step=5, n.draws=64) ######################################## # one marker plots ######################################## ### plot of genotype-specific phenotype means for 1 marker mname <- find.marker(fake.f2, 1, 37) # marker D1M437 effectplot(fake.f2, pheno.col=1, mname1=mname) ### output of the function contains the means and SEs output <- effectplot(fake.f2, mname1=mname) output ### plot a phenotype # Plot of sex-specific phenotype means, # note that "sex" must be a phenotype name here effectplot(fake.f2, mname1="sex", geno1=c("F","M")) # alternatively: sex <- pull.pheno(fake.f2, "sex") effectplot(fake.f2, mname1="Sex", mark1=sex, geno1=c("F","M")) ######################################## # two markers plots ######################################## ### plot two markers # plot of genotype-specific phenotype means for 2 markers mname1 <- find.marker(fake.f2, 1, 37) # marker D1M437 mname2 <- find.marker(fake.f2, 13, 24) # marker D13M254 effectplot(fake.f2, mname1=mname1, mname2=mname2) ### plot two pseudomarkers ##### refer to pseudomarkers by their positions effectplot(fake.f2, mname1="1@35", mname2="13@25") ##### alternatively, find their names via find.pseudomarker pmnames <- find.pseudomarker(fake.f2, chr=c(1, 13), c(35, 25)) effectplot(fake.f2, mname1=pmnames[1], mname2=pmnames[2]) ### Plot of sex- and genotype-specific phenotype means mname <- find.marker(fake.f2, 13, 24) # marker D13M254 # sex and a marker effectplot(fake.f2, mname1=mname, mname2="Sex", mark2=sex, geno2=c("F","M")) # Same as above, switch role of sex and the marker effectscan 67 # sex and marker effectplot(fake.f2, mname1="Sex", mark1=sex, geno1=c("F","M"), mname2=mname) # X chromosome marker mname <- find.marker(fake.f2, "X", 14) # marker DXM66 effectplot(fake.f2, mname1=mname) # Two markers, including one on the X mnames <- find.marker(fake.f2, c(13, "X"), c(24, 14)) effectplot(fake.f2, mname1=mnames[1], mname2=mnames[2]) effectscan Plot estimated QTL effects across the whole genome Description This function is used to plot the estimated QTL effects along selected chromosomes. For a backcross, there will be only one line, representing the additive effect. For an intercross, there will be two lines, representing the additive and dominance effects. Usage effectscan(cross, pheno.col=1, chr, get.se=FALSE, draw=TRUE, gap=25, ylim, mtick=c("line","triangle"), add.legend=TRUE, alternate.chrid=FALSE, ...) Arguments cross An object of class cross. pheno.col Column number in the phenotype matrix which to be drawn in the plot. One may also give a character string matching a phenotype name. chr Optional vector indicating the chromosomes to be drawn in the plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. get.se If TRUE, estimated standard errors are calculated. draw If TRUE, draw the figure. gap Gap separating chromosomes (in cM). ylim Y-axis limits (optional). mtick Tick mark type for markers. add.legend If TRUE, add a legend. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Passed to the function plot when it is called. 68 effectscan Details The results of sim.geno are required for taking account of missing genotype information. For a backcross, the additive effect is estimated as the difference between the phenotypic averages for heterozygotes and homozygotes. For recombinant inbred lines, the additive effect is estimated as half the difference between the phenotypic averages for the two homozygotes. For an intercross, the additive and dominance effects are estimated from linear regression on a and d with a = -1, 0, 1, for the AA, AB and BB genotypes, respectively, and d = 0, 1, 0, for the AA, AB and BB genotypes, respectively. As usual, the X chromosome is a bit more complicated. We estimate separate additive effects for the two sexes, and for the two directions within females. There is an internal function plot.effectscan that creates the actual plot by calling plot.scanone. In the case get.se=TRUE, colored regions indicate ± 1 SE. Value The results are returned silently, as an object of class "effectscan", which is the same as the form returned by the function scanone, though with estimated effects where LOD scores might be. That is, it is a data frame with the first two columns being chromosome ID and position (in cM), and subsequent columns being estimated effects, and (if get.se=TRUE) standard errors. Author(s) Karl W. Broman, References Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also effectplot, plotPXG, sim.geno Examples data(fake.f2) fake.f2 <- sim.geno(fake.f2, step=2.5, n.draws=16) # allelic effect on whole genome effectscan(fake.f2) # on chromosome 13, include standard errors effectscan(fake.f2, chr="13", mtick="triangle", get.se=TRUE) est.map est.map 69 Estimate genetic maps Description Uses the Lander-Green algorithm (i.e., the hidden Markov model technology) to re-estimate the genetic map for an experimental cross. Usage est.map(cross, chr, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), m=0, p=0, maxit=10000, tol=1e-6, sex.sp=TRUE, verbose=FALSE, omit.noninformative=TRUE, offset, n.cluster=1) Arguments cross chr An object of class cross. See read.cross for details. Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.) m Interference parameter for the chi-square model for interference; a non-negative integer, with m=0 corresponding to no interference. This may be used only for a backcross or intercross. p Proportion of chiasmata from the NI mechanism, in the Stahl model; p=0 gives a pure chi-square model. This may be used only for a backcross or intercross. maxit Maximum number of EM iterations to perform. tol Tolerance for determining convergence. sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. verbose If TRUE, print tracing information. omit.noninformative If TRUE, on each chromosome, omit individuals with fewer than two typed markers, since they are not informative for linkage. offset Defines the starting position for each chromosome. If missing, we use the starting positions that are currently present in the input cross object. This should be a single value (to be used for all chromosomes) or a vector with length equal to the number of chromosomes, defining individual starting positions for each chromosome. For a sex-specific map (as in a 4-way cross), we use the same offset for both the male and female maps. n.cluster If the package snow is available calculations for multiple chromosomes are run in parallel using this number of nodes. 70 est.map Details By default, the map is estimated assuming no crossover interference, but a map function is used to derive the genetic distances (though, by default, the Haldane map function is used). For a backcross or intercross, inter-marker distances may be estimated using the Stahl model for crossover interference, of which the chi-square model is a special case. In the chi-square model, points are tossed down onto the four-strand bundle according to a Poisson process, and every (m + 1)st point is a chiasma. With the assumption of no chromatid interference, crossover locations on a random meiotic product are obtained by thinning the chiasma process. The parameter m (a non-negative integer) governs the strength of crossover interference, with m = 0 corresponding to no interference. In the Stahl model, chiasmata on the four-strand bundle are a superposition of chiasmata from two mechanisms, one following a chi-square model and one exhibiting no interference. An additional parameter, p, gives the proportion of chiasmata from the no interference mechanism. Value A map object; a list whose components (corresponding to chromosomes) are either vectors of marker positions (in cM) or matrices with two rows of sex-specific marker positions. The maximized log likelihood for each chromosome is saved as an attribute named loglik. In the case that estimation was under an interference model (with m > 0), allowed only for a backcross, m and p are also included as attributes. Author(s) Karl W Broman, References Armstrong, N. J., McPeek, M. J. and Speed, T. P. (2006) Incorporating interference into linkage analysis for experimental crosses. Biostatistics 7, 374–386. Lander, E. S. and Green, P. (1987) Construction of multilocus genetic linkage maps in humans. Proc. Natl. Acad. Sci. USA 84, 2363–2367. Lange, K. (1999) Numerical analysis for statisticians. Springer-Verlag. Sec 23.3. Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–286. Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. Genetics 139, 1045–1056. See Also map2table, plotMap, replace.map, est.rf, fitstahl Examples data(fake.f2) newmap <- est.map(fake.f2) logliks <- sapply(newmap, attr, "loglik") plotMap(fake.f2, newmap) fake.f2 <- replace.map(fake.f2, newmap) est.rf est.rf 71 Estimate pairwise recombination fractions Description Estimate the sex-averaged recombination fraction between all pairs of genetic markers. Usage est.rf(cross, maxit=10000, tol=1e-6) Arguments cross An object of class cross. See read.cross for details. maxit Maximum number of iterations for the EM algorithm (not used with backcrosses). tol Tolerance for determining convergence (not used with backcrosses). Details For a backcross, one can simply count recombination events. For an intercross or 4-way cross, a version of the EM algorithm must be used to estimate recombination fractions. (Since, for example, in an intercross individual that is heterozygous at two loci, it is not known whether there were 0 or 2 recombination events.) Note that, for the 4-way cross, we estimate sex-averaged recombination fractions. Value The input cross object is returned with a component, rf, added. This is a matrix of size (tot.mar x tot.mar). The diagonal contains the number of typed meioses per marker, the lower triangle contains the estimated recombination fractions, and the upper triangle contains the LOD scores (testing rf = 0.5). Author(s) Karl W Broman, See Also plotRF, pull.rf, plot.rfmatrix, est.map, badorder, checkAlleles Examples data(badorder) badorder <- est.rf(badorder) plotRF(badorder) 72 fake.4way fake.4way Simulated data for a 4-way cross Description Simulated data for a phase-known 4-way cross, obtained using sim.cross. Usage data(fake.4way) Format An object of class cross. See read.cross for details. Details There are 250 individuals typed at 157 markers, including 8 on the X chromosome. There are two phenotypes (including sex, for which 0=female and 1=male). The quantitative phenotype is affected by three QTLs: two on chromosome 2 at positions 10 and 25 cM on the female genetic map, and one on chromosome 7 at position 40 cM on the female map. Author(s) Karl W Broman, See Also sim.cross, fake.bc, fake.f2, listeria, hyper, bristle3, bristleX Examples data(fake.4way) plot(fake.4way) summary(fake.4way) # estimate recombination fractions fake.4way <- est.rf(fake.4way) plotRF(fake.4way) # estimate genetic maps ssmap <- est.map(fake.4way, verbose=TRUE) samap <- est.map(fake.4way, sex.sp=FALSE, verbose=TRUE) plot(ssmap, samap) # error lod scores fake.4way <- calc.genoprob(fake.4way, err=0.01) fake.4way <- calc.errorlod(fake.4way, err=0.01) top.errorlod(fake.4way, cutoff=2.5) # genome scan fake.4way <- calc.genoprob(fake.4way, step=2.5) fake.bc 73 out.hk <- scanone(fake.4way, method="hk") out.em <- scanone(fake.4way, method="em") plot(out.em,out.hk,chr=c(2,7)) fake.bc Simulated data for a backcross Description Simulated data for a backcross, obtained using sim.cross. Usage data(fake.bc) Format An object of class cross. See read.cross for details. Details There are 400 backcross individuals typed at 91 markers and with two phenotypes and two covariates (sex and age). The two phenotypes are due to four QTLs, with no epistasis. There is one on chromosome 2 (at 30 cM), two on chromosome 5 (at 10 and 50 cM), and one on chromosome 10 (at 30 cM). The QTL on chromosome 2 has an effect only in the males (sex=1); the two QTLs on chromosome 5 have effect in coupling for the first phenotype and in repulsion for the second phenotype. Age has an effect of increasing the phenotypes. Author(s) Karl W Broman, See Also sim.cross, fake.4way, fake.f2, listeria, hyper, bristle3, bristleX Examples data(fake.bc) summary(fake.bc) plot(fake.bc) # genome scans without covariates fake.bc <- calc.genoprob(fake.bc, step=2.5) out.nocovar <- scanone(fake.bc, pheno.col=1:2) # genome scans with covariates ac <- pull.pheno(fake.bc, c("sex","age")) ic <- pull.pheno(fake.bc, "sex") out.covar <- scanone(fake.bc, pheno.col=1:2, addcovar=ac, intcovar=ic) 74 fake.f2 # summaries summary(out.nocovar, thr=3, format="allpeaks") summary(out.covar, thr=3, format="allpeaks") # plots plot(out.nocovar, out.covar, chr=c(2,5,10), lod=1, col="blue", lty=1:2, ylim=c(0,13)) plot(out.nocovar, out.covar, chr=c(2,5,10), lod=2, col="red", lty=1:2, add=TRUE) fake.f2 Simulated data for an F2 intercross Description Simulated data for an F2 intercross, obtained using sim.cross. Usage data(fake.f2) Format An object of class cross. See read.cross for details. Details There are 200 F2 individuals typed at 94 markers, including 3 on the X chromosome. There is one quantitative phenotype, along with an indication of sex (0=female, 1=male) and the direction of the cross (pgm = paternal grandmother, 0=A, meaning the cross was (AxB)x(AxB), and 1=B, meaning the cross was (AxB)x(BxA)). Note that the X chromosome genotypes are coded in a special way (see read.cross). For the individuals with pgm=0, sex=0, 1=AA and 2=AB; for individuals with pgm=0, sex=1, 1=A and 2=B (hemizygous); for individuals with pgm=1, sex=0, 1=BB and 2=AB; for individuals with pgm=1, sex=1, 1=A and 2=B. This requires special care! The data were simulated using an additive model with three QTLs on chromosome 1 (at 30, 50 and 70 cM), one QTL on chromosome 13 (at 30 cM), and one QTL on the X chromosome (at 10 cM). Author(s) Karl W Broman, See Also sim.cross, fake.bc, fake.4way, listeria, hyper, bristle3, bristleX Examples data(fake.f2) summary(fake.f2) plot(fake.f2) fill.geno fill.geno 75 Fill holes in genotype data Description Replace the genotype data for a cross with a version imputed either by simulation with sim.geno, by the Viterbi algorithm with argmax.geno, or simply filling in genotypes between markers that have matching genotypes. Usage fill.geno(cross, method=c("imp","argmax", "no_dbl_XO", "maxmarginal"), error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), min.prob=0.95) Arguments cross An object of class cross. See read.cross for details. method Indicates whether to impute using a single simulation replicate from sim.geno, using the Viterbi algorithm, as implemented in argmax.geno, by simply filling in missing genotypes between markers with matching genotypes, or by choosing (at each marker) the genotype with maximal marginal probability. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi or Carter-Falconer map function when converting genetic distances into recombination fractions. min.prob For method="maxmarginal", genotypes with probability greater than this value will be imputed; those less than this value will be made missing. Details This function is written so that one may perform rough genome scans by marker regression without having to drop individuals with missing genotype data. We must caution the user that little trust should be placed in the results. With method="imp", a single random imputation is performed, using sim.geno. With method="argmax", for each individual the most probable sequence of genotypes, given the observed data (via argmax.geno), is used. With method="no_dbl_XO", non-recombinant intervals are filled in; recombinant intervals are left missing. For example, a sequence of genotypes like A---A---H---H---A (with A and H corresponding to genotypes AA and AB, respectively, and with - being a missing value) will be filled in as AAAAA---HHHHH---A. With method="maxmarginal", the conditional genotype probabilities are calculated with calc.genoprob, and then at each marker, the most probable genotype is determined. This is taken as the imputed genotype if it has probability greater than min.prob; otherwise it is made missing. With method="no_dbl_XO" and method="maxmarginal", some missing genotypes likely remain. With method="maxmarginal", some observed genotypes may be made missing. 76 find.flanking Value The input cross object with the genotype data replaced by an imputed version. Any intermediate calculations (such as is produced by calc.genoprob, argmax.geno and sim.geno) are removed. Author(s) Karl W Broman, See Also sim.geno, argmax.geno Examples data(hyper) out.mr <- scantwo(fill.geno(hyper,method="argmax"), method="mr") plot(out.mr) find.flanking Find flanking markers for a specified position Description Find the genetic markers flanking a specified position on a chromosome, as well as the marker that is closest to the specified position. Usage find.flanking(cross, chr, pos) Arguments cross An object of class cross. See read.cross for details. chr A vector of chromosome identifiers, or a single such. pos A vector of cM positions. Value A data.frame, each row corresponding to one of the input positions. The first column contains the left-flanking markers, the second column contains the right-flanking markers, and the third column contains the markers closest to the specified positions. Author(s) Brian Yandell See Also find.marker, plotPXG, find.markerpos, find.pseudomarker find.marker 77 Examples data(listeria) find.flanking(listeria, 5, 28) find.flanking(listeria, c(1, 5, 13), c(81, 28, 26)) find.marker Find marker closest to a specified position Description Find the genetic marker closest to a specified position on a chromosome. Usage find.marker(cross, chr, pos, index) Arguments cross chr pos index An object of class cross. See read.cross for details. A vector of chromosome identifiers, or a single such. A vector of cM positions. A vector of numeric indices of the markers within chromosomes. Details Provide one of pos or index. If the input chr has length one, it is expanded to the same length as the input pos or index. If pos is specified and multiple markers are exactly the same distance from the specified position, one is chosen at random from among those with the most genotype data. For a cross with sex-specific maps, positions specified by pos are assumed to correspond to the female genetic map. Value A vector of marker names (of the same length as the input pos), corresponding to the markers nearest to the specified chromosomes/positions (if pos is specified) or to the input numeric indices (in index is specified). Author(s) Karl W Broman, See Also find.flanking, plotPXG, find.pseudomarker, effectplot, find.markerpos Examples data(listeria) find.marker(listeria, 5, 28) find.marker(listeria, 5, index=6) find.marker(listeria, c(1, 5, 13), c(81, 28, 26)) 78 find.markerpos find.markerindex Determine the numeric index for a marker Description Determine the numeric index for a marker in a cross object, when all markers on all chromosomes are pasted together. Usage find.markerindex(cross, name) Arguments cross An object of class cross. See read.cross for details. name A vector of marker names. Value A vector of numeric indices, from 1, 2, . . . , totmar(cross), with NA for markers not found. Author(s) Danny Arends; Karl W Broman See Also find.markerpos Examples data(hyper) mar <- find.marker(hyper, 4, 30) find.markerindex(hyper, mar) find.markerpos Find position of a marker Description Find the chromosome and cM position of a set of genetic markers. Usage find.markerpos(cross, marker) Arguments cross An object of class cross. See read.cross for details. marker A vector of marker names. find.pheno 79 Value A data frame with two columns: the chromosome and position of the markers. Author(s) Karl W Broman, See Also find.flanking, find.marker, find.pseudomarker Examples data(hyper) find.markerpos(hyper, "D4Mit164") find.markerpos(hyper, c("D4Mit164", "D1Mit94")) find.pheno Find column number for a particular phenotype Description Find the column number corresponding to a particular phenotype name. Usage find.pheno(cross, pheno) Arguments cross An object of class cross. See read.cross for details. pheno Vector of phenotype names (as character strings). Value A vector of numbers, corresponding to the column numbers of the phenotype in the input cross with the specified names. Author(s) Brian Yandell Examples data(fake.bc) find.pheno(fake.bc, "sex") 80 find.pseudomarker find.pseudomarker Find the pseudomarker closest to a specified position Description Find the pseudomarker closest to a specified position on a chromosome. Usage find.pseudomarker(cross, chr, pos, where=c("draws", "prob"), addchr=TRUE) Arguments cross An object of class cross. See read.cross for details. chr A vector of chromosome identifiers, or a single such. pos A vector of cM positions. where Indicates whether to look in the draws or prob components of the input cross. addchr If TRUE, include something like "c5." at the beginning of the names of nonpseudomarker locations, as in the output of scanone; if FALSE, don’t include this sort of string, as in the genotype probabilities from calc.genoprob. Details If the input chr has length one, it is expanded to the same length as the input pos. If multiple markers are exactly the same distance from the specified position, one is chosen at random from among those with the most genotype data. For a cross with sex-specific maps, the input positions are assumed to correspond to the female genetic map. Value A vector of pseudomarker names (of the same length as the input pos), corresponding to the markers nearest to the specified chromosomes/positions. Author(s) Karl W Broman, See Also find.flanking, plotPXG, effectplot, find.marker, find.markerpos Examples data(listeria) listeria <- calc.genoprob(listeria, step=2.5) find.pseudomarker(listeria, 5, 28, "prob") find.pseudomarker(listeria, c(1, 5, 13), c(81, 28, 26), "prob") findDupMarkers findDupMarkers 81 Find markers with identical genotype data Description Identify sets of markers with identical genotype data. Usage findDupMarkers(cross, chr, exact.only=TRUE, adjacent.only=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector specifying which chromosomes to consider. This may be a logical, numeric, or character string vector. exact.only If TRUE, look only for markers that have matching genotypes and the same pattern of missing data; if FALSE, also look for cases where one the observed genotypes at marker match those at another, and where the first marker has missing genotype whenever the genotype for the second marker is missing. adjacent.only If TRUE, look only for sets of markers that are adjacent to each other. Details If exact.only=TRUE, we look only for groups of markers whose pattern of missing data and observed genotypes match exactly. One marker (chosen at random) is selected as the name of the group (in the output of the function). If exact.only=FALSE, we look also for markers whose observed genotypes are contained in the observed genotypes of another marker. We use a pair of nested loops, working from the markers with the most observed genotypes to the markers with the fewest observed genotypes. Value A list of marker names; each component is a set of markers whose genotypes match one other marker, and the name of the component is the name of the marker that they match. Author(s) Karl W Broman, See Also drop.nullmarkers, drop.markers 82 fitqtl Examples data(hyper) hyper <- drop.nullmarkers(hyper) dupmar <- findDupMarkers(hyper) # finds 4 pairs dupmar.adjonly <- findDupMarkers(hyper, adjacent.only=TRUE) # finds 4 pairs dupmar.nexact <- findDupMarkers(hyper, exact.only=FALSE, adjacent.only=TRUE) # finds 6 pairs # one might consider dropping the extra markers totmar(hyper) # 173 markers hyper <- drop.markers(hyper, unlist(dupmar.adjonly)) totmar(hyper) # 169 markers fitqtl Fit a multiple-QTL model Description Fits a user-specified multiple-QTL model. If specified, a drop-one-term analysis will be performed. Usage fitqtl(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp", "hk"), model=c("normal", "binary"), dropone=TRUE, get.ests=FALSE, run.checks=TRUE, tol=1e-4, maxit=1000, forceXcovar=FALSE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either nonintegers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl An object of class qtl, as output from makeqtl. covar A matrix or data.frame of covariates. These must be strictly numeric. formula An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as Q1, Q2, etc. Covariates are referred to by their names in the data frame covar. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits dropone If TRUE, do drop-one-term analysis. get.ests If TRUE, return estimated QTL effects and their estimated variance-covariance matrix. run.checks If TRUE, check the input formula and check for individuals with missing phenotypes or covariates. fitqtl 83 tol Tolerance for convergence for the binary trait model. maxit Maximum number of iterations for fitting the binary trait model. forceXcovar If TRUE, force inclusion of X-chr-related covariates (like sex and cross direction). Details The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect must also be included. In the drop-one-term analysis, for a given QTL/covariate model, all submodels will be analyzed. For each term in the input formula, when it is dropped, all higher order terms that contain it will also be dropped. The comparison between the new model and the full (input) model will be output. The estimated percent variances explained for the QTL are simplify transformations of the conditional LOD scores by the formula h2 = 1 − 10−(2/n)LOD . While these may be reasonable for unlinked, additive QTL, they can be completely wrong in the case of linked QTL, but we don’t currently have any alternative. For model="binary", a logistic regression model is used. The part to get estimated QTL effects is not complete for the case of the X chromosome and 4way crosses. The values returned in these cases are based on a design matrix that is convenient for calculations but not easily interpreted. The estimated QTL effects for a backcross are derived by the coding scheme ± 1/2 for AA and AB, so that the additive effect corresponds to the difference between phenotype averages for the two genotypes. For doubled haploids and RIL, the coding scheme is ± 1 for AA and BB, so that the additive effect corresponds to half the difference between the phenotype averages for the two homozygotes. For an intercross, the additive effect is derived from the coding scheme -1/0/+1 for genotypes AA/AB/BB, and so is half the difference between the phenotype averages for the two homozygotes. The dominance deviation is derived from the coding scheme 0/+1/0 for genotypes AA/AB/BB, and so is the difference between the phenotype average for the heterozygotes and the midpoint between the phenotype averages for the two homozygotes. Epistatic effects and QTL × covariate interaction effects are obtained through the products of the corresponding additive/dominant effect columns. Value An object of class fitqtl. It may contains as many as four components: • result.full is the ANOVA table as a matrix for the full model result. It contains the degree of freedom (df), Sum of squares (SS), mean square (MS), LOD score (LOD), percentage of variance explained (%var) and P value (Pvalue). • lod is the LOD score from the fit of the full model. • result.drop is a drop-one-term ANOVA table as a matrix. It contains degrees of freedom (df), Type III sum of squares (Type III SS), LOD score(LOD), percentage of variance explained (%var), F statistics (F value), and P values for chi square (Pvalue(chi2)) and F distribution (Pvalue(F)). Note that the degree of freedom, Type III sum of squares, the LOD score and the percentage of variance explained are the values comparing the full to the sub-model with the term dropped. Also note that for imputation method, the percentage of variance explained, the the F values and the P values are approximations calculated from the LOD score. 84 fitqtl • ests contains the estimated QTL effects and standard errors. When method="normal", residuals are saved as an attribute of the output, named "residuals" and accessible via the attr function. The part to get estimated QTL effects is fully working only for the case of autosomes in a backcross, intercross, RIL or doubled haploids. In other cases the values returned are based on a design matrix that is convenient for calculations but not easily interpreted. Author(s) Hao Wu; Karl W Broman, References Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also summary.fitqtl, makeqtl, scanqtl, refineqtl, addtoqtl , dropfromqtl, replaceqtl, reorderqtl Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 8, 13) qp <- c(26, 56, 28) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") # fit model with 3 interacting QTLs interacting # (performing a drop-one-term analysis) lod <- fitqtl(fake.f2, pheno.col=1, qtl, formula=y~Q1*Q2*Q3, method="hk") summary(lod) ## Not run: # fit an additive QTL model lod.add <- fitqtl(fake.f2, pheno.col=1, qtl, formula=y~Q1+Q2+Q3, method="hk") summary(lod.add) # fit the model including sex as an interacting covariate Sex <- data.frame(Sex=pull.pheno(fake.f2, "sex")) lod.sex <- fitqtl(fake.f2, pheno.col=1, qtl, formula=y~Q1*Q2*Q3*Sex, cov=Sex, method="hk") summary(lod.sex) # fit the same with an additive model lod.sex.add <- fitqtl(fake.f2, pheno.col=1, qtl, formula=y~Q1+Q2+Q3+Sex, cov=Sex, method="hk") summary(lod.sex.add) fitstahl 85 # residuals residuals <- attr(lod.sex.add, "residuals") plot(residuals) ## End(Not run) fitstahl Fit Stahl interference model Description Fit the Stahl model for crossover inference (or the chi-square model, which is a special case). Usage fitstahl(cross, chr, m, p, error.prob=0.0001, maxit=4000, tol=1e-4, maxm=15, verbose=TRUE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. m Interference parameter (a non-negative integer); if unspecified, this is estimated. p The proportion of chiasmata coming from the no interference mechanism in the Stahl model (0 <= p <= 1). p=0 gives the chi-square model. If unspecified, this is estimated. error.prob The genotyping error probability. If = NULL, it is estimated. maxit Maximum number of iterations to perform. tol Tolerance for determining convergence. maxm Maximum value of m to consider, if m is unspecified. verbose Logical; indicates whether to print tracing information. Details This function is currently only available for backcrosses and intercrosses. The Stahl model of crossover interference (of which the chi-square model is a special case) is fit. In the chi-square model, points are tossed down onto the four-strand bundle according to a Poisson process, and every (m + 1)st point is a chiasma. With the assumption of no chromatid interference, crossover locations on a random meiotic product are obtained by thinning the chiasma process. The parameter m (a non-negative integer) governs the strength of crossover interference, with m = 0 corresponding to no interference. In the Stahl model, chiasmata on the four-strand bundle are a superposition of chiasmata from two mechanisms, one following a chi-square model and one exhibiting no interference. An additional parameter, p, gives the proportion of chiasmata from the no interference mechanism. 86 fitstahl If all of m, p, and error.prob are specified, any of them with length > 1 must all have the same length. If m is unspecified, we do a grid search starting at 0 and stop when the likelihood decreases (thus assuming a single mode), or maxm is reached. Value A matrix with four columns: m, p, error.prob, and the log likelihood. If specific values for m, p, error.prob are provided, the log likelihood for each set are given. If some are left unspecified, the maximum likelihood estimates are provided in the results. Author(s) Karl W Broman, References Armstrong, N. J., McPeek, M. J. and Speed, T. P. (2006) Incorporating interference into linkage analysis for experimental crosses. Biostatistics 7, 374–386. Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. Genetics 139, 1045–1056. See Also est.map, sim.cross Examples # Simulate genetic map: one chromosome of length 200 cM with # a 2 cM marker spacing mymap <- sim.map(200, 51, anchor.tel=TRUE, include.x=FALSE, sex.sp=FALSE, eq.spacing=TRUE) # Simulate data under the chi-square model, no errors mydata <- sim.cross(mymap, n.ind=250, type="bc", error.prob=0, m=3, p=0) # Fit the chi-square model for specified m's ## Not run: output <- fitstahl(mydata, m=1:5, p=0, error.prob=0) plot(output$m, output$loglik, lwd=2, type="b") # Find the MLE of m in the chi-square model ## Not run: mle <- fitstahl(mydata, p=0, error.prob=0) ## Not run: # Simulate data under the Stahl model, no errors mydata <- sim.cross(mymap, n.ind=250, type="bc", error.prob=0, m=3, p=0.1) # Find MLE of m for the Stahl model with known p mle.stahl <- fitstahl(mydata, p=0.1, error.prob=0) # Fit the Stahl model with unknown p and m, flip.order 87 # get results for m=0, 1, 2, ..., 8 output <- fitstahl(mydata, m=0:8, error.prob=0) plot(output$m, output$loglik, type="b", lwd=2) ## End(Not run) flip.order Flip the orders of markers on a set of chromosomes Description Flip the orders of markers on a specified set of chromosome, so that the markers will be in the reverse order. Usage flip.order(cross, chr) Arguments cross An object of class cross. See read.cross for details. chr Vector indicating the chromosomes to flip. This should be a vector of character strings referring to chromosomes by name. A logical (TRUE/FALSE) vector may also be used. Details If the cross contains results from calc.genoprob, sim.geno, argmax.geno, or calc.errorlod, those results are also updated. Results of est.rf and markerlrt are deleted. Value The input cross object, but with the marker order on the specified chromosomes flipped. Author(s) Karl W Broman, See Also switch.order Examples data(fake.f2) fake.f2 <- flip.order(fake.f2, c(1, 5, 13)) 88 formLinkageGroups formLinkageGroups Partition markers into linkage groups Description Use pairwise linkage information between markers (as calculated by est.rf to partition markers into linkage groups. Usage formLinkageGroups(cross, max.rf=0.25, min.lod=3, reorgMarkers=FALSE, verbose=FALSE) Arguments cross An object of class cross. See read.cross for details. max.rf Maximum recombination fraction for placing two markers in the same linkage group (see Details). min.lod Minimum LOD score for placing two markers in the same linkage group (see Details). reorgMarkers If TRUE, the output is a cross object, like the input, but with the markers organized into the inferred linkage groups. If FALSE, the output is a table indicating the initial chromosome assignments and the inferred linkage group partitions. verbose If TRUE, display information about the progress of the calculations. Details Two markers are placed in the same linkage group if the estimated recombination fraction between them is ≤ max.rf and the LOD score (for the test of the rec. frac. = 1/2) is ≥ min.lod. The transitive property (if A is linked to B and B is linked to C then A is linked to C) is used to close the groups. Value If reorgMarkers=FALSE (the default), the output is a data frame with rows corresponding to the markers and with two columns: the initial chromosome assignment and the inferred linkage group. Linkage groups are ordered by the number of markers they contain (from largest to smallest). If reorgMarkers=TRUE, the output is a cross object, like the input, but with the markers reorganized into the inferred linkage groups. The marker order and marker positions within the linkage groups are arbitrary. Author(s) Karl W Broman, See Also est.rf, orderMarkers formMarkerCovar 89 Examples data(listeria) listeria <- est.rf(listeria) result <- formLinkageGroups(listeria) tab <- table(result[,1], result[,2]) apply(tab, 1, function(a) sum(a!=0)) apply(tab, 2, function(a) sum(a!=0)) formMarkerCovar Create matrix of marker covariates for QTL analysis Description Pull out a matrix of genotypes or genotype probabilities to use markers as covariates in QTL analysis. Usage formMarkerCovar(cross, markers, method=c("prob", "imp", "argmax"), ...) Arguments cross An object of class cross. See read.cross for details. markers A vector of character strings of marker or pseudomarker names. Pseudomarker names may be of the form "5@21.5" (for chr 5 at 21.5 cM), but then all names must be of this form. method If method="prob", the genotype probabilities from calc.genoprob are used; otherwise we use fill.geno to impute missing data, with this method. ... Passed to fill.geno, if necessary. Value A matrix containing genotype probabilities or genotype indicators, suitable for use as covariates in scanone. Author(s) Karl W Broman, See Also pull.geno, pull.genoprob, fill.geno, scanone Examples data(hyper) hyper <- calc.genoprob(hyper, step=0) peakMarker <- "D4Mit164" X <- formMarkerCovar(hyper, peakMarker) out <- scanone(hyper, addcovar=X) 90 geno.crosstab geno.crosstab Create table of two-locus genotypes Description Create a cross tabulation of the genotypes at a pair of markers. Usage geno.crosstab(cross, mname1, mname2, eliminate.zeros=TRUE) Arguments cross An object of class cross. See read.cross for details. mname1 The name of the first marker (as a character string). (Alternatively, a vector with the two character strings, in which case mname2 should not be given.) mname2 The name of the second marker (as a character string). eliminate.zeros If TRUE, don’t show the rows and columns that have no data. Value A matrix containing the number of individuals having each possible pair of genotypes. Genotypes for the first marker are in the rows; genotypes for the second marker are in the columns. Author(s) Karl W Broman, See Also geno.table, find.marker Examples data(hyper) geno.crosstab(hyper, "D1Mit123", "D1Mit156") geno.crosstab(hyper, "DXMit22", "DXMit16") geno.crosstab(hyper, c("DXMit22", "DXMit16")) geno.image 91 geno.image Plot grid of genotype data Description Plot a grid showing which the genotype data in a cross. Usage geno.image(x, chr, reorder=FALSE, main="Genotype data", alternate.chrid=FALSE, ...) Arguments x An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to be drawn in the plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. reorder Specify whether to reorder individuals according to their phenotypes. FALSE TRUE n Don’t reorder Reorder according to the sum of the phenotypes Reorder according to phenotype n main Title to place on plot. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Passed to image. Details Uses image to plot a grid with the genotype data. The genotypes AA, AB, BB are displayed in the colors red, blue, and green, respectively. In an intercross, if there are genotypes "not BB" and "not AA", these are displayed in purple and orange, respectively. White pixels indicate missing data. Value None. Author(s) Karl W Broman, See Also plot.cross, plotMissing, plotGeno, image 92 geno.table Examples data(listeria) geno.image(listeria) geno.table Create table of genotype distributions Description Create table showing the observed numbers of individuals with each genotype at each marker, including P-values from chi-square tests for Mendelian segregation. Usage geno.table(cross, chr, scanone.output=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. scanone.output If TRUE, give result in the form output by scanone, so that one may use plot.scanone, etc. Details The P-values are obtained from chi-square tests of Mendelian segregation. In the case of the X chromosome, the sexes and cross directions are tested separately, and the chi-square statistics combined, and so the test is of whether any of the groups show deviation from Mendel’s rules. Value If scanone.output=FALSE, the output is a matrix containing, for each marker, the number of individuals with each possible genotype, as well as the number that were not typed. The first column gives the chromosome ID, and the last column gives P-values from chi-square tests of Mendelian segregation. If scanone.output=TRUE, the output is of the form produced by scanone, with the first two columns being chromosome IDs and cM positions of the markers. The third column is − log10 (P ) from chi-square tests of Mendelian segregation. The fourth column is the proportion of missing data. The remaining columns are the proportions of the different genotypes (among typed individuals). Author(s) Karl W Broman, getid 93 See Also summary.cross, drop.markers, drop.nullmarkers Examples data(listeria) geno.table(listeria) geno.table(listeria, chr=13) gt <- geno.table(listeria) gt[gt$P.value < 0.01,] out <- geno.table(listeria, scanone.output=TRUE) plot(out) plot(out, lod=2) getid Pull out the individual identifiers from a cross Description Pull out the individual identifiers from a cross object. Usage getid(cross) Arguments cross An object of class cross. See read.cross for details. Value A vector of individual identifiers, pulled from the phenotype data (a column named id or ID). If there are no such identifiers in the cross, the function returns NULL. Author(s) Karl W Broman, See Also subset.cross, top.errorlod Examples data(fake.f2) # create an ID column fake.f2$pheno$id <- paste("ind", sample(nind(fake.f2)), sep="") getid(fake.f2) 94 groupclusteredheatmap groupclusteredheatmap Retrieving groups of traits after clustering Description Retrieving groups of clustered traits from the output of mqmplot.clusteredheatmap. Usage groupclusteredheatmap(cross, clusteredheatmapresult, height) Arguments cross An object of class cross. See read.cross for details. clusteredheatmapresult Resultint dendrogram object from mqmplot.clusteredheatmap height Height at which to ’cut’ the dendrogram, a higher cut-off gives less but larger groups. Height represents the maximum distance between two traits clustered together using hclust. the ’normal’ behaviour of bigger groups when using a higher heigh cut-off depends on the tree stucture and the amount of traits clustered using mqmplot.clusteredheatmap Value A list containing groups of traits which were clustered together with a distance less that height Author(s) Danny Arends See Also • • • • • • • • • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf MQM - MQM description and references mqmscan - Main MQM single trait analysis mqmscanall - Parallellized traits analysis mqmaugment - Augmentation routine for estimating missing data mqmautocofactors - Set cofactors using marker density mqmsetcofactors - Set cofactors at fixed locations mqmpermutation - Estimate significance levels scanone - Single QTL scanning Examples data(multitrait) multitrait <- fill.geno(multitrait) # impute missing genotype data result <- mqmscanall(multitrait, logtransform=TRUE) cresults <- mqmplot.clusteredheatmap(multitrait,result) groupclusteredheatmap(multitrait,cresults,10) hyper hyper 95 Data on hypertension Description Data from an experiment on hypertension in the mouse. Usage data(hyper) Format An object of class cross. See read.cross for details. Details There are 250 male backcross individuals typed at 174 markers (actually one contains only missing values), including 4 on the X chromosome, with one phenotype. The phenotype is the blood pressure. See the reference below. Note that, for most markers, genotypes are available on only the individuals with extreme phenotypes. At many markers, only recombinant individuals were typed. Source Bev Paigen and Gary Churchill (The Jackson Laboratory, Bar Harbor, Maine) http://phenome. jax.org/db/q?rtn=projects/projdet&reqprojid=119 References Sugiyama, F., Churchill, G. A., Higgens, D. C., Johns, C., Makaritsis, K. P., Gavras, H. and Paigen, B. (2001) Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci. Genomics 71, 70–77. See Also fake.bc, fake.f2, fake.4way, listeria, bristle3, bristleX Examples data(hyper) summary(hyper) plot(hyper) # Note the selective genotyping ## Not run: plotMissing(hyper, reorder=TRUE) # A marker on c14 has no data; remove it hyper <- drop.nullmarkers(hyper) 96 inferFounderHap inferFounderHap Crude reconstruction of founder haplotypes in multi-parent RIL Description Uses groups of adjacent markers to infer the founder haplotypes in SNP data on multi-parent recombinant inbred lines. Usage inferFounderHap(cross, chr, max.n.markers=15) Arguments cross An object of class cross. See read.cross for details. chr Indicator of chromosome to consider. If multiple chromosomes are selected, only the first is used. max.n.markers Maximum number of adjacent markers to consider. Details We omit SNPs for which any of the founders are missing. We then consider groups of adjacent SNPs, looking for founder haplotypes that are unique; RIL sharing such a unique haplotype are then inferred to have that founder’s DNA. We consider each marker as the center of a haplotype, and consider haplotypes of size 1, 3, 5, . . . , max.n.markers. We end the extension of the haplotypes when all founders have a unique haplotype. Value A matrix of dimension nind(cross) × no. markers, with the inferred founder origin for each line at each marker. Author(s) Karl W Broman, See Also sim.geno, calc.genoprob, fill.geno, argmax.geno Examples map <- sim.map(100, n.mar=101, include.x=FALSE, eq.spacing=TRUE) founderGeno <- simFounderSnps(map, "8") ril <- sim.cross(map, n.ind=10, type="ri8sib", founderGeno=founderGeno) h <- inferFounderHap(ril, max.n.markers=11) mean(!is.na(h)) # proportion inferred plot(map[[1]], h[1,], ylim=c(0.5, 8.5), xlab="Position", ylab="Genotype") inferredpartitions 97 inferredpartitions Identify inferred partitions in mapping QTL to a phylogenetic tree Description Identify the inferred partitions for a chromosome from the results of scanPhyloQTL. Usage inferredpartitions(output, chr, lodthreshold, probthreshold=0.9) Arguments output An object output by the function scanPhyloQTL. chr A character string indicating the chromosome to consider. (It can also be a number, but it’s then converted to a character string.) lodthreshold LOD threshold; if maximum LOD score is less than this, the null model is considered. probthreshold Threshold on posterior probabilities. See Details below. Details We consider a single chromosome, and take the maximum LOD score for each partition on that chromosome. The presence of a QTL is inferred if at least one partition has LOD score greater than lodthreshold. In this case, we then convert the LOD scores for the partitions to approximate posterior probabilities by taking 10LOD and then rescaling them to sum to 1. These are sorted from largest to smallest, and we then take as the inferred partitions the smallest set whose posterior probabilities cumulatively add up to at least probthreshold. Value A vector of character strings. If the null model (no QTL) is inferred, the output is "null". Otherwise, it is the set of inferred partitions. Author(s) Karl W Broman, References Broman, K. W., Kim, S., An\’e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation. See Also scanPhyloQTL, plot.scanPhyloQTL, summary.scanPhyloQTL, max.scanPhyloQTL, simPhyloQTL 98 interpPositions Examples # example map; drop X chromosome data(map10) map10 <- map10[1:19] # simulate data x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"), map=map10, n.ind=150, model=c(1, 50, 0.5, 0)) # run calc.genoprob on each cross ## Not run: x <- lapply(x, calc.genoprob, step=2) # scan genome, at each position trying all possible partitions out <- scanPhyloQTL(x, method="hk") # inferred partitions inferredpartitions(out, chr=3, lodthreshold=3) # inferred partitions with prob'y threshold = 0.95 inferredpartitions(out, chr=3, lodthreshold=3, probthreshold=0.95) interpPositions Interpolate positions from one map to another Description On the basis of a pair of marker maps with common markers, take positions along one map and interpolate (or, past the terminal markers on a chromosome, extrapolate) their positions on the second map. Usage interpPositions(oldpositions, oldmap, newmap) Arguments oldpositions A data frame with two columns: chr (chromosome identifiers) and pos (positions, along oldmap). oldmap An object of class "map"; see sim.map for details. newmap An object of class "map", with the same chromosomes and markers as oldmap. Details In this explanation, take oldmap and newmap to be the physical and genetic maps, respectively. We use linear interpolation within each interval, assuming a constant recombination rate within the interval. Past the terminal markers, we use linear extrapolation, using the chromosome-wide average recombination rate. jittermap 99 Value The input data frame, oldpositions, with an additional column newpos with the interpolated positions along newmap. Author(s) Karl W Broman, See Also shiftmap, rescalemap, pull.map Examples data(hyper) # hyper genetic map gmap <- pull.map(hyper) # a fake physical map, with each chromosome starting at 0. pmap <- shiftmap(rescalemap(gmap, 2)) # positions on pmap to determine location on gmap tofind <- data.frame(chr=c(1, 5, 17, "X"), pos=c(220, 20, 105, 10)) rownames(tofind) <- paste("loc", 1:nrow(tofind), sep="") interpPositions(tofind, pmap, gmap) jittermap Jitter marker positions in a genetic map Description Jitter the marker positions in a genetic map so that no two markers are on top of each other. Usage jittermap(object, amount=1e-6) Arguments object Either a cross (an object of class cross; see read.cross for details) or a map (an object of class map; see pull.map for details). amount The amount by which markers should be moved. Value Either the input cross object or the input map, but with marker positions slightly jittered. If the input was a cross, the function clean is run to strip off any intermediate calculations. Author(s) Karl W Broman, 100 listeria See Also pull.map, replace.map, summary.cross Examples data(hyper) hyper <- jittermap(hyper) listeria Data on Listeria monocytogenes susceptibility Description Data from an experiment on susceptibility to Listeria monocytogenes infection in the mouse. Usage data(listeria) Format An object of class cross. See read.cross for details. Details There are 120 F2 individuals typed at 133 markers, including 2 on the X chromosome, with one phenotype. The phenotype is the survival time (in hours) following infection. Mice with phenotype 264 hours may be considered to have recovered from the infection. See the references below. Source Victor Boyartchuk and William Dietrich (Department of Genetics, Harvard Medical School and Howard Hughes Medical Institute) References Boyartchuk, V. L., Broman, K. W., Mosher, R. E., D’Orazio S. E. F., Starnbach, M. N. and Dietrich, W. F. (2001) Multigenic control of Listeria monocytogenes susceptibility in mice. Nature Genetics 27, 259–260. Broman, K. W. (2003) Mapping quantitative trait loci in the case of a spike in the phenotype distribution. Genetics 163, 1169–1175. See Also fake.bc, fake.f2, fake.4way, hyper, bristle3, bristleX locateXO 101 Examples data(listeria) # Summaries summary(listeria) plot(listeria) # Take log of phenotype listeria$pheno[,1] <- log2(listeria$pheno[,1]) plot(listeria) # Genome scan with a two-part model, using log survival listeria <- calc.genoprob(listeria, step=2) out <- scanone(listeria, model="2part", method="em", upper=TRUE) # Summary of the results summary(out, thr=c(5,3,3), format="allpeaks") # Plot LOD curves for interesting chromosomes # (The two-part model gives three LOD scores) plot(out, chr=c(1,5,6,13,15), lodcolumn=1:3, lty=1, col=c("black","red","blue")) locateXO Estimate locations of crossovers Description Estimate the locations of crossovers for each individual on a given chromosome. Usage locateXO(cross, chr, full.info=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Chromosome to investigate (if unspecified, the first chromosome is considered). This should be a character string referring to a chromosome by name; numeric values are converted to strings. full.info If TRUE, output will include information on the left and right endpoints of the intervals to which recombination events are known, as well as the corresponding marker indices. Details For each individual we detemine the locations of obligate crossovers, and estimate their location to be at the midpoint between the nearest flanking typed markers. The function currently only works for a backcross, intercross, or recombinant inbred line. 102 locations Value A list with one component per individual. Each component is either NULL or is a numeric vector with the estimated crossover locations. If full.info=TRUE, in place of a numeric vector with estimated locations, there is a matrix that includes those locations, the left and right endpoints of the intervals to which crossovers can be placed, the marker indices corresponding to those endpoint, and genotype codes for the genotypes to the left and right of each crossover. The final column indicates the number of typed markers between the current crossover and the next one (useful for identifying potential genotyping errors). Author(s) Karl W Broman, See Also countXO, cleanGeno Examples data(hyper) xoloc <- locateXO(hyper, chr=4) table(sapply(xoloc, length)) locations Genetic locations of traits for the multitrait dataset Description A table with genetic locations of the traits in the multitrait dataset Usage data(locations) Format Each row is a trait with the following information: Name, Name of the trait (will be checked against the name in the cross object Chr, Chromosome of the trait cM, Location in cM from the start of the chromosome Source Additional information from the Arabidopsis RIL selfing experiment with Landsberg erecta (Ler) and Cape Verde Islands (Cvi) with 162 individuals scored (with errors at) 117 markers. Dataset obtained from GBIC - Groningen BioInformatics Centre References • Keurentijes JJB, Fu J, de Vos CHR,Lommen A, Jansen RC et al (2006), The genetics of plant metabolism. Nature Genetics 38, 842–849. • Alonso-Blanco C., Peeters, A. J. and Koornneef, M. (2006) Development of an AFLP based linkage map of Ler, Col and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population. Plant J. 14(2), 259–271. lodint 103 See Also multitrait Examples ## Not run: data(multitrait) data(locations) multiloc <- addloctocross(multitrait,locations) results <- scanall(multiloc) mqmplot.cistrans(results,multiloc, 5, FALSE, TRUE) ## End(Not run) lodint LOD support interval Description Calculate a LOD support interval for a particular chromosome, using output from scanone. Usage lodint(results, chr, qtl.index, drop=1.5, lodcolumn=1, expandtomarkers=FALSE) Arguments results Output from scanone, or a qtl object as output from refineqtl. chr A chromosome ID (if input results are from scanone (should have length 1). qtl.index Numeric index for a QTL (if input results are from refineqtl (should have length 1). drop LOD units to drop to form the interval. lodcolumn An integer indicating which of the LOD score columns should be considered (if input results are from scanone). expandtomarkers If TRUE, the interval is expanded to the nearest flanking markers. Value An object of class scanone indicating the estimated QTL position and the approximate endpoints for the LOD support interval. Author(s) Karl W Broman, See Also scanone, bayesint 104 makeqtl Examples data(hyper) hyper <- calc.genoprob(hyper, step=0.5) out <- scanone(hyper, method="hk") lodint(out, chr=1) lodint(out, chr=4) lodint(out, chr=4, drop=2) lodint(out, chr=4, expandtomarkers=TRUE) makeqtl Make a qtl object Description This function takes a cross object and specified chromosome numbers and positions and pulls out the genotype probabilities or imputed genotypes at the nearest pseudomarkers, for later use by the function fitqtl. Usage makeqtl(cross, chr, pos, qtl.name, what=c("draws","prob")) Arguments cross An object of class cross. See read.cross for details. chr Vector indicating the chromosome for each QTL. (These should be character strings referring to the chromosomes by name.) pos Vector (of same length as chr) indicating the positions on the chromosome to be taken. If there is no marker or pseudomarker at a position, the nearest position is used. qtl.name Optional user-specified name for each QTL, used in the drop-one-term ANOVA table in fitqtl. If unspecified, the names will be of the form "Chr1@10" for a QTL on Chromsome 1 at 10 cM. what Indicates whether to pull out the imputed genotypes or the genotype probabilities. Details This function will take out the genotype probabilities and imputed genotypes if they are present in the input cross object. If both fields are missing in the input object, the function will report an error. Before running this function, the user must have first run either sim.geno (for what="draws") or calc.genoprob (for what="prob"). Value An object of class qtl with the following elements (though only one of geno and prob will be included, according to whether what is given as "draws" or "prob"): geno Imputed genotypes. map10 105 prob Genotype probabilities. name User-defined name for each QTL, or a name of the form "Chr1@10". altname QTL names of the form "Q1", "Q2", etc. chr Input vector of chromosome numbers. pos Input vector of chromosome positions. n.qtl Number of QTLs. n.ind Number of individuals. n.gen A vector indicating the number of genotypes for each QTL. Author(s) Hao Wu; Karl W Broman, See Also fitqtl, calc.genoprob, sim.geno, dropfromqtl, replaceqtl, addtoqtl, summary.qtl, reorderqtl Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c("1", "6", "13") qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- sim.geno(fake.f2, n.draws=8, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="draws") summary(qtl) map10 An example genetic map Description A genetic map corresponding approximately to the mouse genome with a 10 cM marker spacing. Usage data(map10) Format An object of class map: a list whose components are vectors of marker locations. This map approximates the mouse genome, with 20 chromosomes (including the X chromosome) and 187 markers at an approximately 10 cM spacing. The markers are equally spaced on each chromosome, but the spacings are a bit above or below 10 cM, so that the lengths match those in the Mouse Genome Database. 106 map2table See Also sim.map, plotMap, pull.map Examples data(map10) plot(map10) mycross <- sim.cross(map10, type="f2", n.ind=100) map2table Convert genetic map from list to table. Description Convert a map object (as a list) to a table (as a data frame). Usage map2table(map, chr) Arguments map A map object: a list whose components (corresponding to chromosomes) are either vectors of marker positions or matrices with two rows of sex-specific marker positions. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. Value A data frame with two or three columns: chromosome and sex-averaged position, or chromosome, female position, and male position. The row names are the marker names. Author(s) Karl W Broman, See Also table2map, pull.map, est.map Examples data(fake.f2) map <- pull.map(fake.f2) map_as_tab <- map2table(map) mapthis mapthis 107 Simulated data for illustrating genetic map construction Description Simulated data for an F2 intercross, obtained using sim.cross, useful for illustrating the process of constructing a genetic map. Usage data(mapthis) Format An object of class cross. See read.cross for details. Details These are simulated data, consisting of 300 F2 individuals typed at 100 markers on five chromosomes. There are no real phenotypes, just a set of individual identifiers. The data were simulated for the purpose of illustrating the process of constructing a genetic map. The markers are all assigned to a single chromosome and in a random order, and there are a number of problematic markers and individuals. See https://rqtl.org/tutorials/geneticmaps.pdf for a tutorial on how to construct a genetic map with these data. Author(s) Karl W Broman, References Broman, K. W. (2010) Genetic map construction with R/qtl. Technical report #214, Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison See Also fake.f2, est.rf, est.map, formLinkageGroups, orderMarkers Examples data(mapthis) summary(mapthis) plot(mapthis) 108 markernames markerlrt General likelihood ratio test for association between marker pairs Description Calculate a LOD score for a general likelihood ratio test for each pair of markers, to assess their association. Usage markerlrt(cross) Arguments cross An object of class cross. See read.cross for details. Value The input cross object is returned with a component, rf, added. This is a matrix of size (tot.mar x tot.mar). The diagonal contains the number of typed meioses per marker, the upper and lower triangles each contain the LOD scores. Author(s) Karl W Broman, See Also plotRF, est.rf, badorder Examples data(badorder) badorder <- markerlrt(badorder) plotRF(badorder) markernames Pull out the marker names from a cross Description Pull out the marker names from a cross object as one big vector. Usage markernames(cross, chr) max.scanone 109 Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. Value A vector of character strings (the marker names). Author(s) Karl W Broman, See Also pull.map, phenames, chrnames Examples data(listeria) markernames(listeria, chr=5) max.scanone Maximum peak in genome scan Description Print the row of the output from scanone that corresponds to the maximum LOD, genome-wide. Usage ## S3 method for class 'scanone' max(object, chr, lodcolumn=1, na.rm=TRUE, ...) Arguments object An object of the form output by the function scanone: a data.frame whose third column is the LOD score. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. lodcolumn An integer, indicating which of the LOD score columns should be considered in pulling out the peak (these are indexed 1, 2, . . . ). na.rm A logical indicating whether missing values should be removed. ... Ignored. 110 max.scanPhyloQTL Value An object of class summary.scanone, to be printed by print.summary.scanone. This is a data.frame with one row, corresponding to the maximum LOD peak either genome-wide or for the particular chromosome specified. Author(s) Karl W Broman, See Also scanone, plot.scanone, summary.scanone Examples data(listeria) listeria <- calc.genoprob(listeria, step=2.5) out <- scanone(listeria, model="2part", upper=TRUE) # Maximum peak for LOD(p,mu) max(out) # Maximum peak for LOD(p,mu) on chr 5 max(out,chr=5) # Maximum peak for LOD(p,mu) on chromosomes other than chr 13 max(out,chr="-13") # Maximum peak for LOD(p) max(out, lodcolumn=2) # Maximum peak for LOD(mu) max(out, lodcolumn=3) max.scanPhyloQTL Maximum peak in genome scan to map a QTL to a phylogenetic tree Description Print the chromosome with the maximum LOD score across partitions, from the results of scanPhyloQTL. Usage ## S3 method for class 'scanPhyloQTL' max(object, chr, format=c("postprob", "lod"), ...) max.scanPhyloQTL 111 Arguments object An object output by the function scanPhyloQTL. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. format Indicates whether to provide LOD scores or approximate posterior probabilities; see the help file for summary.scanPhyloQTL. ... Ignored at this point. Details The output, and the use of the argument format, is as in summary.scanPhyloQTL. Value An object of class summary.scanPhyloQTL, to be printed by print.summary.scanPhyloQTL. Author(s) Karl W Broman, References Broman, K. W., Kim, S., An\’e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation. See Also scanPhyloQTL, plot.scanPhyloQTL, summary.scanPhyloQTL, max.scanone, inferredpartitions, simPhyloQTL Examples ## Not run: # example map; drop X chromosome data(map10) map10 <- map10[1:19] # simulate data x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"), map=map10, n.ind=150, model=c(1, 50, 0.5, 0)) # run calc.genoprob on each cross x <- lapply(x, calc.genoprob, step=2) # scan genome, at each position trying all possible partitions out <- scanPhyloQTL(x, method="hk") # maximum peak max(out, format="lod") 112 max.scantwo # approximate posterior probabilities at peak max(out, format="postprob") # all peaks above a threshold for LOD(best) - LOD(2nd best) summary(out, threshold=1, format="lod") # all peaks above a threshold for LOD(best), showing approx post'r prob summary(out, format="postprob", threshold=3) # plot of results plot(out) ## End(Not run) max.scantwo Maximum peak in two-dimensional genome scan Description Print the pair of loci with the largest LOD score in the results of scantwo. Usage ## S3 method for class 'scantwo' max(object, lodcolumn=1, what=c("best", "full", "add", "int"), na.rm=TRUE, ...) Arguments object An object of class scantwo, the output of the function scantwo. lodcolumn If the scantwo results contain LOD scores for multiple phenotypes, this argument indicates which to use. what Indicates for which LOD score the maximum should be reported. na.rm Ignored. ... Ignored. Details This is very similar to the summary.scantwo function, though this pulls out one pair of positions. If what="best", we find the pair of positions at which the LOD score for the full model (2 QTL + interaction) is maximized, and then also print the positions on that same pair of chromosomes at which the additive LOD score is maximized. In the other cases, we pull out the pair of positions with the largest LOD score; which LOD score is considered is indicated by the what argument. Value An object of class summary.scantwo, to be printed by print.summary.scantwo, with the pair of positions with the maximum LOD score. (Which LOD score is considered is indicated by the what argument.) movemarker 113 Output of addpair Note that, for output from addpair in which the new loci are indicated explicitly in the formula, the summary provided by max.scantwo is somewhat special. All arguments (except, of course, the input object) are ignored. If the formula is symmetric in the two new QTL, the output has just two LOD score columns: lod.2v0 comparing the full model to the model with neither of the new QTL, and lod.2v1 comparing the full model to the model with just one new QTL. If the formula is not symmetric in the two new QTL, the output has three LOD score columns: lod.2v0 comparing the full model to the model with neither of the new QTL, lod.2v1b comparing the full model to the model in which the first of the new QTL is omitted, and lod.2v1a comparing the full model to the model with the second of the new QTL omitted. Author(s) Karl W Broman, See Also scantwo, plot.scantwo, summary.scantwo Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=10) out.2dim <- scantwo(fake.f2, method="hk") max(out.2dim) movemarker Move a marker to a new chromosome Description Move a specified marker to a different chromosome. Usage movemarker(cross, marker, newchr, newpos) Arguments cross An object of class cross. See read.cross for details. marker The name of the marker to be moved (a character string). newchr The chromosome to which the marker should be moved. newpos The position (in cM) at which the marker should be placed. If missing, the marker is placed at the end of the chromosome. Value The input cross object, but with the specified marker moved to the specified chromosome. All intermediate calculations (such as the results of calc.genoprob and est.rf) are removed. 114 MQM Author(s) Karl W Broman, See Also switch.order Examples data(badorder) badorder <- movemarker(badorder, "D2M937", 3, 48.15) badorder <- movemarker(badorder, "D3M160", 2, 28.83) MQM Introduction to Multiple QTL Model (MQM) mapping Description Overview of the MQM mapping functions Introduction Multiple 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 multiCPU 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. R/qtl-MQM has the following advantages: • Higher power to detect linked as well as unlinked QTL, as long as the QTL explain a reasonable amount of variation • 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 • Prevention of ghost QTL (between two QTL in coupling phase) • Detection of negating QTL (QTL in repulsion phase) MQM 115 Note 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 (https://rqtl. org/STATUS.txt) for updates. Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman References • Arends D, Prins P, Jansen RC. R/qtl: High-throughput multiple QTL mapping. Bioinformatics, to appear • Jansen RC, (2007) Quantitative trait loci in inbred lines. Chapter 18 of Handbook of Stat. Genetics 3rd edition. John Wiley & Sons, Ltd. • Jansen RC, Nap JP (2001), Genetical genomics: the added value from segregation. Trends in Genetics, 17, 388–391. • Jansen RC, Stam P (1994), High resolution of quantitative traits into multiple loci via interval mapping. Genetics, 136, 1447–1455. • Jansen RC (1993), Interval mapping of multiple quantitative trait loci. Genetics, 135, 205– 211. • Swertz MA, Jansen RC. (2007), Beyond standardization: dynamic software infrastructures for systems biology. Nat Rev Genet. 3, 235–243. • Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B, 39, 1–38. See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(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) 116 mqmaugment # 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) mqmaugment MQM augmentation Description Fill in missing genotypes for MQM mapping. For each missing or incomplete marker it fills in (or ‘augments’) all possible genotypes, thus creating new candidate ‘individuals’. The probability of each indidual is calculated using information on neighbouring markers and recombination frequencies. When a genotype of an augmented genotype is less likely than the minprob parameter it is dropped from the dataset. The augmented list of individuals is returned in a new cross object. For a full discussion on augmentation see the MQM tutorial online. Usage mqmaugment(cross, maxaugind=82, minprob=0.1, strategy=c("default","impute","drop"), verbose=FALSE) Arguments cross An object of class cross. See read.cross for details. maxaugind Maximum number of augmentations per individual. The default of 82 allows for six missing markers for an individual in a BC cross (26 = 64) and four missing markers in an F2 (34 = 81). When a large number of markers are missing this default number is quickly reached. minprob Return individuals with augmented genotypes that have at least this probability of occurring. minprob is a value between 0 and 1. For example a value of 0.5 will drop all genotypes that are half as likely as the most likely genotype (candidate of the individual). The default value of 0.1 will drop all genotypes that are less likely of ocurring than 1 in 10, compared against the most likely genotype. Use a value of 1.0 to return a single filled in genotype for each individual. strategy When individuals have too much missing data and augmentation fails three options are provided: 1. "default": Calculate genotypes at missing marker positions, accounting for minprob, and add this individual to the set. 2. "impute": Calculate the most likely genotypes at missing marker positions and impute maxaugind individual-variants around the most likely genotype. 3. "drop": Drop individuals that cannot be augmented from the dataset, this option is not advised because information from the dropped individuals will be lost. verbose If TRUE, give verbose output mqmaugment 117 Value Returns the cross object with augmented individuals (many individuals from the data set will be repeated multiple times). Some individuals may have been dropped completely when the probability falls below minprob. An added component to the cross object named mqm contains information on exactly which individuals are retained and repeated. Note The sex chromosome ’X’ is treated like autosomes during augmentation. With an F2 the sex chromosome is not considered. This will change in a future version of MQM. Run with verbose=TRUE to verify how many individuals are augmented versus moved to the second augmentation round. This could have an effect on the resulting dataset or check the return cross$mqm values. Compare results by using minprob=1. Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • fill.geno - Alternative routine for estimating missing data • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(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) 118 mqmautocofactors mqmautocofactors Automatic setting of cofactors, taking marker density into account Description Sets cofactors, taking underlying marker density into account. Together with mqmscan cofactors are selected through backward elimination. Usage mqmautocofactors(cross, num=50, distance=5, dominance=FALSE, plot=FALSE, verbose=FALSE) Arguments cross num distance dominance plot verbose An object of class cross. See read.cross for details. Number of cofactors to set (warns when setting too many cofactors). Minimal distance between two cofactors, in cM. If TRUE, create a cofactor list that is safe to use with the dominance scan mode of MQM. See mqmscan for details. If TRUE, plots a genetic map displaying the selected markers as cofactors. If TRUE, give verbose output. Value A list of cofactors to be used with mqmscan. Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • • • • • • • • • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf MQM - MQM description and references mqmscan - Main MQM single trait analysis mqmscanall - Parallellized traits analysis mqmaugment - Augmentation routine for estimating missing data mqmautocofactors - Set cofactors using marker density mqmsetcofactors - Set cofactors at fixed locations mqmpermutation - Estimate significance levels scanone - Single QTL scanning Examples data(hyper) # hyper dataset hyperfilled <- fill.geno(hyper) cofactors <- mqmautocofactors(hyperfilled,15) # Set 15 Cofactors result <- mqmscan(hyperfilled,cofactors) # Backward model selection mqmgetmodel(result) mqmextractmarkers mqmextractmarkers 119 MQM marker extraction Description Extract the real markers from a cross object that includes pseudo markers Usage mqmextractmarkers(mqmresult) Arguments mqmresult result from mqmscan, including pseudo markers Value Returns a scanone object with the pseudo markers removed Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(multitrait) multitrait <- fill.geno(multitrait) result <- mqmscan(multitrait) newresult <- mqmextractmarkers(result) 120 mqmfind.marker mqmfind.marker Fetch significant markers after permutation analysis Description Fetch significant makers after permutation analysis. These markers can be used as cofactors for model selection in a forward stepwise approach. Usage mqmfind.marker(cross, mqmscan = NULL, perm = NULL, alpha = 0.05, verbose=FALSE) Arguments cross An object of class cross. See read.cross for details. mqmscan Results from either scanone or mqmscan perm a scanoneperm object alpha Threshold value, everything with significance < alpha is reported verbose Display more output on verbose=TRUE Value returns a matrix with at each row a significant marker (determined from the scanoneperm object) and with columns: markername, chr and pos (cM) Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • mqmprocesspermutation - Function called to convert results from an mqmpermutation into an scanoneperm object • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning mqmgetmodel 121 Examples # Use the multitrait dataset data(multitrait) # Set cofactors at each 3th marker cof <- mqmsetcofactors(multitrait,3) # impute missing genotypes multitrait <- fill.geno(multitrait) # log transform the 7th phenotype multitrait <- transformPheno(multitrait, 7) # Bootstrap 50 runs in batches of 10 ## Not run: result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof, pheno.col=7,n.perm=50,batchsize=10) ## End(Not run) # Create a permutation object f2perm <- mqmprocesspermutation(result) # What LOD score is considered significant ? summary(f2perm) # Find markers with a significant QTL effect (First run is original phenotype data) marker <- mqmfind.marker(multitrait,result[[1]],f2perm) # Print it to the screen marker mqmgetmodel Retrieve the QTL model used in mapping from the results of an MQM scan Description Retrieves the QTL model used for scanning from the output of an MQM scan. The model only contains the selected cofactors significant at the specified cofactor.significance from the results of an mqm scan Usage mqmgetmodel(scanresult) Arguments scanresult An object returned by mqmscan, including cofactors and QTL model. 122 mqmpermutation Value The function returns the multiple QTL model created, which consists of the cofactors selected during the modeling phase of the algorithm. This model was used when scanning for additional QTL in the mqmscan function. The format of the model is compatible with the makeqtl function. For more information about the format of the model see the makeqtl page. When no cofactor was selected in the modeling phase no model was created, then this function will return a NULL value. Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • mqmsetcofactors - Setting multiple cofactors for backward elimination • makeqtl - Make a qtl object • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(hyper) hyperfilled <- fill.geno(hyper) cofactors <- mqmsetcofactors(hyperfilled,4) result <- mqmscan(hyperfilled,cofactors) mqmgetmodel(result) plot(mqmgetmodel(result)) mqmpermutation Estimate QTL LOD score significance using permutations or simulations Description Two randomization approaches to obtain estimates of QTL significance: • Random redistribution of traits (method=’permutation’) • Random redistribution of simulated trait values (method=’simulation’) Calculations can be parallelized using the SNOW package. mqmpermutation 123 Usage mqmpermutation(cross, scanfunction=scanone, pheno.col=1, multicore=TRUE, n.perm=10, batchsize=10, file="MQM_output.txt", n.cluster=1, method=c("permutation","simulation"), cofactors=NULL, plot=FALSE, verbose=FALSE, ...) Arguments cross scanfunction pheno.col multicore n.perm batchsize file n.cluster method cofactors plot verbose ... An object of class cross. See read.cross for details. Function to use when mappingQTL’s (either scanone,cim or mqm) Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers. Use multicore (if available) Number of permutations to perform (DEFAULT=10, should be 1000, or higher, for publications) Batch size. The entire set is split in jobs. Each job contains b.size number of traits per job Name of the intermediate output file used Number of child processes to split the job into What kind permutation should occur: permutation or simulation cofactors, only used when scanfunction is mqm. List of cofactors to be analysed in the QTL model. To set cofactors use mqmautocofactors or mqmsetcofactors. If TRUE, make a plot If TRUE, print tracing information Parameters passed through to the scanone, cim or mqmscan functions Details Analysis of scanone, cim or mqmscan to scan for QTL in shuffled/randomized data. It is recommended to also install the snow library. The snow library allows calculations to run on multiple cores or even scale it up to an entire cluster, thus speeding up calculation. Value Returns a mqmmulti object. this object is a list of scanone objects that can be plotted using plot.scanone(result[[trait]]) Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman References • Bruno M. Tesson, Ritsert C. Jansen (2009) Chapter 3.7. Determining the significance threshold eQTL Analysis in Mice and Rats 1, 20–25 • Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. • Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing. R. UW Biostatistics working paper series University of Washington. 193 • Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. Version 0.2-1. 124 mqmplot.circle See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples # Use the multitrait dataset data(multitrait) multitrait <- calc.genoprob(multitrait) result <- mqmpermutation(multitrait,pheno.col=7, n.perm=2, batchsize=2) ## Not run: #Set 50 cofactors cof <- mqmautocofactors(multitrait,50) ## End(Not run) multitrait <- fill.geno(multitrait) result <- mqmpermutation(multitrait,scanfunction=mqmscan,cofactors=cof, pheno.col=7, n.perm=2,batchsize=2,verbose=FALSE) #Create a permutation object f2perm <- mqmprocesspermutation(result) #Get Significant LOD thresholds summary(f2perm) mqmplot.circle Circular genome plot for MQM Description Circular genome plot - shows QTL locations and relations. Usage mqmplot.circle(cross,result,highlight=0,spacing=25, interactstrength=2, axis.legend=TRUE, col.legend=FALSE, verbose=FALSE, transparency=FALSE) mqmplot.circle 125 Arguments cross An object of class cross with optionally phenotype locations. See read.cross for details on reading in cross objects, and optionally addloctocross for adding phenotype locations. result An object of class mqmmulti or scanone. See mqmscanall scanone for details. highlight With a mqmmulti object, highlight this phenotype (value between one and the number of results in the mqmmultiobject) interactstrength When highlighting a trait, consider interactions significant they have a change of more than interactstrength*SEs. A higher value will show less interactions. However the interactions reported at higher interactstrength values will generaty be more reliable. spacing User defined spacing between chromosomes in cM axis.legend When set to FALSE, suppresses the legends. (defaults to plotting legends besides the axis. col.legend With a mqmmulti object, plots a legend for the non-highlighed version transparency Use transparency when drawing the plots (defaults to no transparency) verbose Be verbose Details Depending on the input of the result being either scanone or mqmmulti a different plot is drawn. If model information is present from mqmscan (by setting cofactors) This will be highlighted in red (see example). If phenotypes have genetic locations (e.g. eQTL) they will be plotted on the genome otherwise phenotypes will be plotted in the middle of the circle (with a small offset) Locations can be added by using the addloctocross function. Value Plotting routine, no return Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning 126 mqmplot.cistrans Examples data(multitrait) data(locations) multifilled <- fill.geno(multitrait) multicof <- mqmsetcofactors(multitrait,10) multiloc <- addloctocross(multifilled,locations) # multires <- mqmscanall(multifilled,cofactors=multicof) # impute missing genotypes # create cofactors add phenotype information to cross # run mqmscan for all phenotypes #Basic mqmmulti, color = trait, round circle = significant mqmplot.circle(multifilled,multires) #mqmmulti with locations of traits in multiloc mqmplot.circle(multiloc,multires) #mqmmulti with highlighting mqmplot.circle(multitrait,multires,highlight=3) #mqmmulti with locations of traits in multiloc and highlighting mqmplot.circle(multiloc,multires,highlight=3) mqmplot.cistrans cis-trans plot Description Plot results for a genomescan using a multiple-QTL model. With genetic location for the traits it is possible to show cis- and trans- locations, and detect trans-bands Usage mqmplot.cistrans(result, cross, threshold=5, onlyPEAK=TRUE, highPEAK=FALSE, cisarea=10, pch=22, cex=0.5, verbose=FALSE, ...) Arguments result An object of class mqmmulti. See mqmscanall for details. cross An object of class cross. See read.cross for details. threshold Threshold value in LOD, Markers that have a LOD score above this threshold are plotted as small squares (see pch parameter). The markers with LODscores below this threshold are not visible onlyPEAK Plot only the peak markers ? (TRUE/FALSE) (Peak markers are markers that have a QTL likelihood above threshold and higher than other markers in the same region) highPEAK Highlight peak markers ? (TRUE/FALSE). When using this option peak markers (the marker with the highest LOD score in a region above the threshold gets an 25% increase in size and is displayed in red) cisarea Adjust the two green lines around the line y=x mqmplot.clusteredheatmap pch cex verbose ... What kind of character is used in plotting of the figure (Default: 22, small square) Size of the points plotted (default to 0.5 half of the original size) If TRUE, give verbose output Extra parameters will be passed to points Value Plotting routine, so no return Author(s) Danny Arends See Also • • • • • • • • • 127 The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf MQM - MQM description and references mqmscan - Main MQM single trait analysis mqmscanall - Parallellized traits analysis mqmaugment - Augmentation routine for estimating missing data mqmautocofactors - Set cofactors using marker density mqmsetcofactors - Set cofactors at fixed locations mqmpermutation - Estimate significance levels scanone - Single QTL scanning Examples data(multitrait) data(locations) multiloc <- addloctocross(multitrait,locations) multiloc <- calc.genoprob(multiloc) results <- scanall(multiloc, method="hk") mqmplot.cistrans(results, multiloc, 5, FALSE, TRUE) mqmplot.clusteredheatmap Plot clustered heatmap of MQM scan on multiple phenotypes Description Plot the results from a MQM scan on multiple phenotypes. Usage mqmplot.clusteredheatmap(cross, mqmresult, directed=TRUE, legend=FALSE, Colv=NA, scale="none", verbose=FALSE, breaks = c(-100,-10,-3,0,3,10,100), col = c("darkblue","blue","lightblue","yellow", "orange","red"), ...) 128 mqmplot.clusteredheatmap Arguments cross An object of class cross. See read.cross for details. mqmresult Result object from mqmscanall, the object needs to be of class mqmmulti directed Take direction of QTLs into account (takes more time because of QTL direction calculations legend If TRUE, add a legend to the plot Colv Cluster only the Rows, the columns (Markers) should not be clustered scale character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. The default "none" verbose If TRUE, give verbose output. breaks Color break points for the LOD scores col Colors used between breaks ... Additional arguments passed to heatmap. Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(multitrait) multitrait <- fill.geno(multitrait) # impute missing genotype data result <- mqmscanall(multitrait, logtransform=TRUE) cresults <- mqmplot.clusteredheatmap(multitrait,result) groupclusteredheatmap(multitrait,cresults,10) mqmplot.cofactors mqmplot.cofactors 129 Plot cofactors on the genetic map Description Plots cofactors as created by mqmsetcofactors or mqmautocofactors on the genetic map. Usage mqmplot.cofactors(cross,cofactors, ...) Arguments cross An object of class cross. See read.cross for details. cofactors List of cofactors to be analysed in the QTL model. To set cofactors use mqmautocofactors or mqmsetcofactors. ... Passed to plot.qtl Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(multitrait) cof1 <- mqmsetcofactors(multitrait,20) cof2 <- mqmsetcofactors(multitrait,10) op <- par(mfrow=c(2,1)) mqmplot.cofactors(multitrait,cof1,col="blue") mqmplot.cofactors(multitrait,cof2,col="blue") op <- par(mfrow=c(1,1)) 130 mqmplot.directedqtl mqmplot.directedqtl Plot LOD*Effect curves of a multiple-QTL model Description Plot the LOD*Effect curve for a genome scan with a multiple-QTL model (the output of mqmscan). Usage mqmplot.directedqtl(cross, mqmresult, pheno.col=1, draw = TRUE) Arguments cross An object of class cross. See read.cross for details. mqmresult Results from mqmscan of type scanone pheno.col From which phenotype in the crossobject are the result calculated draw If TRUE, draw the figure. Value Returns a scanone object, with added the effectsign calculated internally by the function effect.scan. For more info on the scanone object see: scanone Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples #Simulated F2 Population f2qtl <- c(3,15,1,0) data(map10) f2cross <- sim.cross(map10,f2qtl,n=100,type="f2") f2cross <- fill.geno(f2cross) f2result <- mqmscan(f2cross) mqmplot.directedqtl(f2cross,f2result) # QTL at chromosome 3 # Mouse genetic map # Simulate a F2 Cross # Fill in missing genotypes # Do a MQM scan of the genome mqmplot.heatmap mqmplot.heatmap 131 Heatmap of a genome of MQM scan on multiple phenotypes Description Plotting routine to display a heatmap of results obtained from a multiple-QTL model on multiple phenotypes (the output of mqmscanall) Usage mqmplot.heatmap(cross, result, directed=TRUE, legend=FALSE, breaks = c(-100,-10,-3,0,3,10,100), col = c("darkblue","blue","lightblue","yellow","orange","red"), ...) Arguments cross An object of class cross. See read.cross for details. result Result object from mqmscanall, the object needs to be of class mqmmulti directed Take direction of QTLs into account (takes more time because of QTL direction calculations legend If TRUE, add a legend to the plot breaks Color break points for the LOD scores col Colors used between breaks ... Additional arguments passed to the image function Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(multitrait) multitrait <- fill.geno(multitrait) # impute missing genotype data result <- mqmscanall(multitrait, logtransform=TRUE) mqmplot.heatmap(multitrait,result) 132 mqmplot.multitrait mqmplot.multitrait Plot the results from a genomescan using a multiple-QTL model on multiple phenotypes Description Plotting routine to display the results from a multiple-QTL model on multiple phenotypes. It supports four different visualizations: a contourmap, heatmap, 3D graph or a multiple QTL plot created by using plot.scanone on the mqmmulti object Usage mqmplot.multitrait(result, type=c("lines","image","contour","3Dplot"), group=NULL, meanprofile=c("none","mean","median"), theta=30, phi=15, ...) Arguments result Result object from mqmscanall type Selection of the plot method to visualize the data: "lines" (defaut plotting option), "image", "contour" and "3Dplot" group A numeric vector indicating which traits to plot. NULL means no grouping meanprofile Plot a mean/median profile from the group selected theta Horizontal axis rotation in a 3D plot phi Vertical axis rotation in a 3D plot ... Additional arguments passed to plot. Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning mqmplot.permutations 133 Examples data(multitrait) multitrait <- fill.geno(multitrait) # impute missing genotype data result <- mqmscanall(multitrait, logtransform=TRUE) mqmplot.multitrait(result,"lines") mqmplot.multitrait(result,"contour") mqmplot.multitrait(result,"image") mqmplot.multitrait(result,"3Dplot") mqmplot.permutations Plot results from mqmpermutation Description Plotting routine to display the results from a permutation QTL scan. (the output of mqmpermutation) Usage mqmplot.permutations(permutationresult, ...) Arguments permutationresult mqmmulti object returned by mqmpermutation permutation analysis. ... Extra arguments passed to polyplot Value No value returned (plotting routine) Author(s) Danny Arends , Rutger Brouwer See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning 134 mqmplot.singletrait Examples # Simulated F2 Population # QTL at chromosome 3 f2qtl <- c(3,15,1,0) # Mouse genetic map data(map10) # Simulate a F2 Cross f2cross <- sim.cross(map10,f2qtl,n=100,type="f2") f2cross <- calc.genoprob(f2cross) ## Not run: # Permutations to obtain significance threshold f2result <- mqmpermutation(f2cross, n.perm=1000, method="permutation") ## End(Not run) # Plot results mqmplot.permutations(f2result) mqmplot.singletrait Plot LOD curves of a multiple-QTL model Description Plot the LOD curve for a genome scan for a single trait, with a multiple-QTL model (the output of mqmscan). Usage mqmplot.singletrait(result, extended = 0 ,...) Arguments result mqmscan result. extended Extended plotting of the information content ... Extra arguments passed to plot.scanone Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density mqmprocesspermutation 135 • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples #Simulated F2 Population f2qtl <- c(3,15,1,0) data(map10) # QTL at chromosome 3 # Mouse genetic map f2cross <- sim.cross(map10,f2qtl,n=100,type="f2") # Simulate a F2 Cross f2cross <- mqmaugment(f2cross) f2result <- mqmscan(f2cross) # Do a MQM scan of the genome mqmplot.singletrait(f2result) # Use our fancy plotting routine mqmprocesspermutation Convert mqmmulti objects into a scanoneperm object Description Function to convert mqmmulti objects into a scanoneperm object, this allows the use of R/qtl methods for permutation analysis that do not support the output of a multiple QTL scan using mqm’s outputstructure. Usage mqmprocesspermutation(mqmpermutationresult = NULL) Arguments mqmpermutationresult mqmmulti object obtained after performing permutations on a single trait.using the function mqmpermutation Value Output of the algorithm is a scanoneperm object. See also: summary.scanoneperm Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • • • • • • • • • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf MQM - MQM description and references mqmscan - Main MQM single trait analysis mqmscanall - Parallellized traits analysis mqmaugment - Augmentation routine for estimating missing data mqmautocofactors - Set cofactors using marker density mqmsetcofactors - Set cofactors at fixed locations mqmpermutation - Estimate significance levels scanone - Single QTL scanning 136 mqmscan Examples # QTL at chromosome 3 f2qtl <- c(3,15,1,0) # Mouse genetic map data(map10) # Simulate a F2 Cross f2cross <- sim.cross(map10,f2qtl,n=100,type="f2") ## Not run: # Bootstrap MQM mapping on the f2cross f2result <- mqmpermutation(f2cross,scanfunction=mqmscan) ## End(Not run) # Create a permutation object f2perm <- mqmprocesspermutation(f2result) # What LOD score is considered significant? summary(f2perm) mqmscan Genome scan with a multiple QTL model (MQM) Description Genome scan with a multiple QTL model. Usage mqmscan(cross, cofactors=NULL, pheno.col = 1, model=c("additive","dominance"), forceML=FALSE, cofactor.significance=0.02, em.iter=1000, window.size=25.0, step.size=5.0, logtransform = FALSE, estimate.map = FALSE, plot=FALSE, verbose=FALSE, outputmarkers=TRUE, multicore=TRUE, batchsize=10, n.clusters=1, test.normality=FALSE,off.end=0 ) Arguments cross An object of class cross. See read.cross for details. cofactors List of cofactors to be analysed as cofactors in backward elimination procedure when building the QTL model. See mqmsetcofactors on how-to manually set cofactors for backward elimination. Or use mqmautocofactors for automatic selection of cofactors. Only three kind of (integer) values are allowed in the cofactor list. (0: no cofactor at this marker, 1: Use this marker as an additive cofactor, 2: Use this marker as an sexfactor (Dominant cofactor)) pheno.col Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers; One may also give a character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypeIDs. This should consist of integers with 0 < value < no. phenotypes. mqmscan model 137 When scanning for QTLs should haplotype dominance be considered in an F2 intercross. Using the dominance model we scan for additive effects but also allow an additional effect where AA+AB versus BB and AA versus AB+BB. This setting is ignored for BC and RIL populations forceML Specify which statistical method to use to estimate variance components to use when QTL modeling and mapping. Default usage is the Restricted maximum likelihood approach (REML). With this option a user can disable REML and use maximum likelihood. cofactor.significance Significance level at which a cofactor is considered significant. This is estimated using an analysis of deviance, and compared to the level specified by the user. The cofactors that dont reach this level of statistical significance are NOT used in the mapping stage. Value between 0 and 1 em.iter Maximum number of iterations for the EM algorithm to converge window.size Window size for mapping QTL locations, this parameter is used in the interval mapping stage. When calculating LOD scores at a genomic position all cofactors within window.size are dropped to estimate the (unbiased) effect of the location under interest. step.size Step size used in interval mapping. A lower step.size parameter increases the number of output points, this creates a smoother QTL profile off.end Distance (in cM) past the terminal markers on each chromosome to which the genotype simulations will be carried. logtransform Indicate if the algorithm should do a log transformation on the trait data in the pheno.col estimate.map Should Re-estimation of the marker locations on the genetic map occur before mapping QTLs. This method is deprecated rather use the est.map function in R/qtl. This is because no map is returned into the crossobject. The old map remains in the cross object. plot plot the results (default FALSE) verbose verbose output outputmarkers Needs to be explained multicore Use multicore (if available) batchsize Needs to be explained n.clusters Number of child processes to split the job into. test.normality If TRUE, test whether the phenotype follows a normal distribution via mqmtestnormal. Value When scanning a single phenotype the function returns a scanone object. The object contains a matrix of three columns for LOD scores, information content and LOD*information content with pseudo markers sorted in increasing order. For more information on the scanone object see: scanone Note The resulting scanone object itself can be visualized using the standard R/qtl plotting routines (plot.scanone) or specialized function to show the mqm model (mqmplot.singletrait) and QTL profile. If cofactors were specified the QTL model used in scanning is also returned as a 138 mqmscanall named attribute of the scanone object called mqmmodel. It can be extracted from the resulting scanone object by using the mqmgetmodel function or the attr function. Also note the estimate.map parameter does not return its re-estimated genetic map, altough it is used internally. When scanning multiple genotypes a mqmmulti object is created. This object is just a list composed of scanone objects. The results for a single trait can be obtained from the mqmmulti object, in scanone format. Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(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) mqmscanall Parallelized MQM on multiple phenotypes in a cross object Description Parallelized QTL analysis using MQM on multiple phenotypes in a cross object (uses SNOW) mqmscanall 139 Usage mqmscanall(cross, multicore=TRUE, n.clusters = 1,batchsize=10,cofactors=NULL, ...) Arguments cross An object of class cross. See read.cross for details. multicore Use multiple cores (only if the package SNOW is available, otherwise this setting will be ignored) n.clusters Number of parallel processes to spawn, recommended is setting this lower than the number of cores in the computer batchsize Batch size. The entire set is split in jobs to reduce memory load per core. Each job contains batchsize number of traits per job. cofactors cofactors, only used when scanfunction is mqmscan. List of cofactors to be analysed in the QTL model. To set cofactors use mqmautocofactors or mqmsetcofactors. ... Parameters passed through to the mqmscan function used in scanning for QTLs Details Uses mqmscan to scan for QTL’s for each phenotype in the cross object. It is recomended that the package SNOW is installed before using this function on large numbers of phenotypes. Value Returns a MQMmulti object. This object is a list of scanone objects that can be plotted using plot.scanone(result[[trait]]) or using mqmplot.multitrait(result) Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman References • Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing. R. UW Biostatistics working paper series University of Washington. 193 • Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. Version 0.2-1. See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning 140 mqmscanfdr Examples #Doing a multitrait analysis data(multitrait) multitrait <- calc.genoprob(multitrait) cof <- mqmsetcofactors(multitrait,3) multitrait <- fill.geno(multitrait) result <- mqmscanall(multitrait,cofactors=cof,batchsize=5) mqmplot.multitrait(result,"lines") mqmscanfdr Estimate FDR for multiple trait QTL analysis Description Estimate the false discovery rate (FDR) for multiple trait analysis Usage mqmscanfdr(cross, scanfunction=mqmscanall, thresholds=c(1,2,3,4,5,7,10,15,20), n.perm=10, verbose=FALSE, ... ) Arguments cross An object of class cross. See read.cross for details. scanfunction QTL mapping function, Note: Must use scanall or mqmscanall. Otherwise this will not produce usefull results. Reason: We need a function that maps all traits ecause of the correlation structure which is not changed (between traits) during permutation (Valis options: scanall or mqmscanall) thresholds False discovery rate (FDR) is calculated for peaks above these LOD thresholds (DEFAULT=Range from 1 to 20, using 10 thresholds) Parameter is a list of LOD scores at which FDR is calculated. n.perm Number of permutations (DEFAULT=10 for quick analysis, however for publications use 1000, or higher) verbose verbose output ... Parameters passed to the mapping function Details This function wraps the analysis of scanone, cim and mqmscan to scan for QTL in shuffled/randomized data. It is recommended to also install the snow library for parallelization of calculations. The snow library allows calculations to run on multiple cores or even scale it up to an entire cluster, thus speeding up calculation by the number of computers used. Value Returns a data.frame with 3 columns: FalsePositives, FalseNegatives and False Discovery Rates. In the rows the userspecified thresholds are with scores for the 3 columns. mqmsetcofactors 141 Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman References • Bruno M. Tesson, Ritsert C. Jansen (2009) Chapter 3.7. Determining the significance threshold eQTL Analysis in Mice and Rats 1, 20–25 • Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. • Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing. R. UW Biostatistics working paper series University of Washington. 193 • Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. Version 0.2-1. See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(multitrait) # impute missing genotype data multitrait <- fill.geno(multitrait) ## Not run: # Calculate the thresholds result <- mqmscanfdr(multitrait, threshold=10.0, n.perm=1000) ## End(Not run) mqmsetcofactors Set cofactors at fixed intervals, to be used with MQM Description Set cofactors, at fixed marker intervals. Together with mqmscan cofactors are selected through backward elimination. Usage mqmsetcofactors(cross, each = NULL, cofactors=NULL, sexfactors=NULL, verbose=FALSE) 142 mqmsetcofactors Arguments cross An object of class cross. See read.cross for details. each Every ’each’ marker will be used as a cofactor, when each is used the cofactors and sexfactors parameter is ignored cofactors List of cofactors to be analysed in the QTL model. To set cofactors use mqmautocofactors or mqmsetcofactors; when each is set, this parameter is ignored sexfactors list of markers which should be treated as dominant cofactors (sexfactors), when each is set, this parameter is ignored verbose If TRUE, print tracing information. Value An list of cofactors to be passed into mqmscan. Author(s) Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(hyper) # Hyper dataset hyperfilled <- fill.geno(hyper) # Automatic cofactors every third marker cofactors <- mqmsetcofactors(hyperfilled,3) result <- mqmscan(hyperfilled,cofactors) # Backward model selection mqmgetmodel(result) #Manual cofactors at markers 3,6,9,12,40 and 60 cofactors <- mqmsetcofactors(hyperfilled,cofactors=c(3,6,9,12,40,60)) result <- mqmscan(hyperfilled,cofactors) # Backward model selection mqmgetmodel(result) mqmtestnormal mqmtestnormal 143 Shapiro normality test used for MQM Description Wraps a shapiro’s normality test from the nortest package. This function is used in MQM to test the normality of the trait under investigation Usage mqmtestnormal(cross, pheno.col = 1,significance=0.05, verbose=FALSE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers. significance Significance level used in the normality test. Lower significance levels will accept larger deviations from normality. verbose If TRUE, print result as well as return it. Details For augmented data (as from mqmaugment), the cross is first reduced to distinct individuals. Furthermore the shapiro used to test normality works only for 3 <= nind(cross) <= 5000 Value Boolean indicating normality of the trait in pheno.col. (FALSE when not normally distributed.) Author(s) Danny Arends See Also • shapiro.test - Function wrapped by our mqmtestnormal • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning 144 multitrait Examples data(multitrait) # test normality of 7th phenotype mqmtestnormal(multitrait, pheno.col=7) # take log multitrait <- transformPheno(multitrait, pheno.col=7, transf=log) # test again mqmtestnormal(multitrait, pheno.col=7) multitrait Example Cross object from R/QTL with multiple traits Description Cross object from R/QTL, an object of class cross from R/QTL. See read.cross for details. Usage data(multitrait) Format Cross object from R/QTL Details Arabidopsis recombinant inbred lines by selfing. There are 162 lines, 24 phenotypes, and 117 markers on 5 chromosomes. Source Part of the Arabidopsis RIL selfing experiment with Landsberg erecta (Ler) and Cape Verde Islands (Cvi) with 162 individuals scored (with errors at) 117 markers. Dataset obtained from GBIC Groningen BioInformatics Centre References • Keurentjes, J. J. and Fu, J. and de Vos, C. H. and Lommen, A. and Hall, R. D. and Bino, R. J. and van der Plas, L. H. and Jansen, R. C. and Vreugdenhil, D. and Koornneef, M. (2006), The genetics of plant metabolism. Nature Genetics. 38-7, 842–849. • Alonso-Blanco, C. and Peeters, A. J. and Koornneef, M. and Lister, C. and Dean, C. and van den Bosch, N. and Pot, J. and Kuiper, M. T. (1998), Development of an AFLP based linkage map of Ler, Col and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population . Plant J. 14(2), 259–271. nchr 145 Examples data(multitrait) # Load dataset multitrait <- fill.geno(multitrait) # impute missing genotype data result <- mqmscanall(multitrait, logtransform=TRUE) # Analyse all 24 traits nchr Determine the number of chromosomes Description Determine the number of chromosomes in a cross or map object. Usage nchr(object) Arguments object An object of class cross (see read.cross for details) or map (see sim.map for details). Value The number of chromosomes in the input. Author(s) Karl W Broman, See Also read.cross, plot.cross, summary.cross, nind, totmar, nmar, nphe Examples data(fake.f2) nchr(fake.f2) map <- pull.map(fake.f2) nchr(map) 146 nmar nind Determine the number of individuals QTL experiment Description Determine the number of individuals in cross object. Usage nind(object) Arguments object An object of class cross. See read.cross for details. Value The number of individuals in the input cross object. Author(s) Karl W Broman, See Also read.cross, plot.cross, summary.cross, nmar, nchr, totmar, nphe Examples data(fake.f2) nind(fake.f2) nmar Determine the numbers of markers on each chromosome Description Determine the number of markers on each chromosome in a cross or map object. Usage nmar(object) Arguments object An object of class cross (see read.cross for details) or map (see sim.map for details). Value A vector with the numbers of markers on each chromosome in the input. nmissing 147 Author(s) Karl W Broman, See Also read.cross, plot.cross, summary.cross, nind, nchr, totmar, nphe Examples data(fake.f2) nmar(fake.f2) map <- pull.map(fake.f2) nmar(map) nmissing Number of missing genotypes Description Count the number of missing genotypes for each individual or each marker in a cross. Usage nmissing(cross, what=c("ind","mar")) Arguments cross An object of class cross. See read.cross for details. what Indicates whether to count missing genotypes for each individual or each marker. Value A vector containing the number of missing genotypes for each individual or for each marker. Author(s) Karl W Broman, See Also ntyped, summary.cross, nind, totmar Examples data(listeria) # plot number of missing genotypes for each individual plot(nmissing(listeria)) # plot number of missing genotypes for each marker plot(nmissing(listeria, what="mar")) 148 nqrank nphe Determine the number of phenotypes QTL experiment Description Determine the number of phenotypes in cross object. Usage nphe(object) Arguments object An object of class cross. See read.cross for details. Value The number of phenotypes in the input cross object. Author(s) Karl W Broman, See Also read.cross, plot.cross, summary.cross, nmar, nchr, totmar, nind Examples data(fake.f2) nphe(fake.f2) nqrank Transform a vector of quantitative values to the corresponding normal quantiles Description Transform a vector of quantitative values to the corresponding normal quantiles (preserving the mean and SD). Usage nqrank(x, jitter) Arguments x A numeric vector jitter If TRUE, randomly jitter the values to break ties. nqtl 149 Value A numeric vector; the input x is converted to ranks and then to normal quantiles. Author(s) Karl W Broman, See Also rank, qnorm, transformPheno Examples data(hyper) hyper <- transformPheno(hyper, pheno.col=1, transf=nqrank) nqtl Determine the number of QTL in a QTL object Description Determine the number of QTL in a QTL object. Usage nqtl(qtl) Arguments qtl An object of class qtl. See makeqtl for details. Value The number of QTL in the input QTL object. Author(s) Karl W Broman, See Also makeqtl, fitqtl, dropfromqtl, replaceqtl, addtoqtl, summary.qtl, reorderqtl 150 ntyped Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c("1", "6", "13") qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0) qtl <- makeqtl(fake.f2, qc, qp, what="prob") nqtl(qtl) ntyped Number of genotypes Description Count the number of genotypes for each individual or each marker in a cross. Usage ntyped(cross, what=c("ind","mar")) Arguments cross An object of class cross. See read.cross for details. what Indicates whether to count genotypes for each individual or each marker. Value A vector containing the number of genotypes for each individual or for each marker. Author(s) Karl W Broman, See Also nmissing, summary.cross, nind, totmar Examples data(listeria) # plot number of genotypes for each individual plot(ntyped(listeria)) # plot number of genotypes for each marker plot(ntyped(listeria, what="mar")) nullmarkers 151 nullmarkers Identify markers without any genotype data Description Identify markers in a cross that have no genotype data. Usage nullmarkers(cross) Arguments cross An object of class cross. See read.cross for details. Value Marker names (a vector of character strings) with no genotype data. Author(s) Karl W Broman, See Also drop.nullmarkers Examples # one marker with no data data(hyper) nullmarkers(hyper) # nothing in listeria data(listeria) nullmarkers(listeria) orderMarkers Find an initial order for markers within chromosomes Description Establish initial orders for markers within chromosomes by a greedy algorithm, adding one marker at a time with locations of previous markers fixed, in the position giving the miniminum number of obligate crossovers. Usage orderMarkers(cross, chr, window=7, use.ripple=TRUE, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), maxit=4000, tol=1e-4, sex.sp=TRUE, verbose=FALSE) 152 orderMarkers Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. window If use.ripple=TRUE, this indicates the number of markers to include in the sliding window of permuted markers. Larger numbers result in the comparison of a greater number of marker orders, but will require a considerable increase in computation time. use.ripple If TRUE, the initial order is refined by a call to the function ripple. error.prob Assumed genotyping error rate used in the final estimated map. map.function Indicates the map function to use in the final estimated map. maxit Maximum number of EM iterations to perform in the final estimated map. tol Tolerance for determining convergence in the final estimated map. sex.sp Indicates whether to estimate sex-specific maps in the final estimated map; this is used only for the 4-way cross. verbose If TRUE, information about the progress of the calculations is displayed; if > 1, even more information is given. Details Markers within a linkage group are considered in order of decreasing number of genotyped individuals. The first two markers are placed in an arbitrary order. Additional markers are considered one at a time, and each possible placement of a marker is compared (with the order of the previously placed markers taken as fixed) via the number of obligate crossovers (that is, the minimal number of crossovers that would explain the observed data). The marker is placed in the position giving the minimal number of obligate crossovers. If multiple positions give the same number of obligate crossovers, a single location (among those positions) is chosen at random. If use.ripple=TRUE, the final order is passed to ripple with method="countxo" to refine the marker order. If use.ripple=TRUE and the number of markers on a chromosome is ≤ the argument window, the initial greedy algorithm is skipped and all possible marker orders are compared via ripple. Value The output is a cross object, as in the input, with orders of markers on selected chromosomes revised. Author(s) Karl W Broman, See Also formLinkageGroups, ripple, est.map, countXO phenames 153 Examples data(listeria) pull.map(listeria, chr=3) revcross <- orderMarkers(listeria, chr=3, use.ripple=FALSE) pull.map(revcross, chr=3) phenames Pull out the phenotypes names from a cross Description Pull out the phenotype names from a cross object as a vector. Usage phenames(cross) Arguments cross An object of class cross. See read.cross for details. Value A vector of character strings (the phenotype names). Author(s) Karl W Broman, See Also markernames, chrnames Examples data(listeria) phenames(listeria) 154 pickMarkerSubset pickMarkerSubset Identify the largest subset of markers that are some distance apart Description Identify the largest subset of markers for which no two adjacent markers are separated by less than some specified distance; if weights are provided, find the marker subset for which the sum of the weights is maximized. Usage pickMarkerSubset(locations, min.distance, weights) Arguments locations A vector of marker locations. min.distance Minimum distance between adjacent markers in the chosen subset. weights (Optional) vector of weights for the markers. If missing, we take weights == 1. Details Let di be the location of marker i, for i ∈ 1, . . . , M . We use the dynamic programming algorithm of Broman and Weber P (1999) to identify the subset of markers i1 , . . . , ik for which dij+1 − dij ≤ min.distance and wij is maximized. If there are multiple optimal subsets, we pick one at random. Value A vector of marker names. Author(s) Karl W Broman, References Broman, K. W. and Weber, J. L. (1999) Method for constructing confidently ordered linkage maps. Genet. Epidemiol., 16, 337–343. See Also drop.markers, pull.markers Examples data(hyper) # subset of markers on chr 4 spaced >= 5 cM pickMarkerSubset(pull.map(hyper)[[4]], 5) # no. missing genotypes at each chr 4 marker n.missing <- nmissing(subset(hyper, chr=4), what="mar") plot.cross 155 # weight by -log(prop'n missing), but don't let 0 missing go to +Inf wts <- -log( (n.missing+1) / (nind(hyper)+1) ) # subset of markers on chr 4 spaced >= 5 cM, with weights = -log(prop'n missing) pickMarkerSubset(pull.map(hyper)[[4]], 5, wts) plot.cross Plot various features of a cross object Description Plots grid of the missing genotypes, genetic map, and histograms or barplots of phenotypes for the data from an experimental cross. Usage ## S3 method for class 'cross' plot(x, auto.layout=TRUE, pheno.col, alternate.chrid=TRUE, ...) Arguments x An object of class cross. See read.cross for details. auto.layout If TRUE, par(mfrow) is set so that all plots fit within one figure. pheno.col Vector of numbers or character strings corresponding to phenotypes that should be plotted. If unspecified, all phenotypes are plotted. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Ignored at this point. Details Calls plotMissing, plotMap and plotPheno to plot the missing genotypes, genetic map, and histograms or barplots of all phenotypes. If auto.format=TRUE, par(mfrow) is used with ceiling(sqrt(n.phe+2)) rows and the minimum number of columns so that all plots fit on the plotting device. Numeric phenotypes are displayed as histograms or barplots by calling plotPheno. Value None. Author(s) Karl W Broman, ; Brian Yandell See Also plotMissing, plotMap, plotPheno 156 plot.qtl Examples data(fake.bc) plot(fake.bc) plot.qtl Plot QTL locations Description Plot the locations of the QTL against a genetic map Usage ## S3 method for class 'qtl' plot(x, chr, horizontal=FALSE, shift=TRUE, show.marker.names=FALSE, alternate.chrid=FALSE, justdots=FALSE, col="red", ...) Arguments x An object of class "qtl", as produced by makeqtl. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. horizontal Specifies whether the chromosomes should be plotted horizontally. shift If TRUE, shift the first marker on each chromosome to be at 0 cM. show.marker.names If TRUE, marker names are included. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. justdots If FALSE, just plot dots at the QTL, rather than arrows and QTL names. col Color used to plot indications of QTL ... Passed to plotMap. Details Creates a plot, via plotMap, and indicates the locations of the QTL in the input QTL object, x. Value None. Author(s) Karl W Broman, plot.rfmatrix 157 See Also plotMap, makeqtl Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c("1", "6", "13") qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") plot(qtl) plot(qtl, justdots=TRUE, col="seagreen") plot.rfmatrix Plot recombination fractions or LOD scores for a single marker Description Plot a slice (corresponding to a single marker) through the pairwise recombination fractions or LOD scores calculated by est.rf and extracted with pull.rf. Usage ## S3 method for class 'rfmatrix' plot(x, marker, ...) Arguments x An object of class rfmatrix, as output by pull.rf. marker A single marker name, as a character string. ... Optional arguments passed to plot.scanone. Value An object of class "scanone" (as output by scanone, and which may be summarized by summary.scanone or plotted with plot.scanone), containing the estimated recombination fractions or LOD scores for the input marker against all others. Author(s) Karl W Broman, See Also pull.rf, est.rf, plotRF 158 plot.scanone Examples data(fake.f2) fake.f2 <- est.rf(fake.f2) marker <- markernames(fake.f2, chr=5)[6] lod <- pull.rf(fake.f2, "lod") plot(lod, marker, bandcol="gray70") plot.scanone Plot LOD curves Description Plot the LOD curve for a genome scan with a single-QTL model (the output of scanone). Usage ## S3 method for class 'scanone' plot(x, x2, x3, chr, lodcolumn=1, incl.markers=TRUE, xlim, ylim, lty=1, col=c("black","blue","red"), lwd=2, add=FALSE, gap=25, mtick = c("line", "triangle"), show.marker.names=FALSE, alternate.chrid=FALSE, bandcol=NULL, type="l", cex=1, pch=1, bg="transparent", bgrect=NULL, ...) Arguments x An object of class "scanone", as output by scanone. x2 Optional second scanone object. x3 Optional third scanone object. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. lodcolumn An integer, or vector of 3 integers, indicating which of the LOD score columns should be plotted (generally this is 1). incl.markers Indicate whether to plot line segments at the marker locations. xlim Limits for x-axis (optional). ylim Limits for y-axis (optional). lty Line types; a vector of length 1 or 3. col Line colors; a vector of length 1 or 3. lwd Line widths; a vector of length 1 or 3. add If TRUE, add to a current plot. gap Gap separating chromosomes (in cM). mtick Tick mark type for markers (line segments or upward-pointing triangels). plot.scanone 159 show.marker.names If TRUE, show the marker names along the x axis. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. bandcol Optional color for alternating bands to indicate chromosomes. If NULL (the default), no bands are plotted. A good choice might be bandcol="gray70". type Type of plot (see plot): for example, type="l" for lines or type="p" for points only, may be of length 1 or 3. cex Point size expansion, for example if type="p" is used. May be of length 1 or 3. pch Point type, for example if type="p" is used. See points. May be of length 1 or 3. bg Background color for points, for example if type="p" and pch=21 are used. See points. May be of length 1 or 3. bgrect Optional background color for the rectangular plotting region. ... Passed to the function plot when it is called. Details This function allows you to plot the results of up to three genome scans against one another. Such objects must conform with each other. One may alternatively use the argument add to add the plot of an additional genome scan to the current figure, but some care is required: the same chromosomes should be selected, and the results must concern crosses with the same genetic maps. If a single scanone object containing multiple LOD score columns (for example, from different phenotypes) is input, up to three LOD curves may be plotted, by providing a vector in the argument lodcolumn. If multiple scanone objects are input (via x, x2 and x3), the LOD score columns to be plotted are chosen from the corresponding element of the lodcolumn argument. Value None. Author(s) Karl W Broman, See Also scanone, summary.scanone, par, colors, add.threshold, xaxisloc.scanone Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2,step=2.5) out.mr <- scanone(fake.f2, method="mr") out.em <- scanone(fake.f2, method="em") plot(out.mr) plot(out.mr, out.em, chr=c(1,13), lty=1, col=c("violetred","black")) out.hk <- scanone(fake.f2, method="hk") 160 plot.scanoneboot plot(out.hk, chr=c(1,13), add=TRUE, col="slateblue") plot(out.hk, chr=13, show.marker.names=TRUE) plot(out.hk, bandcol="gray70") # plot points rather than lines plot(out.hk, bandcol="gray70", type="p", cex=0.3, pch=21, bg="slateblue") plot.scanoneboot Plot results of bootstrap for QTL position Description Plot a histogram of the results of a nonparametric bootstrap to assess uncertainty in QTL position. Usage ## S3 method for class 'scanoneboot' plot(x, ...) Arguments x An object of class "scanoneboot", as output by scanoneboot. ... Passed to the function hist when it is called. Details The function plots a histogram of the bootstrap results obtained by scanoneboot. Genetic marker locations are displayed by vertical lines at the bottom of the plot. Value None. Author(s) Karl W Broman, See Also scanone, summary.scanoneboot Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=1) ## Not run: out.boot <- scanoneboot(fake.f2, chr=13, method="hk") summary(out.boot) plot(out.boot) plot.scanoneperm plot.scanoneperm 161 Plot permutation results for a single-QTL genome scan Description Plot a histogram of the permutation results from a single-QTL genome scan. Usage ## S3 method for class 'scanoneperm' plot(x, lodcolumn=1, ...) Arguments x An object of class "scanoneperm", as output by scanone when n.perm is specified. lodcolumn This indicates the LOD score column to plot. This should be a single number between 1 and the number of LOD columns in the object input. ... Passed to the function hist when it is called. Details The function plots a histogram of the permutation results obtained by scanone when n.perm is specified. If separate permutations were performed for the autosomes and the X chromosome (using perm.Xsp=TRUE), separate histograms are given. Value None. Author(s) Karl W Broman, See Also scanone, summary.scanoneperm Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc) operm <- scanone(fake.bc, method="hk", n.perm=100) plot(operm) 162 plot.scanPhyloQTL plot.scanPhyloQTL Plot LOD curves from single-QTL scan to map QTL to a phylogenetic tree Description Plot the LOD curves for each partition for a genome scan with a single diallelic QTL (the output of scanPhyloQTL). Usage ## S3 method for class 'scanPhyloQTL' plot(x, chr, incl.markers=TRUE, col, xlim, ylim, lwd=2, gap=25, mtick=c("line", "triangle"), show.marker.names=FALSE, alternate.chrid=FALSE, legend=TRUE, ...) Arguments x An object of class "scanPhyloQTL", as output by scanPhyloQTL. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. incl.markers Indicate whether to plot line segments at the marker locations. col Optional vector of colors to use for each partition. xlim Limits for x-axis (optional). ylim Limits for y-axis (optional). lwd Line width. gap Gap separating chromosomes (in cM). mtick Tick mark type for markers (line segments or upward-pointing triangels). show.marker.names If TRUE, show the marker names along the x axis. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. legend Indicates whether to include a legend in the plot. ... Passed to the function plot.scanone when it is called. Value None. Author(s) Karl W Broman, plot.scantwo 163 References Broman, K. W., Kim, S., An\’e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation. See Also scanPhyloQTL, max.scanPhyloQTL, summary.scanPhyloQTL, plot.scanone, inferredpartitions, simPhyloQTL, par, colors Examples ## Not run: # example map; drop X chromosome data(map10) map10 <- map10[1:19] # simulate data x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"), map=map10, n.ind=150, model=c(1, 50, 0.5, 0)) # run calc.genoprob on each cross x <- lapply(x, calc.genoprob, step=2) # scan genome, at each position trying all possible partitions out <- scanPhyloQTL(x, method="hk") # maximum peak max(out, format="lod") # approximate posterior probabilities at peak max(out, format="postprob") # all peaks above a threshold for LOD(best) - LOD(2nd best) summary(out, threshold=1, format="lod") # all peaks above a threshold for LOD(best), showing approx post'r prob summary(out, format="postprob", threshold=3) # plot of results plot(out) ## End(Not run) plot.scantwo Plot LOD scores for a two-dimensional genome scan Description Plot the results of a two-dimensional, two-QTL genome scan. 164 plot.scantwo Usage ## S3 method for class 'scantwo' plot(x, chr, incl.markers=FALSE, zlim, lodcolumn=1, lower = c("full", "add", "cond-int", "cond-add", "int"), upper = c("int", "cond-add", "cond-int", "add", "full"), nodiag=TRUE, contours=FALSE, main, zscale=TRUE, point.at.max=FALSE, col.scheme = c("viridis", "redblue","cm","gray","heat","terrain","topo"), gamma=0.6, allow.neg=FALSE, alternate.chrid=FALSE, ...) Arguments x An object of class "scantwo", as output by scantwo. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. incl.markers If FALSE, plot LOD scores on an evenly spaced grid (not including the results at the markers). zlim A vector of length 2 (optional), indicating the z limits for the lower-right and upper-left triangles, respectively. If one number is given, the same limits are used for both triangles. If zlim is missing, the maximum limits are used for each. lodcolumn If the scantwo results contain LOD scores for multiple phenotypes, this argument indicates which to use in the plot. lower Indicates which LOD scores should be plotted in the lower triangle. See the details below. upper Indicates which LOD scores should be plotted in the upper triangle. See the details below. nodiag If TRUE, suppress the plot of the scanone output (which is normally along the diagonal.) contours If TRUE, add a contour to the plot at 1.5-LOD below its maximum, using a call to contour. If a numeric vector, contours are drawn at these values below the maximum LOD. main An optional title for the plot. zscale If TRUE, a color scale is plotted at the right. point.at.max If TRUE, plot an X at the maximum LOD. col.scheme Name of color pallet. The default is "viridis"; see Option D at https://bids. github.io/colormap gamma Parameter affecting range of colors when col.scheme="gray" or ="redblue". allow.neg If TRUE, allow the plot of negative LOD scores; in this case, the z-limits are symmetric about 0. This option is chiefly to allow a plot of difference between LOD scores from different methods, calculated via -.scantwo. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Ignored at this point. plot.scantwo 165 Details Uses image to plot a grid of LOD scores. The particular LOD scores plotted in the upper-left and lower-right triangles are selected via upper and lower, respectively. By default, the upper-left triangle contains the epistasis LOD scores ("int"), and the lower-right triangle contains the LOD scores for the full model ("full"). The diagonal contains either all zeros or the main effects LOD scores (from scanone). The scantwo function calculates, for each pair of putative QTLs, (q1 , q2 ), the likelihood under the null model L0 , the likelihood under each of the single-QTL models, L(q1 ) and L(q2 ), the likelihood under an additive QTL model, La (q1 , q2 ), and the likelihood under a full QTL model (including QTL-QTL interaction), Lf (q1 , q2 ). The five possible LOD scores that may be plotted are the following. The epistasis LOD scores ("int") are LODi = log10 Lf (q1 , q2 ) − log10 La (q1 , q2 ). The full LOD scores ("full") are LODf = log10 Lf (q1 , q2 ) − log10 L0 . The additive LOD scores ("add") are LODa = log10 La (q1 , q2 ) − log10 L0 . In addition, we may calculate, for each pair of chromosomes, the difference between the full LOD score and the maximum single-QTL LOD scores for that pair of chromosomes ("cond-int"). Finally, we may calculate, for each pair of chromosomes, the difference between the additive LOD score and the maximum single-QTL LOD scores for that pair of chromosomes ("cond-add"). If a color scale is plotted (zscale=TRUE), the axis on the left indicates the scale for the upper-left triangle, while the axis on the right indicates the scale for the lower-right triangle. Note that the axis labels can get screwed up if you change the size of the figure window; you’ll need to redo the plot. Value None. Output of addpair Note that, for output from addpair in which the new loci are indicated explicitly in the formula, the summary provided by plot.scantwo is somewhat special. In particular, the lower and upper arguments are ignored. In the case that the formula used in addpair was not symmetric in the two new QTL, the x-axis in the plot corresponds to the first of the new QTL and the y-axis corresponds to the second of the new QTL. Author(s) Hao Wu; Karl W Broman, ; Brian Yandell See Also scantwo, summary.scantwo, plot.scanone, -.scantwo Examples data(hyper) hyper <- calc.genoprob(hyper, step=5) # 2-d scan by EM and by Haley-Knott regression 166 plot.scantwoperm out2.em <- scantwo(hyper, method="em") out2.hk <- scantwo(hyper, method="hk") # plot epistasis and full LOD scores plot(out2.em) # plot cond-int in upper triangle and full in lower triangle # for chromosomes 1, 4, 6, 15 plot(out2.em, upper="cond-int", chr=c(1,4,6,15)) # plot cond-add in upper triangle and add in lower triangle # for chromosomes 1, 4 plot(out2.em, upper="cond-add", lower="add", chr=c(1,4)) # plot the differences between the LOD scores from Haley-Knott # regression and the EM algorithm plot(out2.hk - out2.em, allow.neg=TRUE) plot.scantwoperm Plot permutation results for a 2d, 2-QTL genome scan Description Plot a histogram of the permutation results from a two-dimensional, two-QTL genome scan. Usage ## S3 method for class 'scantwoperm' plot(x, lodcolumn=1, include_rug=TRUE, ...) Arguments x An object of class "scantwoperm", as output by scantwo when n.perm is specified. lodcolumn This indicates the LOD score column to plot. This should be a single number between 1 and the number of LOD columns in the object input. include_rug If TRUE, include a call to rug. ... Passed to the function hist when it is called. Details The function plots a histogram of the permutation results obtained by scantwo when n.perm is specified. Separate histograms are provided for the five LOD scores, full, fv1, int, add, and av1. Value None. Author(s) Karl W Broman, plotErrorlod 167 See Also scantwo, summary.scantwoperm Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc) operm2 <- scantwo(fake.bc, method="hk", n.perm=10) plot(operm2) plotErrorlod Plot grid of error LOD values Description Plot a grid of the LOD scores indicating which genotypes are likely to be in error. Usage plotErrorlod(x, chr, ind, breaks=c(-Inf,2,3,4.5,Inf), col=c("white","gray85","hotpink","purple3"), alternate.chrid=FALSE, ...) Arguments x An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to be drawn in the plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. ind Indicates the individuals for which the error LOD scores should be plotted (passed to subset.cross). breaks A set of breakpoints for the colors; must give one more breakpoint than color. Intervals are open on the left and closed on the right, except for the lowest interval. col A vector of colors to appear in the image. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Ignored at this point. Details Uses image to plot a grid with different shades of pixels to indicate which genotypes are likely to be in error. Darker pixels have higher error LOD scores: LOD ≤ 2 in white; 2 < LOD ≤ 3 in gray; 3 < LOD ≤ 4.5 in pink; LOD > 4.5 in purple. 168 plotGeno Value None. Author(s) Karl W Broman, References Lincoln, S. E. and Lander, E. S. (1992) Systematic detection of errors in genetic linkage data. Genomics 14, 604–610. See Also calc.errorlod, top.errorlod, image, subset.cross, plotGeno Examples data(hyper) # Calculate error LOD scores hyper <- calc.errorlod(hyper,error.prob=0.01) # plot the error LOD scores; print those above a specified cutoff plotErrorlod(hyper) plotErrorlod(hyper,chr=1) plotGeno Plot observed genotypes, flagging likely errors Description Plot the genotypes on a particular chromosome for a set of individuals, flagging likely errors. Usage plotGeno(x, chr, ind, include.xo=TRUE, horizontal=TRUE, cutoff=4, min.sep=2, cex=1.2, ...) Arguments x An object of class cross. See read.cross for details. chr The chromosome to plot. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.) ind Vector of individuals to plot (passed to subset.cross). If missing, all individuals are plotted. include.xo If TRUE, plot X’s in intervals having a crossover. Not available for a 4-way cross. horizontal If TRUE, chromosomes are plotted horizontally. cutoff Genotypes with error LOD scores above this value are flagged as possible errors. plotGeno 169 min.sep Markers separated by less than this value (as a percent of the chromosome length) are pulled apart, so that they may be distinguished in the picture. cex Character expansion for the size of points in the plot. Larger numbers give larger points; see par. ... Ignored at this point. Details Plots the genotypes for a set of individuals. Likely errors are indicated by red squares. In a backcross, genotypes AA and AB are indicated by white and black circles, respectively. In an intercross, genotypes AA, AB and BB are indicated by white, gray, and black circles, respectively, and the partially missing genotypes "not BB" (D in mapmaker) and "not AA" (C in mapmaker) are indicated by green and orange circles, respectively. For the X chromosome in a backcross or intercross, hemizygous males are plotted as if they were homozygous (that is, with white and black circles). For a 4-way cross, two lines are plotted for each individual. The left or upper line indicates the allele A (white) or B (black); the right or lower line indicates the allele C (white) or D (black). For the case that genotype is known to be only AC/BD or AD/BC, we use green and orange, respectively. Value None. Author(s) Karl W Broman, See Also calc.errorlod, top.errorlod, subset.cross Examples data(hyper) # Calculate error LOD scores hyper <- calc.errorlod(hyper,error.prob=0.01) # print those above a specified cutoff top.errorlod(hyper,cutoff=4) # plot genotype data, flagging genotypes with error LOD > cutoff plotGeno(hyper, chr=1, ind=160:200, cutoff=7, min.sep=2) 170 plotInfo plotInfo Plot the proportion of missing genotype information Description Plot a measure of the proportion of missing information in the genotype data. Usage plotInfo(x, chr, method=c("entropy","variance","both"), step=1, off.end=0, error.prob=0.001, map.function=c("haldane","kosambi","c-f","morgan"), alternate.chrid=FALSE, fourwaycross=c("all", "AB", "CD"), include.genofreq=FALSE, ...) Arguments x An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. method Indicates whether to plot the entropy version of the information, the variance version, or both. step Maximum distance (in cM) between positions at which the missing information is calculated, though for step=0, it is are calculated only at the marker locations. off.end Distance (in cM) past the terminal markers on each chromosome to which the genotype probability calculations will be carried. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi or Carter-Falconer map function when converting genetic distances into recombination fractions. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. fourwaycross For a phase-known four-way cross, measure missing genotype information overall ("all"), or just for the alleles from the first parent ("AB") or from the second parent ("CD"). include.genofreq If TRUE, estimated genotype frequencies (from the results of calc.genoprob averaged across the individuals) are included as additional columns in the output. ... Passed to plot.scanone. plotLodProfile 171 Details The entropy version of the missing information:P for a single individual at a single genomic position, we measure the missing information as H = g pg log pg / log n, where pg is the probability of the genotype g, and n is the number of possible genotypes, defining 0 log 0 = 0. This takes values between 0 and 1, assuming the value 1 when the genotypes (given the marker data) are equally likely and 0 when the genotypes are completely determined. We calculate the missing information at a particular position as the average of H across individuals. For an intercross, we don’t scale by log n but by the entropy in the case of genotype probabilities (1/4, 1/2, 1/4). The variance version of the missing information: we calculate the average, across individuals, of the variance of the genotype distribution (conditional on the observed marker data) at a particular locus, and scale by the maximum such variance. Calculations are done in C (for the sake of speed in the presence of little thought about programming efficiency) and the plot is created by a call to plot.scanone. Note that summary.scanone may be used to display the maximum missing information on each chromosome. Value An object with class scanone: a data.frame with columns the chromosome IDs and cM positions followed by the entropy and/or variance version of the missing information. Author(s) Karl W Broman, See Also plot.scanone, plotMissing, calc.genoprob, geno.table Examples data(hyper) plotInfo(hyper,chr=c(1,4)) # save the results and view maximum missing info on each chr info <- plotInfo(hyper) summary(info) plotInfo(hyper, bandcol="gray70") plotLodProfile Plot 1-d LOD profiles for a multiple QTL model Description Use the results of refineqtl to plot one-dimensional LOD profiles for each QTL. 172 plotLodProfile Usage plotLodProfile(qtl, chr, incl.markers=TRUE, gap=25, lwd=2, lty=1, col="black", qtl.labels=TRUE, mtick=c("line", "triangle"), show.marker.names=FALSE, alternate.chrid=FALSE, add=FALSE, showallchr=FALSE, labelsep=5, ...) Arguments qtl An object of class "qtl"; must have been produced by refineqtl using keeplodprofiles=TRUE. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. incl.markers Indicate whether to plot line segments at the marker locations. gap Gap separating chromosomes (in cM). lwd Line widths for each QTL trace (length 1 or the number of QTL). lty Line types for each QTL trace (length 1 or the number of QTL). col Line col for each QTL trace (length 1 or the number of QTL). qtl.labels If TRUE, place a label on each QTL trace. mtick Tick mark type for markers (line segments or upward-pointing triangels). show.marker.names If TRUE, show the marker names along the x axis. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. add If TRUE, add curves to a current plot. showallchr If FALSE (the default), only show the chr with a QTL labelsep If qtl.labels=TRUE, separation between peak LOD and QTL label, as percent of the height of the plot. ... Passed to the function plot when it is called. Details The function plots LOD profiles in the context of a multiple QTL model, using a scheme best described in Zeng et al. (2000). The position of each QTL is varied, keeping all other loci fixed. If a QTL is isolated on a chromosome, the entire chromosome is scanned; if there are additional linked QTL, the position of a QTL is scanned over the largest interval possible without allowing the order of QTLs along a chromosome to change. At each position for the QTL being scanned, we calculate a LOD score comparing the full model, with the QTL of interest at that particular position (and all others at their fixed positions) to the model with the QTL of interest (and any interactions that include that QTL) omitted. Care should be take regarding the arguments lwd, lty, and col; if vectors are given, they should be in the order of the QTL within the object, which may be different than the order in which they are plotted. (The LOD profiles are sorted by chromosome and position.) plotMap 173 Value None. Author(s) Karl W Broman, References Zeng Z.-B., Liu, J., Stam, L. F., Kao, C.-H., Mercer, J. M. and Laurie, C. C. (2000) Genetic architecture of a morphological shape difference between two Drosophila species. Genetics 154, 299–310. See Also refineqtl, makeqtl, scanqtl Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2) qtl <- makeqtl(fake.bc, chr=c(2,5), pos=c(32.5, 17.5), what="prob") out <- scanone(fake.bc, method="hk") # refine QTL positions and keep LOD profiles rqtl <- refineqtl(fake.bc, qtl=qtl, method="hk", keeplodprofile=TRUE) # plot the LOD profiles plotLodProfile(rqtl) # add the initial scan results, for comparison plot(out, add=TRUE, chr=c(2,5), col="red") plotMap Plot genetic map Description Plot genetic map of marker locations for all chromosomes. Usage ## S3 method for class 'map' plot(x, map2, chr, horizontal=FALSE, shift=TRUE, show.marker.names=FALSE, alternate.chrid=FALSE, ...) plotMap(x, map2, chr, horizontal=FALSE, shift=TRUE, show.marker.names=FALSE, alternate.chrid=FALSE, ...) 174 plotMap Arguments x A list whose components are vectors of marker locations. A cross object may be given instead, in which case the genetic map it contains is used. map2 An optional second genetic map with the same number (and names) of chromosomes. As with the first argument, a cross object may be given instead. If this argument is given, a comparison of the two genetic maps is plotted. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. horizontal Specifies whether the chromosomes should be plotted horizontally. shift If TRUE, shift the first marker on each chromosome to be at 0 cM. show.marker.names If TRUE, marker names are included. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Passed to plot. Details Plots the genetic map for each chromosome, or a comparison of the genetic maps if two maps are given. For a comparison of two maps, the first map is on the left (or, if horizontal=TRUE, on the top). Lines are drawn to connect markers. Markers that exist in just one map and not the other are indicated by short line segments, on one side or the other, that are not connected across. For a sex-specific map, female and male maps are plotted against one another. For two sex-specific maps, the two female maps are plotted against one another and the two male maps are plotted against one another. Value None. Author(s) Karl W Broman, See Also est.map, plot.cross Examples data(fake.bc) plotMap(fake.bc) plotMap(fake.bc,horizontal=TRUE) newmap <- est.map(fake.bc) plotMissing 175 plot(newmap) plotMap(fake.bc, newmap) plotMap(fake.bc, show.marker.names=TRUE) plotMissing Plot grid of missing genotypes Description Plot a grid showing which genotypes are missing. Usage plotMissing(x, chr, reorder=FALSE, main="Missing genotypes", alternate.chrid=FALSE, ...) Arguments x An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. reorder Specify whether to reorder individuals according to their phenotypes. FALSE TRUE n Don’t reorder Reorder according to the sum of the phenotypes Reorder according to phenotype n main Title to place on plot. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. ... Ignored at this point. Details Uses image to plot a grid with black pixels where the genotypes are missing. For intercross and 4-way cross data, gray pixels are plotted for the partially missing genotypes (for example, "not AA"). Value None. Author(s) Karl W Broman, 176 plotModel See Also plot.cross, geno.image, image Examples data(fake.f2) plotMissing(fake.f2) plotModel Plot a QTL model Description Plot a graphical representation of a QTL model, with nodes representing QTL and line segments representing pairwise interactions. Usage plotModel(qtl, formula, circrad.rel=0.25, circrad.abs, cex.name=1, chronly=FALSE, order, ...) Arguments qtl A QTL object (as created by makeqtl) or vector of character strings indicating the names for the QTL. This is also allowed to be a list that contains a component named "chr" (and, optionally, components names "pos" and "formula"). formula Optional formula defining the QTL model. If missing, we look for an attribute "formula" to the input QTL object or a item named "formula" within the QTL object. circrad.rel Radius of the circles that indicate the QTL, relative to the distance between the circles. circrad.abs Optional radius of the circles that indicate the QTL; note that the plotting region will have x- and y-axis limits spanning 3 units. cex.name Character expansion for the QTL names. chronly If TRUE and a formal QTL object is given, only the chromosome IDs are used to identify the QTL. order Optional vector indicating a permutation of the QTL to define where they are to appear in the plot. QTL are placed around a circle, starting at the top and going clockwise. ... Passed to the function plot. Value None. Author(s) Karl W Broman, plotPheno 177 See Also stepwiseqtl, makeqtl Examples # plot a QTL model, using a vector of character strings to define the QTL plotModel(c("1","4","6","15"), formula=y~Q1+Q2+Q3*Q4) # plot an additive QTL model data(hyper) hyper <- calc.genoprob(hyper) qtl <- makeqtl(hyper, chr=c(1,4,6,15), pos=c(68.3,30,60,18), what="prob") plotModel(qtl) # include an interaction plotModel(qtl, formula=y~Q1+Q2+Q3*Q4) # alternatively, include the formula as an attribute to the QTL object attr(qtl, "formula") <- y~Q1+Q2+Q3*Q4 plotModel(qtl) # if formula given, the attribute within the object is ignored plotModel(qtl, y~Q1+Q2+Q3+Q4) # NULL formula indicates additive QTL model plotModel(qtl, NULL) # reorder the QTL in the figure plotModel(qtl, order=c(1,3,4,2)) # show just the chromosome numbers plotModel(qtl, chronly=TRUE) plotPheno Plot a phenotype distribution Description Plots a histogram or barplot of the data for a phenotype from an experimental cross. Usage plotPheno(x, pheno.col=1, ...) Arguments x An object of class cross. See read.cross for details. pheno.col The phenotype column to plot: a numeric index, or the phenotype name as a character string. Alternatively, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. ... Passed to hist or barplot. 178 plotPXG Details √ Numeric phenotypes are displayed as histograms with approximately 2 n bins. Phenotypes that are factors or that have very few unique values are displayed as barplots. Value None. Author(s) Karl W Broman, See Also plot.cross, plotMap, plotMissing, hist, barplot Examples data(fake.bc) plotPheno(fake.bc, pheno.col=1) plotPheno(fake.bc, pheno.col=3) plotPheno(fake.bc, pheno.col="age") plotPXG Plot phenotypes versus marker genotypes Description Plot the phenotype values versus the genotypes at a marker or markers. Usage plotPXG(x, marker, pheno.col=1, jitter=1, infer=TRUE, pch, ylab, main, col, ...) Arguments x An object of class cross. See read.cross for details. marker Marker name (a character string; can be a vector). pheno.col Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either nonintegers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. jitter A positive number indicating how much to spread out the points horizontally. (Larger numbers correspond to greater spread.) infer If TRUE, missing genotypes are filled in with a single random imputation and plotted in red; if FALSE, only individuals typed at the specified marker are plotted. plotPXG 179 pch Plot symbol. ylab Label for y-axis. main Main title for the plot. If missing, the names of the markers are used. col A vector of colors to use for the confidence intervals (optional). ... Passed to plot. Details Plots the phenotype data against the genotypes at the specified marker. If infer=TRUE, the genotypes of individuals that were not typed is inferred based the genotypes at linked markers via a single imputation from sim.geno; these points are plotted in red. For each genotype, the phenotypic mean is plotted, with error bars at ± 1 SE. Value A data.frame with initial columns the marker genotypes, then the phenotype data, then a column indicating whether any of the marker genotypes were inferred (1=at least one genotype inferred, 0=none were inferred). Author(s) Karl W Broman, ; Brian Yandell See Also find.marker, effectplot, find.flanking, effectscan Examples data(listeria) mname <- find.marker(listeria, 5, 28) # marker D5M357 plotPXG(listeria, mname) mname2 <- find.marker(listeria, 13, 26) # marker D13Mit147 plotPXG(listeria, c(mname, mname2)) plotPXG(listeria, c(mname2, mname)) # output of the function contains the raw data output <- plotPXG(listeria, mname) head(output) # another example data(fake.f2) mname <- find.marker(fake.f2, 1, 37) # marker D1M437 plotPXG(fake.f2, mname) mname2 <- find.marker(fake.f2, "X", 14) # marker DXM66 plotPXG(fake.f2, mname2) plotPXG(fake.f2, c(mname,mname2)) plotPXG(fake.f2, c(mname2,mname)) 180 plotRF plotRF Plot recombination fractions Description Plot a grid showing the recombination fractions for all pairs of markers, and/or the LOD scores for tests of linkage between pairs of markers. Usage plotRF(x, chr, what=c("both","lod","rf"), alternate.chrid=FALSE, zmax=12, mark.diagonal=FALSE, col.scheme=c("viridis", "redblue"), ...) Arguments x An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to plot. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. what Indicate whether to plot LOD scores, recombination fractions or both. alternate.chrid If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished. zmax Maximum LOD score plotted; values above this are all thresholded at this value. mark.diagonal If TRUE, include black line segments around the pixels along the diagonal, to better separate the upper left triangle from the lower right triangle. col.scheme The color palette. The default is "viridis"; see Option D at https://bids. github.io/colormap ... Generally ignored, but you can include main to change or omit the title of the figure. Details Uses image to plot a grid showing the recombination fractions and/or LOD scores for all pairs of markers. (The LOD scores are for a test of r = 1/2.) If both are plotted, the recombination fractions are in the upper left triangle while the LOD scores are in the lower right triangle. With col.scheme="viridis" (the default), purple corresponds to a large LOD score or a small recombination fraction, while yellow is the reverse. With col.scheme="redblue", red corresponds to a large LOD or a small recombination fraction, while blue is the reverse. Note that missing values appear in light gray. Recombination fractions are transformed by −4(log2 r + 1) to make them on the same sort of scale as LOD scores. Values of LOD or the transformed recombination fraction that are above 12 are set to 12. pull.argmaxgeno 181 Value None. Author(s) Karl W Broman, See Also est.rf, pull.rf, plot.rfmatrix, image, badorder, ripple Examples data(badorder) badorder <- est.rf(badorder) plotRF(badorder) # plot just chr 1 plotRF(badorder, chr=1) # plot just the recombination fractions plotRF(badorder, what="rf") # plot just the LOD scores, and just for chr 2 and 3 plotRF(badorder, chr=2:3, what="lod") pull.argmaxgeno Pull out the results of the Viterbi algorithm from a cross Description Pull out the results of argmax.geno from a cross as a matrix. Usage pull.argmaxgeno(cross, chr, include.pos.info=FALSE, rotate=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. include.pos.info If TRUE, include columns with marker name, chromosmoe ID, and cM position. (If include.pos.info=TRUE, we take rotate=TRUE.) rotate If TRUE, return matrix with individuals as columns and positions as rows. If FALSE, rows correspond to individuals. 182 pull.draws Value A matrix containing numeric indicators of the inferred genotypes. Multiple chromosomes are pasted together. Author(s) Karl W Broman, See Also pull.geno, pull.genoprob, pull.draws, argmax.geno Examples data(listeria) listeria <- argmax.geno(listeria, step=1, stepwidth="max") amg <- pull.argmaxgeno(listeria, chr=c(5,13), include.pos.info=TRUE, rotate=TRUE) amg[1:5,1:10] pull.draws Pull out the genotype imputations from a cross Description Pull out the results of sim.geno from a cross as an array. Usage pull.draws(cross, chr) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. Value An array containing numeric indicators of the imputed genotypes. Multiple chromosomes are pasted together. The dimensions are individuals by positions by imputations Author(s) Karl W Broman, See Also pull.geno, pull.genoprob, pull.argmaxgeno, sim.geno pull.geno 183 Examples data(listeria) listeria <- sim.geno(listeria, step=5, stepwidth="max", n.draws=8) dr <- pull.draws(listeria, chr=c(5,13)) dr[1:20,1:10,1] pull.geno Pull out the genotype data from a cross Description Pull out the genotype data from a cross object, as a single big matrix. Usage pull.geno(cross, chr) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. Value A matrix of size n.ind x tot.mar. The raw genotype data in the input cross object, with the chromosomes pasted together. Author(s) Karl W Broman, See Also pull.pheno, pull.map pull.draws, pull.genoprob, pull.argmaxgeno Examples data(listeria) dat <- pull.geno(listeria) # image of the genotype data image(1:ncol(dat),1:nrow(dat),t(dat),ylab="Individuals",xlab="Markers", col=c("red","yellow","blue","green","violet")) abline(v=cumsum(c(0,nmar(listeria)))+0.5) abline(h=nrow(dat)+0.5) 184 pull.genoprob pull.genoprob Pull out the genotype probabilities from a cross Description Pull out the results of calc.genoprob from a cross as a matrix. Usage pull.genoprob(cross, chr, omit.first.prob=FALSE, include.pos.info=FALSE, rotate=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. omit.first.prob If TRUE, omit the probabilities for the first genotype at each position (since they sum to 1). include.pos.info If TRUE, include columns with marker name, genotype, chromosome ID, and cM position. (If include.pos.info=TRUE, we take rotate=TRUE.) rotate If TRUE, return matrix with individuals as columns and positions/genotypes as rows. If FALSE, rows correspond to individuals. Value A matrix containing genotype probabilities. Multiple chromosomes and the multiple genotypes at each position are pasted together. Author(s) Karl W Broman, See Also pull.geno, pull.argmaxgeno, pull.draws, calc.genoprob Examples data(listeria) listeria <- calc.genoprob(listeria, step=1, stepwidth="max") pr <- pull.genoprob(listeria, chr=c(5,13), omit.first.prob=TRUE, include.pos.info=TRUE, rotate=TRUE) pr[1:5,1:10] pull.map pull.map 185 Pull out the genetic map from a cross Description Pull out the map portion of a cross object. Usage pull.map(cross, chr, as.table=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. as.table If TRUE, return the genetic map as a table with chromosome assignments and marker names. If FALSE, return the map as a "map" object. Value The genetic map: a list with each component containing the marker positions (in cM) for a chromosome. Each component has class A or X according to whether it is an autosome or the X chromosome. The components are either vectors of marker positions or, for a sex-specific map, 2-row matrices containing the female and male marker locations. The map itself is given class map. Author(s) Karl W Broman, See Also replace.map, plotMap, map2table Examples data(fake.f2) map <- pull.map(fake.f2) plot(map) 186 pull.pheno pull.markers Drop all but a selected set of markers Description Drop all but a selected set of markers from the data matrices and genetic maps. Usage pull.markers(cross, markers) Arguments cross markers An object of class cross. See read.cross for details. A character vector of marker names. Value The input object, with any markers not specified in the vector markers removed from the genotype data matrices, genetic maps, and, if applicable, any derived data (such as produced by calc.genoprob). (It might be a good idea to re-derive such things after using this function.) Author(s) Karl W Broman, See Also drop.nullmarkers, drop.markers, geno.table, clean.cross Examples data(listeria) listeria2 <- pull.markers(listeria, c("D10M44","D1M3","D1M75")) pull.pheno Pull out phenotype data from a cross Description Pull out selected phenotype data from a cross object, as a data frame or vector. Usage pull.pheno(cross, pheno.col) Arguments cross pheno.col An object of class cross. See read.cross for details. A vector specifying which phenotypes to keep or discard. This may be a logical vector, a numeric vector, or a vector of character strings (for the phenotype names). If missing, the entire set of phenotypes is output. pull.rf 187 Value A data.frame with columns specifying phenotypes and rows specifying individuals. If there is just one phenotype, a vector (rather than a data.frame) is returned. Author(s) Karl W Broman, See Also pull.geno, pull.map Examples data(listeria) pull.pheno(listeria, "sex") pull.rf Pull out recombination fractions or LOD scores from a cross object Description Pull out either the pairwise recombination fractions or the LOD scores, as calculated by est.rf, from a cross object. Usage pull.rf(cross, what=c("rf", "lod"), chr) Arguments cross An object of class cross. See read.cross for details. what Indicates whether to pull out a matrix of estimated recombination fractions or a matrix of LOD scores. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. Value An object of class "rfmatrix", which is a matrix of either estimated recombination fractions between all marker pairs or of LOD scores (for the test of rf=1/2) for all marker pairs. The genetic map is included as an attribute. Author(s) Karl W Broman, 188 read.cross See Also est.rf, plot.rfmatrix, plotRF Examples data(fake.f2) fake.f2 <- est.rf(fake.f2) rf <- pull.rf(fake.f2) lod <- pull.rf(fake.f2, "lod") plot(rf[1,], lod[1,], xlab="rec frac", ylab="LOD score") marker <- markernames(fake.f2, chr=5)[6] par(mfrow=c(2,1)) plot(rf, marker, bandcol="gray70") plot(lod, marker, bandcol="gray70") qtlversion Installed version of R/qtl Description Print the version number of the currently installed version of R/qtl. Usage qtlversion() Value A character string with the version number of the currently installed version of R/qtl. Author(s) Karl W Broman, Examples qtlversion() read.cross Read data for a QTL experiment Description Data for a QTL experiment is read from a set of files and converted into an object of class cross. The comma-delimited format (csv) is recommended. All formats require chromosome assignments for the genetic markers, and assume that markers are in their correct order. read.cross 189 Usage read.cross(format=c("csv", "csvr", "csvs", "csvsr", "mm", "qtx", "qtlcart", "gary", "karl", "mapqtl", "tidy"), dir="", file, genfile, mapfile, phefile, chridfile, mnamesfile, pnamesfile, na.strings=c("-","NA"), genotypes=c("A","H","B","D","C"), alleles=c("A","B"), estimate.map=FALSE, convertXdata=TRUE, error.prob=0.0001, map.function=c("haldane", "kosambi", "c-f", "morgan"), BC.gen=0, F.gen=0, crosstype, ...) Arguments format Specifies the format of the data. dir Directory in which the data files will be found. In Windows, use forward slashes ("/") or double backslashes ("\\") to specify directory trees. file The main input file for formats csv, csvr and mm. genfile File with genotype data (formats csvs, csvsr, karl, gary and mapqtl only). mapfile File with marker position information (all except the csv formats). phefile File with phenotype data (formats csvs, csvsr, karl, gary and mapqtl only). chridfile File with chromosome ID for each marker (gary format only). mnamesfile File with marker names (gary format only). pnamesfile File with phenotype names (gary format only). na.strings A vector of strings which are to be interpreted as missing values (csv and gary formats only). For the csv formats, these are interpreted globally for the entire file, so missing value codes in phenotypes must not be valid genotypes, and vice versa. For the gary format, these are used only for the phenotype data. genotypes A vector of character strings specifying the genotype codes (csv formats only). Generally this is a vector of length 5, with the elements corresponding to AA, AB, BB, not BB (i.e., AA or AB), and not AA (i.e., AB or BB). Note: Pay careful attention to the third and fourth of these; the order of these can be confusing. If you are trying to read 4-way cross data, your file must have genotypes coded as described below, and you need to set genotypes=NULL so that no re-coding gets done. alleles A vector of two one-letter character strings (or four, for the four-way cross), to be used as labels for the two alleles. estimate.map For all formats but qtlcart, mapqtl, and karl: if TRUE and marker positions are not included in the input files, the genetic map is estimated using the function est.map. convertXdata If TRUE, any X chromosome genotype data is converted to the internal standard, using columns sex and pgm in the phenotype data if they available or by inference if they are not. If FALSE, the X chromsome data is read as is. error.prob In the case that the marker map must be estimated: Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function In the case that the marker map must be estimated: Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.) 190 read.cross BC.gen F.gen crosstype ... Used only for cross type "bcsft". Used only for cross type "bcsft". Optional character string to force a particular cross type. Additional arguments, passed to the function read.table in the case of csv and csvr formats. In particular, one may use the argument sep to specify the field separator (the default is a comma), dec to specify the character used for the decimal point (the default is a period), and comment.char to specify a character to indicate comment lines. Details The available formats are comma-delimited (csv), rotated comma-delimited (csvr), comma-delimited with separate files for genotype and phenotype data (csvs), rotated comma-delimited with separate files for genotype and phenotype data (csvsr), Mapmaker (mm), Map Manager QTX (qtx), Gary Churchill’s format (gary), Karl Broman’s format (karl) and MapQTL/JoinMap (mapqtl). The required files and their specification for each format appears below. The comma-delimited formats are recommended. Note that most of these formats work only for backcross and intercross data. The sampledata directory in the package distribution contains sample data files in multiple formats. Also see https://rqtl.org/sampledata. The ... argument enables additional arguments to be passed to the function read.table in the case of csv and csvr formats. In particular, one may use the argument sep to specify the field separator (the default is a comma), dec to specify the character used for the decimal point (the default is a period), and comment.char to specify a character to indicate comment lines. Value An object of class cross, which is a list with two components: geno pheno This is a list with elements corresponding to chromosomes. names(geno) contains the names of the chromsomes. Each chromosome is itself a list, and is given class A or X according to whether it is autosomal or the X chromosome. There are two components for each chromosome: data, a matrix whose rows are individuals and whose columns are markers, and map, either a vector of marker positions (in cM) or a matrix of dim (2 x n.mar) where the rows correspond to marker positions in female and male genetic distance, respectively. The genotype data gets converted into numeric codes, as follows. The genotype data for a backcross is coded as NA = missing, 1 = AA, 2 = AB. For an F2 intercross, the coding is NA = missing, 1 = AA, 2 = AB, 3 = BB, 4 = not BB (i.e. AA or AB; D in Mapmaker/qtl), 5 = not AA (i.e. AB or BB; C in Mapmaker/qtl). For a 4-way cross, the mother and father are assumed to have genotypes AB and CD, respectively. The genotype data for the progeny is assumed to be phaseknown, with the following coding scheme: NA = missing, 1 = AC, 2 = BC, 3 = AD, 4 = BD, 5 = A = AC or AD, 6 = B = BC or BD, 7 = C = AC or BC, 8 = D = AD or BD, 9 = AC or BD, 10 = AD or BC, 11 = not AC, 12 = not BC, 13 = not AD, 14 = not BD. data.frame of size (n.ind x n.phe) containing the phenotypes. If a phenotype with the name id or ID is included, these identifiers will be used in top.errorlod, plotErrorlod, and plotGeno as identifiers for the individual. While the data format is complicated, there are a number of functions, such as subset.cross, to assist in pulling out portions of the data. read.cross 191 X chromosome The genotypes for the X chromosome require special care! The X chromosome should be given chromosome identifier X or x. If it is labeled by a number or by Xchr, it will be interpreted as an autosome. The phenotype data should contain a column named "sex" which indicates the sex of each individual, either coded as 0=female and 1=male, or as a factor with levels female/male or f/m. Case will be ignored both in the name and in the factor levels. If no such phenotype column is included, it will be assumed that all individuals are of the same sex. In the case of an intercross, the phenotype data may also contain a column named "pgm" (for "paternal grandmother") indicating the direction of the cross. It should be coded as 0/1 with 0 indicating the cross (AxB)x(AxB) or (BxA)x(AxB) and 1 indicating the cross (AxB)x(BxA) or (BxA)x(BxA). If no such phenotype column is included, it will be assumed that all individuals come from the same direction of cross. The internal storage of X chromosome data is quite different from that of autosomal data. Males are coded 1=AA and 2=BB; females with pgm==0 are coded 1=AA and 2=AB; and females with pgm==1 are coded 1=BB and 2=AB. If the argument convertXdata is TRUE, conversion to this format is made automatically; if FALSE, no conversion is done, summary.cross will likely return a warning, and most analyses will not work properly. Use of convertXdata=FALSE (in which case the X chromosome genotypes will not be converted to our internal standard) can be useful for diagnosing problems in the data, but will require some serious mucking about in the internal data structure. CSV format The input file is a comma-delimited text file. A different field separator may be specified via the argument sep, which will be passed to the function read.table). For example, in Europe, it is common to use a comma in place of the decimal point in numbers and so a semi-colon in place of a comma as the field separator; such data may be read by using sep=";" and dec=",". The first line should contain the phenotype names followed by the marker names. At least one phenotype must be included; for example, include a numerical index for each individual. The second line should contain blanks in the phenotype columns, followed by chromosome identifiers for each marker in all other columns. If a chromosome has the identifier X or x, it is assumed to be the X chromosome; otherwise, it is assumed to be an autosome. An optional third line should contain blanks in the phenotype columns, followed by marker positions, in cM. Marker order is taken from the cM positions, if provided; otherwise, it is taken from the column order. Subsequent lines should give the data, with one line for each individual, and with phenotypes followed by genotypes. If possible, phenotypes are made numeric; otherwise they are converted to factors. The genotype codes must be the same across all markers. For example, you can’t have one marker coded AA/AB/BB and another coded A/H/B. This includes genotypes for the X chromosome, for which hemizygous individuals should be coded as if they were homoyzogous. The cross is determined to be a backcross if only the first two elements of the genotypes string are found; otherwise, it is assumed to be an intercross. 192 read.cross CSVr format This is just like the csv format, but rotated (or really transposed), so that rows are columns and columns are rows. CSVs format This is like the csv format, but with separate files for the genotype and phenotype data. The first column in the genotype data must specify individuals’ identifiers, and there must be a column in the phenotype data with precisely the same information (and with the same name). These IDs will be included in the data as a phenotype. If the name id or ID is used, these identifiers will be used in top.errorlod, plotErrorlod, and plotGeno as identifiers for the individual. The first row in each file contains the column names. For the phenotype file, these are the names of the phenotypes. For the genotype file, the first cell will be the name of the identifier column (id or ID) and the subsequent fields will be the marker names. In the genotype data file, the second row gives the chromosome IDs. The cell in the second row, first column, must be blank. A third row giving cM positions of markers may be included, in which case the cell in the third row, first column, must be blank. There need be no blank rows in the phenotype data file. CSVsr format This is just like the csvs format, but with each file rotated (or really transposed), so that rows are columns and columns are rows. Mapmaker format This format requires two files. The so-called rawfile, specified by the argument file, contains the genotype and phenotype data. Rows beginning with the symbol # are ignored. The first line should be either data type f2 intercross or data type f2 backcross. The second line should begin with three numbers indicating the numbers of individuals, markers and phenotypes in the file. This line may include the word symbols followed by symbol assignments (see the documentation for mapmaker, and cross your fingers). The rest of the lines give genotype data followed by phenotype data, with marker and phenotype names always beginning with the * symbol. A second file contains the genetic map information, specified with the argument mapfile. The map file may be in one of two formats. The function will determine which format of map file is presented. The simplest format for the map file is not standard for the Mapmaker software, but is easy to create. The file contains two or three columns separated by white space and with no header row. The first column gives the chromosome assignments. The second column gives the marker names, with markers listed in the order along the chromosomes. An optional third column lists the map positions of the markers. Another possible format for the map file is the .maps format, which is produced by Mapmaker. The code for reading this format was written by Brian Yandell. Marker order is taken from the map file, either by the order they are presented or by the cM positions, if specified. Map Manager QTX format This format requires a single file (that produced by the Map Manager QTX program). read.cross 193 QTL Cartographer format This format requires two files: the .cro and .map files for QTL Cartographer (produced by the QTL Cartographer sub-program, Rmap and Rcross). Note that the QTL Cartographer cross types are converted as follows: RF1 to riself, RF2 to risib, RF0 (doubled haploids) to bc, B1 or B2 to bc, RF2 or SF2 to f2. Tidy format This format requires three simple CSV files, separating the genotype, phenotype, and marker map information so that each file may be of a simple form. Gary format This format requires the six files. All files have default names, and so the file names need not be specified if the default names are used. genfile (default = "geno.dat") contains the genotype data. The file contains one line per individual, with genotypes for the set of markers separated by white space. Missing values are coded as 9, and genotypes are coded as 0/1/2 for AA/AB/BB. mapfile (default = "markerpos.txt") contains two columns with no header row: the marker names in the first column and their cM position in the second column. If marker positions are not available, use mapfile=NULL, and a dummy map will be inserted. phefile (default = "pheno.dat") contains the phenotype data, with one row for each mouse and one column for each phenotype. There should be no header row, and missing values are coded as "-". chridfile (default = "chrid.dat") contains the chromosome identifier for each marker. mnamesfile (default = "mnames.txt") contains the marker names. pnamesfile (default = "pnames.txt") contains the names of the phenotypes. If phenotype names file is not available, use pnamesfile=NULL; arbitrary phenotype names will then be assigned. Karl format This format requires three files; all files have default names, and so need not be specified if the default name is used. genfile (default = "gen.txt") contains the genotype data. The file contains one line per individual, with genotypes separated by white space. Missing values are coded 0; genotypes are coded as 1/2/3/4/5 for AA/AB/BB/not BB/not AA. mapfile (default = "map.txt") contains the map information, in the following complicated format: n.chr n.mar(1) rf(1,1) rf(1,2) ... rf(1,n.mar(1)-1) mar.name(1,1) mar.name(1,2) ... mar.name(1,n.mar(1)) n.mar(2) ... etc. phefile (default = "phe.txt") contains a matrix of phenotypes, with one individual per line. The first line in the file should give the phenotype names. 194 read.cross MapQTL format This format requires three files, described in the manual of the MapQTL program (same as JoinMap). genfile corresponds to the loc file containing the genotype data. Each marker and its genotypes should be on a single line. mapfile corresponds to the map file containing the linkage group assignment, marker names and their map positions. phefile corresponds to the qua file containing the phenotypes. For the moment, only 4-way crosses are supported (CP population type in MapQTL). Author(s) Karl W Broman, ; Brian S. Yandell; Aaron Wolen References Broman, K. W. and Sen, Ś. (2009) A guide to QTL mapping with R/qtl. Springer. https://rqtl. org/book See Also subset.cross, summary.cross, plot.cross, c.cross, clean.cross, write.cross, sim.cross, read.table. The sampledata directory in the package distribution contains sample data files in multiple formats. Also see https://rqtl.org/sampledata. Examples ## Not run: # CSV format dat1 <- read.cross("csv", dir="Mydata", file="mydata.csv") # CSVS format dat2 <- read.cross("csvs", dir="Mydata", genfile="mydata_gen.csv", phefile="mydata_phe.csv") # you can read files directly from the internet datweb <- read.cross("csv", "https://rqtl.org/sampledata", "listeria.csv") # Mapmaker format dat3 <- read.cross("mm", dir="Mydata", file="mydata.raw", mapfile="mydata.map") # Map Manager QTX format dat4 <- read.cross("qtx", dir="Mydata", file="mydata.qtx") # QTL Cartographer format dat5 <- read.cross("qtlcart", dir="Mydata", file="qtlcart.cro", mapfile="qtlcart.map") # Gary format dat6 <- read.cross("gary", dir="Mydata", genfile="geno.dat", mapfile="markerpos.txt", phefile="pheno.dat", chridfile="chrid.dat", mnamesfile="mnames.txt", readMWril 195 pnamesfile="pnames.txt") # Karl format dat7 <- read.cross("karl", dir="Mydata", genfile="gen.txt", phefile="phe.txt", mapfile="map.txt") ## End(Not run) readMWril Read data for 4- or 8-way RIL Description Data for a set of 4- or 8-way recombinant inbred lines (RIL) is read from a pair of comma-delimited files and converted into an object of class cross. We require chromosome assignments for the genetic markers, and assume that markers are in their correct order. Usage readMWril(dir="", rilfile, founderfile, type=c("ri4self", "ri4sib", "ri8self", "ri8selfIRIP1", "ri8sib", "bgmagic16"), na.strings=c("-","NA"), rotate=FALSE, ...) Arguments dir rilfile founderfile type na.strings rotate ... Directory in which the data files will be found. In Windows, use forward slashes ("/") or double backslashes ("\\") to specify directory trees. Comma-delimited file for the RIL, in the "csv" format described in the help file for read.cross. File with founder strains’ genotypes, in the same orientation as the rilfile, but with just marker names and the founders’ marker genotypes. The type of RIL. A vector of strings which are to be interpreted as missing values. For the csv formats, these are interpreted globally for the entire file, so missing value codes in phenotypes must not be valid genotypes, and vice versa. For the gary format, these are used only for the phenotype data. If TRUE, the rilfile and founderfile are rotated (really transposed), with rows corresponding to markers and columns corresponding to individuals. Additional arguments, passed to the function read.table in the case of csv and csvr formats. In particular, one may use the argument sep to specify the field separator (the default is a comma) and dec to specify the character used for the decimal point (the default is a period). Details The rilfile should include a phenotype cross containing character strings of the form ABCDEFGH, indicating the cross used to generate each RIL. The genotypes should be coded as integers (e.g., 1 and 2). The founder strains in the founderfile should be the strains A, B, C, . . . , as indicated in the cross phenotype. The default arrangement of the files is to have markers as columns and individuals/founders as rows. If rotate=TRUE, do the opposite: markers as rows and individuals/founders as columns. 196 reduce2grid Value An object of class cross; see the help file for read.cross for details. An additional component crosses is included; this is a matrix indicating the crosses used to generate the RIL. Author(s) Karl W Broman, See Also read.cross, sim.cross Examples ## Not run: ril <- read.cross("../Data", "ril_data.csv", "founder_geno.csv", "ri4self", rotate=TRUE) ## End(Not run) reduce2grid Reduce to a grid of pseudomarkers. Description For high-density marker data, rather than run scanone at both the markers and at a set of pseudomarkers, we reduce to just a set of evenly-spaced pseudomarkers Usage reduce2grid(cross) Arguments cross An object of class cross. See read.cross for details. Details Genotype probabilities (from calc.genoprob) and/or imputations (from sim.geno) are subset to a grid of pseudomarkers. This is so that, in the case of high-density markers, we can do the genome scan calculations at a smaller set of points (on an evenly-spaced grid, but not at the markers) to save computation time. You need to first have run calc.genoprob and/or sim.geno, and you must use stepwidth="fixed". When plotting results with plot.scanone, use incl.markers=FALSE, as the output of scanone won’t include information about the marker locations and so will plot tick marks only at the first marker on each chromosome. Value The input cross object with included genotype probabilities or imputations subset to an evenlyspaced grid. refineqtl 197 Author(s) Karl W Broman, See Also calc.genoprob, sim.geno, scanone, plot.scanone Examples data(hyper) hyper <- calc.genoprob(hyper, step=2) hypersub <- reduce2grid(hyper) ## Not run: out <- scanone(hypersub) plot(out, incl.markers=FALSE) ## End(Not run) refineqtl Refine the positions of QTL Description Iteratively scan the positions for QTL in the context of a multiple QTL model, to try to identify the positions with maximum likelihood, for a fixed QTL model. Usage refineqtl(cross, pheno.col=1, qtl, chr, pos, qtl.name, covar=NULL, formula, method=c("imp","hk"), model=c("normal", "binary"), verbose=TRUE, maxit=10, incl.markers=TRUE, keeplodprofile=TRUE, tol=1e-4, maxit.fitqtl=1000, forceXcovar=FALSE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching the phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl A QTL object, as produced by makeqtl, containing the positions of the QTL. Provide either qtl or the pair chr and pos. chr Vector indicating the chromosome for each QTL; if qtl is provided, this should not be. pos Vector indicating the positions for each QTL; if qtl is provided, this should not be. qtl.name Optional user-specified name for each QTL. If qtl is provided, this should not be. covar A matrix or data.frame of covariates. These must be strictly numeric. 198 refineqtl formula An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are indicated as Q1, Q2, etc. Covariates are indicated by their names in covar. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits verbose If TRUE, give feedback about progress. If verbose is an integer > 1, further messages from scanqtl are also displayed. maxit Maximum number of iterations. incl.markers If FALSE, do calculations only at points on an evenly spaced grid. keeplodprofile If TRUE, keep the LOD profiles from the last iteration as attributes to the output. tol Tolerance for convergence for the binary trait model. maxit.fitqtl Maximum number of iterations for fitting the binary trait model. forceXcovar If TRUE, force inclusion of X-chr-related covariates (like sex and cross direction). Details QTL positions are optimized, within the context of a fixed QTL model, by a scheme described in Zeng et al. (1999). Each QTL is considered one at a time (in a random order), and a scan is performed, allowing the QTL to vary across its chromosome, keeping the positions of all other QTL fixed. If there is another QTL on the chromosome, the position of the floating QTL is scanned from the end of the chromosome to the position of the flanking QTL. If the floating QTL is between two QTL on a chromosome, its position is scanned between those two QTL positions. Each QTL is moved to the position giving the highest likelihood, and the entire process is repeated until no further improvement in likelihood can be obtained. One may provide either a qtl object (as produced by makeqtl), or vectors chr and pos (and, optionally, qtl.name) indicating the positions of the QTL. If a qtl object is provided, QTL that do not appear in the model formula are ignored, but they remain part of the QTL object that is output. Value An object of class qtl, with QTL placed in their new positions. If keeplodprofile=TRUE, LOD profiles from the last pass through the refinement algorithm are retained as an attribute, "lodprofile", to the object. These may be plotted with plotLodProfile. Author(s) Karl W Broman, References Zeng, Z.-B., Kao, C.-H., and Basten, C. J. (1999) Estimating the genetic architecture of quantitative traits. Genet. Res. 74, 279–289. Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. reorderqtl 199 See Also fitqtl, makeqtl, scanqtl, addtoqtl, dropfromqtl, replaceqtl, plotLodProfile Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2) qtl <- makeqtl(fake.bc, chr=c(2,5), pos=c(32.5, 17.5), what="prob") rqtl <- refineqtl(fake.bc, qtl=qtl, method="hk") reorderqtl Reorder the QTL in a qtl object Description This function changes the order of the QTL in a QTL object. Usage reorderqtl(qtl, neworder) Arguments qtl A qtl object, as created by makeqtl. neworder A vector containing the positive integers up to the number of QTL in qtl, indicating the new order for the QTL. If missing, the QTL are ordered by chromosome and then by their position within a chromosome. Details Everything in the input qtl is reordered except the altname component, which contains names of the form Q1, Q2, etc. Value The input qtl object, with the loci reordered. Author(s) Karl W Broman, See Also makeqtl, fitqtl, dropfromqtl, addtoqtl, replaceqtl 200 replace.map Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 6, 13) qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2) qtl <- makeqtl(fake.f2, qc, qp, what="prob") qtl <- reorderqtl(qtl, c(2,3,1)) qtl qtl <- reorderqtl(qtl) qtl replace.map Replace the genetic map of a cross Description Replace the map portion of a cross object. Usage replace.map(cross, map) ## S3 method for class 'cross' replacemap(object, map) Arguments cross An object of class cross. See read.cross for details. object Same as cross. map A list containing the new genetic map. This must be the same length and with the same marker names as that contained in cross. Value The input cross object with the genetic map replaced by the input map. Maps for results from calc.genoprob, sim.geno and argmax.geno are also replaced, using interpolation if necessary. Author(s) Karl W Broman, See Also pull.map, est.map replacemap.scanone 201 Examples data(fake.f2) newmap <- est.map(fake.f2) plotMap(fake.f2, newmap) fake.f2 <- replace.map(fake.f2, newmap) replacemap.scanone Replace the genetic map in QTL mapping results with an alternate map Description Replace the positions of LOD scores in output from scanone with values based on an alternative map (such as a physical map), with pseudomarker locations determined by linear interpolation. Usage ## S3 method for class 'scanone' replacemap(object, map) Arguments object An object of class "scanone", as output by the function scanone. map A list containing the alternative genetic map. All chromosomes in object should have corresponding chromosomes in map, and markers must be in the same order in the two maps. There must be at least two markers on each chromosome in map that appear in object. Details The positions of pseudomarkers are determined by linear interpolation between markers. In the case of pseudomarkers beyond the ends of the terminal markers on chromosomes, we use the overall lengths of the chromosome in object and map to determine the new spacing. Value The input object with the positions of LOD scores revised to match those in the input map. Author(s) Karl W Broman, See Also replacemap.cross, est.map, replacemap.scantwo 202 replacemap.scantwo Examples data(fake.f2) origmap <- pull.map(fake.f2) newmap <- est.map(fake.f2) fake.f2 <- replacemap(fake.f2, newmap) fake.f2 <- calc.genoprob(fake.f2, step=2.5) out <- scanone(fake.f2, method="hk") out.rev <- replacemap(out, origmap) replacemap.scantwo Replace the genetic map in QTL mapping results with an alternate map Description Replace the positions of LOD scores in output from scantwo with values based on an alternative map (such as a physical map), with pseudomarker locations determined by linear interpolation. Usage ## S3 method for class 'scantwo' replacemap(object, map) Arguments object An object of class "scantwo", as output by the function scantwo. map A list containing the alternative genetic map. All chromosomes in object should have corresponding chromosomes in map, and markers must be in the same order in the two maps. There must be at least two markers on each chromosome in map that appear in object. Details The positions of pseudomarkers are determined by linear interpolation between markers. In the case of pseudomarkers beyond the ends of the terminal markers on chromosomes, we use the overall lengths of the chromosome in object and map to determine the new spacing. Value The input object with the positions of LOD scores revised to match those in the input map. Author(s) Karl W Broman, See Also replacemap.cross, est.map, replacemap.scanone replaceqtl 203 Examples data(hyper) origmap <- pull.map(hyper) newmap <- est.map(hyper) hyper <- replacemap(hyper, newmap) hyper <- calc.genoprob(hyper, step=0) out <- scantwo(hyper, method="hk") out.rev <- replacemap(out, origmap) replaceqtl Replace a QTL in a qtl object with a different position Description This function replaces a QTL or QTLs in a qtl object with a different position. Usage replaceqtl(cross, qtl, index, chr, pos, qtl.name, drop.lod.profile=TRUE) Arguments cross An object of class cross. See read.cross for details. qtl A qtl object, as created by makeqtl. index Numeric index indicating the QTL to be replaced. chr Vector (of same length as index) indicating the chromosomes for the new QTL. pos Vector (of same length as index) indicating the positions for the new QTL. If there is no marker or pseudomarker at a position, the nearest position is used. qtl.name Optional vector (of same length as index) of user-specified names for each new QTL, used in the drop-one-term ANOVA table in fitqtl. If unspecified, the names will be of the form "Chr1@10" for a QTL on Chromsome 1 at 10 cM. drop.lod.profile If TRUE, remove any LOD profiles from the object. Value The input qtl object, but with some QTL replaced by new ones. See makeqtl for details on the format. Author(s) Karl W Broman, See Also makeqtl, fitqtl, dropfromqtl, addtoqtl, reorderqtl 204 rescalemap Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 6, 13) qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") qtl <- replaceqtl(fake.f2, qtl, 2, 6, 48.1) rescalemap Rescale genetic maps Description Rescale a genetic map by multiplying all positions by a constant Usage rescalemap(object, scale=1e-6) Arguments object An object of class cross (see read.cross for details) or map (see sim.map for details). scale Scale factor by which all positions will be multiplied. Details This function is included particularly for the case that map positions in a cross object were provided in basepairs and one wishes to quickly convert them to Mbp or some other approximation of cM distances. (In the mouse, 1 cM is approximation 2 Mbp, so one might use scale=5e-7 in this function.) Value If the input is a map object, a map object is returned; if the input is a cross object, a cross object is returned. In either case, the positions of markers are simply multiplied by scale. Author(s) Karl W Broman, See Also replace.map, est.map ripple 205 Examples data(hyper) rescaled <- rescalemap(hyper, scale=2) plotMap(hyper, rescaled) ripple Compare marker orders Description Investigate different marker orders for a given chromosome, comparing all possible permutations of a sliding window of markers. Usage ripple(cross, chr, window=4, method=c("countxo","likelihood"), error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), maxit=4000, tol=1e-6, sex.sp=TRUE, verbose=TRUE, n.cluster=1) Arguments cross An object of class cross. See read.cross for details. chr The chromosome to investigate. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.) window Number of markers to include in the sliding window of permuted markers. Larger numbers result in the comparison of a greater number of marker orders, but will require a considerable increase in computation time. method Indicates whether to compare orders by counting the number of obligate crossovers, or by a likelihood analysis. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. maxit Maximum number of EM iterations to perform. tol Tolerance for determining convergence. sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. verbose If TRUE, information about the number of orders (and, if method="likelihood", about progress) are printed. n.cluster If the package snow is available and n.perm > 0, permutations are run in parallel using this number of nodes. This is really only useful with method="likelihood". Details For method="likelihood", calculations are done by first constructing a matrix of marker orders and then making repeated calls to the R function est.map. Of course, it would be faster to do everything within C, but this was a lot easier to code. For method="countxo", calculations are done within C. 206 scanone Value A matrix, given class "ripple"; the first set of columns are marker indices describing the order. In the case of method="countxo", the last column is the number of obligate crossovers for each particular order. In the case of method="likelihood", the last two columns are LOD scores (log base 10 likelihood ratios) comparing each order to the initial order and the estimated chromosome length for the given order. Positive LOD scores indicate that the alternate order has more support than the original. Author(s) Karl W Broman, See Also summary.ripple, switch.order, est.map, est.rf Examples data(badorder) rip1 <- ripple(badorder, chr=1, window=3) summary(rip1) ## Not run: rip2 <- ripple(badorder, chr=1, window=2, method="likelihood") summary(rip2) ## End(Not run) badorder <- switch.order(badorder, 1, rip1[2,]) scanone Genome scan with a single QTL model Description Genome scan with a single QTL model, with possible allowance for covariates, using any of several possible models for the phenotype and any of several possible numerical methods. Usage scanone(cross, chr, pheno.col=1, model=c("normal","binary","2part","np"), method=c("em","imp","hk","ehk","mr","mr-imp","mr-argmax"), addcovar=NULL, intcovar=NULL, weights=NULL, use=c("all.obs", "complete.obs"), upper=FALSE, ties.random=FALSE, start=NULL, maxit=4000, tol=1e-4, n.perm, perm.Xsp=FALSE, perm.strata=NULL, verbose, batchsize=250, n.cluster=1, ind.noqtl) scanone 207 Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers; for methods "hk" and "imp" this can be considerably faster than doing them one at a time. One may also give a character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. model The phenotype model: the usual normal model, a model for binary traits, a twopart model or non-parametric analysis method Indicates whether to use the EM algorithm, imputation, Haley-Knott regression, the extended Haley-Knott method, or marker regression. Not all methods are available for all models. Marker regression is performed either by dropping individuals with missing genotypes ("mr"), or by first filling in missing data using a single imputation ("mr-imp") or by the Viterbi algorithm ("mr-argmax"). addcovar Additive covariates; allowed only for the normal and binary models. intcovar Interactive covariates (interact with QTL genotype); allowed only for the normal and binary models. weights Optional weights of individuals. Should be either NULL or a vector of length n.ind containing positive weights. Used only in the case model="normal". use In the case that multiple phenotypes are selected to be scanned, this argument indicates whether to use all individuals, including those missing some phenotypes, or just those individuals that have data on all selected phenotypes. upper Used only for the two-part model; if true, the "undefined" phenotype is the maximum observed phenotype; otherwise, it is the smallest observed phenotype. ties.random Used only for the non-parametric "model"; if TRUE, ties in the phenotypes are ranked at random. If FALSE, average ranks are used and a corrected LOD score is calculated. start Used only for the EM algorithm with the normal model and no covariates. If NULL, use the usual starting values; if length 1, use random initial weights for EM; otherwise, this should be a vector of length n+1 (where n is the number of possible genotypes for the cross), giving the initial values for EM. maxit Maximum number of iterations for methods "em" and "ehk". tol Tolerance value for determining convergence for methods "em" and "ehk". n.perm If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates. perm.Xsp If n.perm > 0, so that a permutation test will be performed, this indicates whether separate permutations should be performed for the autosomes and the X chromosome, in order to get an X-chromosome-specific LOD threshold. In this case, additional permutations are performed for the X chromosome. 208 scanone perm.strata If n.perm > 0, this may be used to perform a stratified permutation test. This should be a vector with the same number of individuals as in the cross data. Unique values indicate the individual strata, and permutations will be performed within the strata. verbose In the case n.perm is specified, display information about the progress of the permutation tests. batchsize The number of phenotypes (or permutations) to be run as a batch; used only for methods "hk" and "imp". n.cluster If the package snow is available and n.perm > 0, permutations are run in parallel using this number of nodes. ind.noqtl Indicates individuals who should not be allowed a QTL effect (used rarely, if at all); this is a logical vector of same length as there are individuals in the cross. Details Use of the EM algorithm, Haley-Knott regression, and the extended Haley-Knott method require that multipoint genotype probabilities are first calculated using calc.genoprob. The imputation method uses the results of sim.geno. Individuals with missing phenotypes are dropped. In the case that n.perm>0, so that a permutation test is performed, the R function scanone is called repeatedly. If perm.Xsp=TRUE, separate permutations are performed for the autosomes and the X chromosome, so that an X-chromosome-specific threshold may be calculated. In this case, n.perm specifies the number of permutations used for the autosomes; for the X chromosome, n.perm × LA /LX permutations will be run, where LA and LX are the total genetic lengths of the autosomes and X chromosome, respectively. More permutations are needed for the X chromosome in order to obtain thresholds of similar accuracy. For further details on the models, the methods and the use of covariates, see below. Value If n.perm is missing, the function returns a data.frame whose first two columns contain the chromosome IDs and cM positions. Subsequent columns contain the LOD scores for each phenotype. In the case of the two-part model, there are three LOD score columns for each phenotype: LOD(p, µ), LOD(p) and LOD(µ). The result is given class "scanone" and has attributes "model", "method", and "type" (the latter is the type of cross analyzed). If n.perm is specified, the function returns the results of a permutation test and the output has class "scanoneperm". If perm.Xsp=FALSE, the function returns a matrix with n.perm rows, each row containing the genome-wide maximum LOD score for each of the phenotypes. In the case of the two-part model, there are three columns for each phenotype, corresponding to the three different LOD scores. If perm.Xsp=TRUE, the result contains separate permutation results for the autosomes and the X chromosome respectively, and an attribute indicates the lengths of the chromosomes and an indicator of which chromosome is X. Models The normal model is the standard model for QTL mapping (see Lander and Botstein 1989). The residual phenotypic variation is assumed to follow a normal distribution, and analysis is analogous to analysis of variance. The binary model is for the case of a binary phenotype, which must have values 0 and 1. The proportions of 1’s in the different genotype groups are compared. Currently only methods em, hk, and mr are available for this model. See Xu and Atchley (1996) and Broman (2003). scanone 209 The two-part model is appropriate for the case of a spike in the phenotype distribution (for example, metastatic density when many individuals show no metastasis, or survival time following an infection when individuals may recover from the infection and fail to die). The two-part model was described by Boyartchuk et al. (2001) and Broman (2003). Individuals with QTL genotype g have probability pg of having an undefined phenotype (the spike), while if their phenotype is defined, it comes from a normal distribution with mean µg and common standard deviation σ. Three LOD scores are calculated: LOD(p, µ) is for the test of the hypothesis that pg = p and µg = µ. LOD(p) is for the test that pg = p while the µg may vary. LOD(µ) is for the test that µg = µ while the pg may vary. With the non-parametric "model", an extension of the Kruskal-Wallis test is used; this is similar to the method described by Kruglyak and Lander (1995). In the case of incomplete genotype information (such as at locations between genetic markers), the Kruskal-Wallis statistic is modified so that the rank for each individual is weighted by the genotype probabilities, analogous to HaleyKnott regression. For this method, if the argument ties.random is TRUE, ties in the phenotypes are assigned random ranks; if it is FALSE, average ranks are used and a corrected LOD score is calculate. Currently the method argument is ignored for this model. Methods em: maximum likelihood is performed via the EM algorithm (Dempster et al. 1977), first used in this context by Lander and Botstein (1989). imp: multiple imputation is used, as described by Sen and Churchill (2001). hk: Haley-Knott regression is used (regression of the phenotypes on the multipoint QTL genotype probabilities), as described by Haley and Knott (1992). ehk: the extended Haley-Knott method is used (like H-K, but taking account of the variances), as described in Feenstra et al. (2006). mr: Marker regression is used. Analysis is performed only at the genetic markers, and individuals with missing genotypes are discarded. See Soller et al. (1976). Covariates Covariates are allowed only for the normal and binary models. The normal model is y = βq + Aγ + Zδq + where q is the unknown QTL genotype, A is a matrix of additive covariates, and Z is a matrix of covariates that interact with the QTL genotype. The columns of Z are forced to be contained in the matrix A. The binary model is the logistic regression analog. The LOD score is calculated comparing the likelihood of the above model to that of the null model y = µ + Aγ + . Covariates must be numeric matrices. Individuals with any missing covariates are discarded. X chromosome The X chromosome must be treated specially in QTL mapping. See Broman et al. (2006). If both males and females are included, male hemizygotes are allowed to be different from female homozygotes. Thus, in a backcross, we will fit separate means for the genotype classes AA, AB, AY, and BY. In such cases, sex differences in the phenotype could cause spurious linkage to the X chromosome, and so the null hypothesis must be changed to allow for a sex difference in the phenotype. Numerous special cases must be considered, as detailed in the following table. BC Sexes Null Alternative df 210 scanone F2 Direction Both Forward Backward both sexes all female all male sex grand mean grand mean AA/AB/AY/BY AA/AB AY/BY 2 1 1 Sexes both sexes all female all male both sexes all female all male both sexes all female all male Null femaleF/femaleR/male pgm grand mean sex grand mean grand mean sex grand mean grand mean Alternative AA/ABf/ABr/BB/AY/BY AA/ABf/ABr/BB AY/BY AA/AB/AY/BY AA/AB AY/BY AB/BB/AY/BY AB/BB AY/BY df 3 2 1 2 1 1 2 1 1 In the case that the number of degrees of freedom for the linkage test for the X chromosome is different from that for autosomes, a separate X-chromosome LOD threshold is recommended. Autosomeand X-chromosome-specific LOD thresholds may be estimated by permutation tests with scanone by setting n.perm>0 and using perm.Xsp=TRUE. Author(s) Karl W Broman, ; Hao Wu References Boyartchuk, V. L., Broman, K. W., Mosher, R. E., D’Orazio S. E. F., Starnbach, M. N. and Dietrich, W. F. (2001) Multigenic control of Listeria monocytogenes susceptibility in mice. Nature Genetics 27, 259–260. Broman, K. W. (2003) Mapping quantitative trait loci in the case of a spike in the phenotype distribution. Genetics 163, 1169–1175. Broman, K. W., Sen, Ś, Owens, S. E., Manichaikul, A., Southard-Smith, E. M. and Churchill G. A. (2006) The X chromosome in quantitative trait locus mapping. Genetics, 174, 2151–2158. Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B, 39, 1–38. Feenstra, B., Skovgaard, I. M. and Broman, K. W. (2006) Mapping quantitative trait loci by an extension of the Haley-Knott regression method using estimating equations. Genetics, 173, 2111– 2119. Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Kruglyak, L. and Lander, E. S. (1995) A nonparametric approach for mapping quantitative trait loci. Genetics 139, 1421–1428. Lander, E. S. and Botstein, D. (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. scanone 211 Soller, M., Brody, T. and Genizi, A. (1976) On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor. Appl. Genet. 47, 35–39. Xu, S., and Atchley, W.R. (1996) Mapping quantitative trait loci for complex binary diseases using line crosses. Genetics 143, 1417–1424. See Also plot.scanone, summary.scanone, scantwo, calc.genoprob, sim.geno, max.scanone, summary.scanoneperm, -.scanone, +.scanone Examples ################### # Normal Model ################### data(hyper) # Genotype probabilities for EM and H-K ## Not run: hyper <- calc.genoprob(hyper, step=2.5) out.em <- scanone(hyper, method="em") out.hk <- scanone(hyper, method="hk") # Summarize results: peaks above 3 summary(out.em, thr=3) summary(out.hk, thr=3) # An alternate method of summarizing: # patch them together and then summarize out <- c(out.em, out.hk) summary(out, thr=3, format="allpeaks") # Plot the results plot(out.hk, out.em) plot(out.hk, out.em, chr=c(1,4), lty=1, col=c("blue","black")) # Imputation; first need to run sim.geno # Do just chromosomes 1 and 4, to save time ## Not run: hyper.c1n4 <- sim.geno(subset(hyper, chr=c(1,4)), step=2.5, n.draws=8) ## End(Not run) out.imp <- scanone(hyper.c1n4, method="imp") summary(out.imp, thr=3) # Plot all three results plot(out.imp, out.hk, out.em, chr=c(1,4), lty=1, col=c("red","blue","black")) # extended Haley-Knott out.ehk <- scanone(hyper, method="ehk") plot(out.hk, out.em, out.ehk, chr=c(1,4)) # Permutation tests ## Not run: permo <- scanone(hyper, method="hk", n.perm=1000) 212 scanone # Threshold from the permutation test summary(permo, alpha=c(0.05, 0.10)) # Results above the 0.05 threshold summary(out.hk, perms=permo, alpha=0.05) #################### # scan with square-root of phenotype # (Note that pheno.col can be a vector of phenotype values) #################### out.sqrt <- scanone(hyper, pheno.col=sqrt(pull.pheno(hyper, 1))) plot(out.em - out.sqrt, ylim=c(-0.1,0.1), ylab="Difference in LOD") abline(h=0, lty=2, col="gray") #################### # Stratified permutations #################### extremes <- (nmissing(hyper)/totmar(hyper) < 0.5) ## Not run: operm.strat <- scanone(hyper, method="hk", n.perm=1000, perm.strata=extremes) ## End(Not run) summary(operm.strat) #################### # X-specific permutations #################### data(fake.f2) ## Not run: fake.f2 <- calc.genoprob(fake.f2, step=2.5) # genome scan out <- scanone(fake.f2, method="hk") # X-chr-specific permutations ## Not run: operm <- scanone(fake.f2, method="hk", n.perm=1000, perm.Xsp=TRUE) # thresholds summary(operm) # scanone summary with p-values summary(out, perms=operm, alpha=0.05, pvalues=TRUE) ################### scanone # Non-parametric ################### out.np <- scanone(hyper, model="np") summary(out.np, thr=3) # Plot with previous results plot(out.np, chr=c(1,4), lty=1, col="green") plot(out.imp, out.hk, out.em, chr=c(1,4), lty=1, col=c("red","blue","black"), add=TRUE) ################### # Two-part Model ################### data(listeria) ## Not run: listeria <- calc.genoprob(listeria,step=2.5) out.2p <- scanone(listeria, model="2part", upper=TRUE) summary(out.2p, thr=c(5,3,3), format="allpeaks") # Plot all three LOD scores together plot(out.2p, out.2p, out.2p, lodcolumn=c(2,3,1), lty=1, chr=c(1,5,13), col=c("red","blue","black")) # Permutation test ## Not run: permo <- scanone(listeria, model="2part", upper=TRUE, n.perm=1000) ## End(Not run) # Thresholds summary(permo) ################### # Binary model ################### binphe <- as.numeric(pull.pheno(listeria,1)==264) out.bin <- scanone(listeria, pheno.col=binphe, model="binary") summary(out.bin, thr=3) # Plot LOD for binary model with LOD(p) from 2-part model plot(out.bin, out.2p, lodcolumn=c(1,2), lty=1, col=c("black", "red"), chr=c(1,5,13)) # Permutation test ## Not run: permo <- scanone(listeria, pheno.col=binphe, model="binary", n.perm=1000) ## End(Not run) # Thresholds summary(permo) ################### # Covariates ################### data(fake.bc) 213 214 scanoneboot ## Not run: fake.bc <- calc.genoprob(fake.bc, step=2.5) # genome scans without covariates out.nocovar <- scanone(fake.bc) # genome scans with covariates ac <- pull.pheno(fake.bc, c("sex","age")) ic <- pull.pheno(fake.bc, "sex") out.covar <- scanone(fake.bc, pheno.col=1, addcovar=ac, intcovar=ic) summary(out.nocovar, thr=3) summary(out.covar, thr=3) plot(out.covar, out.nocovar, chr=c(2,5,10)) scanoneboot Bootstrap to get interval estimate of QTL location Description Nonparametric bootstrap to get an estimated confidence interval for the location of a QTL, in the context of a single-QTL model. Usage scanoneboot(cross, chr, pheno.col=1, model=c("normal","binary","2part","np"), method=c("em","imp","hk","ehk","mr","mr-imp","mr-argmax"), addcovar=NULL, intcovar=NULL, weights=NULL, use=c("all.obs", "complete.obs"), upper=FALSE, ties.random=FALSE, start=NULL, maxit=4000, tol=1e-4, n.boot=1000, verbose=FALSE) Arguments cross An object of class cross. See read.cross for details. chr The chromosome to investigate. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.) pheno.col Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either nonintegers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. model The phenotypic model: the usual normal model, a model for binary traits, a two-part model or non-parametric analysis method Indicates whether to use the EM algorithm, imputation, Haley-Knott regression, the extended Haley-Knott method, or marker regression. Not all methods are available for all models. Marker regression is performed either by dropping individuals with missing genotypes ("mr"), or by first filling in missing data using a single imputation ("mr-imp") or by the Viterbi algorithm ("mr-argmax"). scanoneboot addcovar intcovar weights use upper ties.random start maxit tol n.boot verbose 215 Additive covariates; allowed only for the normal and binary models. Interactive covariates (interact with QTL genotype); allowed only for the normal and binary models. Optional weights of individuals. Should be either NULL or a vector of length n.ind containing positive weights. Used only in the case model="normal". In the case that multiple phenotypes are selected to be scanned, this argument indicates whether to use all individuals, including those missing some phenotypes, or just those individuals that have data on all selected phenotypes. Used only for the two-part model; if true, the "undefined" phenotype is the maximum observed phenotype; otherwise, it is the smallest observed phenotype. Used only for the non-parametric "model"; if TRUE, ties in the phenotypes are ranked at random. If FALSE, average ranks are used and a corrected LOD score is calculated. Used only for the EM algorithm with the normal model and no covariates. If NULL, use the usual starting values; if length 1, use random initial weights for EM; otherwise, this should be a vector of length n+1 (where n is the number of possible genotypes for the cross), giving the initial values for EM. Maximum number of iterations for methods "em" and "ehk". Tolerance value for determining convergence for methods "em" and "ehk". Number of bootstrap replicates. If TRUE, display information about the progress of the bootstrap. Details We recommend against the use of the bootstrap to derive a confidence interval for the location of a QTL; see Manichaikul et al. (2006). Use lodint or bayesint instead. The bulk of the arguments are the same as for the scanone function. A single chromosome should be indicated with the chr argument; otherwise, we focus on the first chromosome in the input cross object. A single-dimensional scan on the relevant chromosome is performed. We further perform a nonparametric bootstrap (sampling individuals with replacement from the available data, to create a new data set with the same size as the input cross; some individuals with be duplicated and some omitted). The same scan is performed with the resampled data; for each bootstrap replicate, we store only the location with maximum LOD score. Use summary.scanoneboot to obtain the desired confidence interval. Value A vector of length n.boot, giving the estimated QTL locations in the bootstrap replicates. The results for the original data are included as an attribute, "results". Author(s) Karl W Broman, References Manichaikul, A., Dupuis, J., Sen, Ś and Broman, K. W. (2006) Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus. Genetics 174, 481–489. Visscher, P. M., Thompson, R. and Haley, C. S. (1996) Confidence intervals in QTL mapping by bootstrap. Genetics 143, 1013–1020. 216 scanonevar See Also scanone, summary.scanoneboot, plot.scanoneboot, lodint, bayesint Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=1, err=0.001) ## Not run: bootoutput <- scanoneboot(fake.f2, chr=13, method="hk") plot(bootoutput) summary(bootoutput) scanonevar Genome scan for QTL affecting mean and/or variance Description Genome scan with a single QTL model for loci that can affect the variance as well as the mean. Usage scanonevar(cross, pheno.col=1, mean_covar=NULL, var_covar=NULL, maxit=25, tol=1e-6, quiet=TRUE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This must be a single value (integer index or phenotype name) or a numeric vector of phenotype values, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. mean_covar Numeric matrix with covariates affecting the mean. var_covar Numeric matrix with covariates affecting the variances. maxit Maximum number of iterations in the algorithm to fit the model at a given position. tol Tolerance for convergence. quiet If FALSE, print some information about the course of the calculations. Value A data frame (with class "scanone", in the form output by scanone), with four columns: chromosome, position, the -log P-value for the mean effect, and the -log P-value for the effect on the variance. The result is given class "scanone" Author(s) Lars Ronnegard and Karl Broman scanonevar.meanperm 217 References Ronnegard, L. and Valdar W. (2011) Detecting major genetic loci controlling phenotypic variability in experimental crosses. Genetics 188:435-447 Ronnegard, L. and Valdar W. (2012) Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genetics 13:63 See Also scanone, summary.scanone, calc.genoprob, summary.scanoneperm Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2.5) out <- scanonevar(fake.bc) color <- c("slateblue", "violetred") plot(out, lod=1:2, col=color, bandcol="gray80") legend("topright", lwd=2, c("mean", "variance"), col=color) # use format="allpeaks" to get summary for each of mean and variance # also consider format="tabByCol" or format="tabByChr" summary(out, format="allpeaks") # with sex and age as covariates covar <- fake.bc$pheno[,c("sex", "age")] out.cov <- scanonevar(fake.bc, mean_covar=covar, var_covar=covar) scanonevar.meanperm Permutation test for mean effect in scanonevar Description Executes permutations of the genotypes in the mean-effect part of scanonevar Usage scanonevar.meanperm(cross, pheno.col=1, mean_covar=NULL, var_covar=NULL, maxit=25, tol=1e-6, n.mean.perm = 2, seed = 27517, quiet=TRUE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This must be a single value (integer index or phenotype name) or a numeric vector of phenotype values, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. mean_covar Numeric matrix with covariates affecting the mean. var_covar Numeric matrix with covariates affecting the variances. 218 scanonevar.varperm maxit Maximum number of iterations in the algorithm to fit the model at a given position. tol Tolerance for convergence. n.mean.perm Numeric vector of length one indicates the number of permutations to execute. seed Numeric vector of length one indicates the random seed to start the permutations. quiet If FALSE, print some information about the course of the calculations. Value A vector of length n.mean.perm of the maximum negative log10 p-value that resulted from each permutation. scanonevar.varperm Permutation test for variance effect in scanonevar Description Executes permutations of the genotypes in the variance-effect part of scanonevar Usage scanonevar.varperm(cross, pheno.col=1, mean_covar=NULL, var_covar=NULL, maxit=25, tol=1e-6, n.var.perm = 2, seed = 27517, quiet=TRUE) Arguments cross An object of class cross. See read.cross for details. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This must be a single value (integer index or phenotype name) or a numeric vector of phenotype values, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. mean_covar Numeric matrix with covariates affecting the mean. var_covar Numeric matrix with covariates affecting the variances. maxit Maximum number of iterations in the algorithm to fit the model at a given position. tol Tolerance for convergence. n.var.perm Numeric vector of length one indicates the number of permutations to execute. seed Numeric vector of length one indicates the random seed to start the permutations. quiet If FALSE, print some information about the course of the calculations. Value A vector of length n.var.perm of the maximum negative log10 p-value that resulted from each permutation. scanPhyloQTL scanPhyloQTL 219 Single-QTL genome scan to map QTL to a phylogenetic tree Description Jointly consider multiple intercrosses with a single diallelic QTL model, considering all possible partitions of the strains into the two QTL allele groups. Usage scanPhyloQTL(crosses, partitions, chr, pheno.col=1, model=c("normal", "binary"), method=c("em", "imp", "hk"), addcovar, maxit=4000, tol=0.0001, useAllCrosses=TRUE, verbose=FALSE) Arguments crosses A list with each component being an intercross, as an object of class cross (see read.cross for details). The names (of the form "AB") indicate the strains in the cross. partitions A vector of character strings of the form "AB|CD" or "A|BCD" indicating the set of paritions of the strains into two allele groups. If missing, all partitions should be considered. chr Optional vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers; for methods "hk" and "imp" this can be considerably faster than doing them one at a time. One may also give a character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. model The phenotype model: the usual normal model or a model for binary traits method Indicates whether to use the EM algorithm, imputation, or Haley-Knott regression. addcovar Optional set of additive covariates to include in the analysis, as a list with the same length as crosses. They must be numeric vectors or matrices, as for scanone. maxit Maximum number of iterations for method "em". tol Tolerance value for determining convergence for method "em". useAllCrosses If TRUE, use all crosses in the analysis of all partitions, with crosses not segregating the QTL included in the estimation of the residual variance. verbose If TRUE, print information about progress. 220 scanPhyloQTL Details The aim is to jointly consider multiple intercrosses to not just map QTL but to also, under the assumption of a single diallelic QTL, identify the set of strains with each QTL allele. For each partition (of the strains into two groups) that is under consideration, we pull out the set of crosses that are segregating the QTL, re-code the alleles, and combine the crosses into one large cross. Crosses not segregating the QTL are also used, though with no QTL effects. Additive covariate indicators for the crosses are included in the analysis, to allow for the possibility that there are overall shifts in the phenotypes between crosses. Value A data frame, as for the output of scanone, though with LOD score columns for each partition that is considered. The result is given class "scanPhyloQTL". Author(s) Karl W Broman, References Broman, K. W., Kim, S., An\’e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation. See Also plot.scanPhyloQTL, summary.scanPhyloQTL, max.scanPhyloQTL, inferredpartitions, simPhyloQTL Examples # example map; drop X chromosome data(map10) map10 <- map10[1:19] # simulate data x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"), map=map10, n.ind=150, model=c(1, 50, 0.5, 0)) # run calc.genoprob on each cross ## Not run: x <- lapply(x, calc.genoprob, step=2) # scan genome, at each position trying all possible partitions out <- scanPhyloQTL(x, method="hk") # maximum peak max(out, format="lod") # approximate posterior probabilities at peak max(out, format="postprob") # all peaks above a threshold for LOD(best) - LOD(2nd best) summary(out, threshold=1, format="lod") scanqtl 221 # all peaks above a threshold for LOD(best), showing approx post'r prob summary(out, format="postprob", threshold=3) # plot results plot(out) scanqtl General QTL scan Description Performs a multiple QTL scan for specified chromosomes and positions or intervals, with the possible inclusion of QTL-QTL interactions and/or covariates. Usage scanqtl(cross, pheno.col=1, chr, pos, covar=NULL, formula, method=c("imp","hk"), model=c("normal", "binary"), incl.markers=FALSE, verbose=TRUE, tol=1e-4, maxit=1000, forceXcovar=FALSE) Arguments cross pheno.col chr pos covar formula method model incl.markers verbose tol maxit forceXcovar An object of class cross. See read.cross for details. Column number in the phenotype matrix to be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. Vector indicating the chromosome for each QTL. (These should be character strings referring to the chromosomes by name.) List indicating the positions or intervals on the chromosome to be scanned. Each element should be either a single number (for a specific position) or a pair of numbers (for an interval). A matrix or data.frame of covariates. These must be strictly numeric. An object of class formula indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are indicated as Q1, Q2, etc. Covariates are indicated by their names in covar. Indicates whether to use multiple imputation or Haley-Knott regression. The phenotype model: the usual model or a model for binary traits If FALSE, do calculations only at points on an evenly spaced grid. If calc.genoprob or sim.geno were run with stepwidth="variable" or stepwidth="max", we force incl.markers=TRUE. If TRUE, give feedback about progress. Tolerance for convergence for the binary trait model. Maximum number of iterations for fitting the binary trait model. If TRUE, force inclusion of X-chr-related covariates (like sex and cross direction). 222 scanqtl Details The formula is used to specified the model to be fit. In the formula, use Q1, Q2, etc., or q1, q2, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. We enforce a hierarchical structure on the model formula: if a QTL or covariate is in involved in an interaction, its main effect are also be included. Only the interaction terms need to be specifed in the formula. The main effects of all input QTLs (as specified by chr and pos) and covariates (as specifed by covar) will be included by default. For example, if the formula is y~Q1*Q2*Sex, and there are three elements in input chr and pos and Sex is one of the column names for input covariates, the formula used in genome scan will be y ~ Q1 + Q2 + Q3 + Sex + Q1:Q2 + Q1:Sex + Q2:Sex + Q1:Q2:Sex. The input pos is a list or vector to specify the position/range of the input chromosomes to be scanned. If it is a vector, it gives the precise positions of the QTL on the chromosomes. If it is a list, it will contain either the precise positions or a range on the chromosomes. For example, consider the case that the input chr = c(1, 6, 13). If pos = c(9.8, 34.0, 18.6), it means to fit a model with QTL on chromosome 1 at 9.8cM, chromosome 6 at 34cM and chromosome 13 at 18.6cM. If pos = list(c(5,15), c(30,36), 18), it means to scan chromosome 1 from 5cM to 15cM, chromosome 6 from 30cM to 36cM, fix the QTL on chromosome 13 at 18cM. Value An object of class scanqtl. It is a multi-dimensional array of LOD scores, with the number of dimension equal to the number of QTLs specifed. Author(s) Hao Wu References Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. See Also fitqtl, makeqtl, refineqtl Examples data(fake.f2) # take out several QTLs qc <- c(1, 8, 13) fake.f2 <- subset(fake.f2, chr=qc) # imputate genotypes fake.f2 <- calc.genoprob(fake.f2, step=5, err=0.001) # 2-dimensional genome scan with additive 3-QTL model pos <- list(c(15,35), c(45,65), 28) result <- scanqtl(fake.f2, pheno.col=1, chr=qc, pos=pos, scantwo 223 formula=y~Q1+Q2+Q3, method="hk") # image of the results # chr locations chr1 <- as.numeric(matrix(unlist(strsplit(colnames(result),"@")), ncol=2,byrow=TRUE)[,2]) chr8 <- as.numeric(matrix(unlist(strsplit(rownames(result),"@")), ncol=2,byrow=TRUE)[,2]) # image plot image(chr1, chr8, t(result), las=1, col=rev(rainbow(256,start=0,end=2/3))) # do the same, allowing the QTLs on chr 1 and 13 to interact result2 <- scanqtl(fake.f2, pheno.col=1, chr=qc, pos=pos, formula=y~Q1+Q2+Q3+Q1:Q3, method="hk") # image plot image(chr1, chr8, t(result2), las=1, col=rev(rainbow(256,start=0,end=2/3))) scantwo Two-dimensional genome scan with a two-QTL model Description Perform a two-dimensional genome scan with a two-QTL model, with possible allowance for covariates. Usage scantwo(cross, chr, pheno.col=1, model=c("normal","binary"), method=c("em","imp","hk","mr","mr-imp","mr-argmax"), addcovar=NULL, intcovar=NULL, weights=NULL, use=c("all.obs", "complete.obs"), incl.markers=FALSE, clean.output=FALSE, clean.nmar=1, clean.distance=0, maxit=4000, tol=1e-4, verbose=TRUE, n.perm, perm.Xsp=FALSE, perm.strata=NULL, assumeCondIndep=FALSE, batchsize=250, n.cluster=1) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This can be a vector of integers; for methods "hk" and "imp" this can be considerably faster than doing them one at a time. One may also give character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. 224 scantwo model The phenotype model: the usual normal model or a model for binary traits. method Indicates whether to use the the EM algorithm, imputation, Haley-Knott regression, or marker regression. Marker regression is performed either by dropping individuals with missing genotypes ("mr"), or by first filling in missing data using a single imputation ("mr-imp") or by the Viterbi algorithm ("mr-argmax"). addcovar Additive covariates. intcovar Interactive covariates (interact with QTL genotype). weights Optional weights of individuals. Should be either NULL or a vector of length n.ind containing positive weights. Used only in the case model="normal". use In the case that multiple phenotypes are selected to be scanned, this argument indicates whether to use all individuals, including those missing some phenotypes, or just those individuals that have data on all selected phenotypes. incl.markers If FALSE, do calculations only at points on an evenly spaced grid. If calc.genoprob or sim.geno were run with stepwidth="variable" or stepwidth="max", we force incl.markers=TRUE. clean.output If TRUE, clean the output with clean.scantwo, replacing LOD scores for pairs of positions that are not well separated with 0. In permutations, this will be done for each permutation replicate. This can be important for the case of method="em", as there can be difficulty with algorithm convergence in these regions. clean.nmar If clean.output=TRUE, this is the number of markers that must separate two positions. clean.distance If clean.output=TRUE, this is the cM distance that must separate two positions. maxit Maximum number of iterations; used only with method "em". tol Tolerance value for determining convergence; used only with method "em". verbose If TRUE, display information about the progress of calculations. For method "em", if verbose is an integer above 1, further details on the progress of the algorithm will be displayed. n.perm If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates. perm.Xsp If n.perm > 0, so that a permutation test will be performed, this indicates whether separate permutations should be performed for the autosomes and the X chromosome, in order to get an X-chromosome-specific LOD threshold. In this case, additional permutations are performed for the X chromosome. perm.strata If n.perm > 0, this may be used to perform a stratified permutation test. This should be a vector with the same number of individuals as in the cross data. Unique values indicate the individual strata, and permutations will be performed within the strata. assumeCondIndep If TRUE, assume conditional independence of QTL genotypes given marker genotypes. This is an approximation, but it may speed things up. batchsize The number of phenotypes (or permutations) to be run as a batch; used only for methods "hk" and "imp". n.cluster If the package snow is available and n.perm > 0, permutations are run in parallel using this number of nodes. scantwo 225 Details Standard interval mapping (method="em") and Haley-Knott regression (method="hk") require that multipoint genotype probabilities are first calculated using calc.genoprob. The imputation method uses the results of sim.geno. The method "em" is standard interval mapping by the EM algorithm (Dempster et al. 1977; Lander and Botstein 1989). Marker regression (method="mr") is simply linear regression of phenotypes on marker genotypes (individuals with missing genotypes are discarded). Haley-Knott regression (method="hk") uses the regression of phenotypes on multipoint genotype probabilities. The imputation method (method="imp") uses the pseudomarker algorithm described by Sen and Churchill (2001). Individuals with missing phenotypes are dropped. In the presence of covariates, the full model is y = µ + βq1 + βq2 + βq1 ×q2 + Aγ + Zδq1 + Zδq2 + Zδq1 ×q2 + where q1 and q2 are the unknown QTL genotypes at two locations, A is a matrix of covariates, and Z is a matrix of covariates that interact with QTL genotypes. The columns of Z are forced to be contained in the matrix A. The above full model is compared to the additive QTL model, y = µ + βq1 + βq2 + Aγ + Zδq1 + Zδq2 + and also to the null model, with no QTL, y = µ + Aγ + In the case that n.perm is specified, the R function scantwo is called repeatedly. For model="binary", a logistic regression model is used. Value If n.perm is missing, the function returns a list with class "scantwo" and containing three components. The first component is a matrix of dimension [tot.pos x tot.pos]; the upper triangle contains the LOD scores for the additive model, and the lower triangle contains the LOD scores for the full model. The diagonal contains the results of scanone. The second component of the output is a data.frame indicating the locations at which the two-QTL LOD scores were calculated. The first column is the chromosome identifier, the second column is the position in cM, the third column is a 1/0 indicator for ease in later pulling out only the equally spaced positions, and the fourth column indicates whether the position is on the X chromosome or not. The final component is a version of the results of scanone including sex and/or cross direction as additive covariates, which is needed for a proper calculation of conditional LOD scores. If n.perm is specified, the function returns a list with six different LOD scores from each of the permutation replicates. First, the maximum LOD score for the full model (two QTLs plus an interaction). Second, for each pair of chromosomes, we take the difference between the full LOD and the maximum single-QTL LOD for those two chromosomes, and then maximize this across chromosome pairs. Third, for each pair of chromosomes we take the difference between the maximum full LOD and the maximum additive LOD, and then maximize this across chromosome pairs. Fourth, the maximum LOD score for the additive QTL model. Fifth, for each pair of chromosomes, we take the difference between the additive LOD and the maximum single-QTL LOD for those two chromosomes, and then maximize this across chromosome pairs. Finally, the maximum single-QTL LOD score (that is, from a single-QTL scan). The latter is not used in summary.scantwo, but does get calculated at each permutation, so we include it for the sake of completeness. 226 scantwo If n.perm is specified and perm.Xsp=TRUE, the result is a list with the permutation results for the regions A:A, A:X, and X:X, each of which is a list with the six different LOD scores. Independent permutations are performed in each region, n.perm is the number of permutations for the A:A region; additional permutations are are used for the A:X and X:X parts, as estimates of quantiles farther out into the tails are needed. X chromosome The X chromosome must be treated specially in QTL mapping. As in scanone, if both males and females are included, male hemizygotes are allowed to be different from female homozygotes, and the null hypothesis must be changed in order to ensure that sex- or pgm-differences in the phenotype do not results in spurious linkage to the X chromosome. (See the help file for scanone.) If n.perm is specified and perm.Xsp=TRUE, X-chromosome-specific permutations are performed, to obtain separate thresholds for the regions A:A, A:X, and X:X. Author(s) Karl W Broman, ; Hao Wu References Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B, 39, 1–38. Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Lander, E. S. and Botstein, D. (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. Soller, M., Brody, T. and Genizi, A. (1976) On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor. Appl. Genet. 47, 35–39. See Also plot.scantwo, summary.scantwo, scanone, max.scantwo, summary.scantwoperm, c.scantwoperm Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=5) out.2dim <- scantwo(fake.f2, method="hk") plot(out.2dim) # permutations ## Not run: permo.2dim <- scantwo(fake.f2, method="hk", n.perm=1000) summary(permo.2dim, alpha=0.05) scantwopermhk 227 # summary with p-values summary(out.2dim, perms=permo.2dim, pvalues=TRUE, alphas=c(0.05, 0.10, 0.10, 0.05, 0.10)) # covariates data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=10) ac <- pull.pheno(fake.bc, c("sex","age")) ic <- pull.pheno(fake.bc, "sex") out <- scantwo(fake.bc, method="hk", pheno.col=1, addcovar=ac, intcovar=ic) plot(out) scantwopermhk Permutation test for 2d genome scan by Haley-Knott regression Description Perform a permutation test with a two-dimensional genome scan with a two-QTL model, with possible allowance for additive covariates, by Haley-Knott regression. Usage scantwopermhk(cross, chr, pheno.col=1, addcovar=NULL, weights=NULL, n.perm=1, batchsize=1000, perm.strata=NULL, perm.Xsp=NULL, verbose=FALSE, assumeCondIndep=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix which should be used as the phenotype. This should be a single value (numeric index or character string for a phenotype name), but it may also be a vector of numeric values with length equal to the number of individuals in the cross, in which case it is taken to be a vector of individuals’ phenotypes. addcovar Additive covariates. weights Optional weights of individuals. Should be either NULL or a vector of length n.ind containing positive weights. Used only in the case model="normal". n.perm Number of permutation replicates. 228 scantwopermhk batchsize If n.perm > batchsize, permutations will be run in batches of no more than batchsize permutations. perm.strata Used to perform a stratified permutation test. This should be a vector with the same number of individuals as in the cross data. Unique values indicate the individual strata, and permutations will be performed within the strata. perm.Xsp If TRUE, run separate permutations for A:A, A:X, and X:X. In this case, n.perm refers to the number of permutations for the A:A part; more permutations are used for the A:X and X:X parts, as estimates of quantiles farther out into the tails are needed. verbose If TRUE, display information about the progress of calculations. assumeCondIndep If TRUE, assume conditional independence of QTL genotypes given marker genotypes. This is an approximation, but it may speed things up. Details This is a scaled-back version of the permutation test provided by scantwo: only for a normal model with Haley-Knott regression, and not allowing interactive covariates. This is an attempt to speed things up and attentuate the memory usage problems in scantwo. In the case of perm.Xsp=TRUE (X-chr-specific thresholds), we use a stratified permutation test, stratified by sex and cross-direction. Value A list with six different LOD scores from each of the permutation replicates. First, the maximum LOD score for the full model (two QTLs plus an interaction). Second, for each pair of chromosomes, we take the difference between the full LOD and the maximum single-QTL LOD for those two chromosomes, and then maximize this across chromosome pairs. Third, for each pair of chromosomes we take the difference between the maximum full LOD and the maximum additive LOD, and then maximize this across chromosome pairs. Fourth, the maximum LOD score for the additive QTL model. Fifth, for each pair of chromosomes, we take the difference between the additive LOD and the maximum single-QTL LOD for those two chromosomes, and then maximize this across chromosome pairs. Finally, the maximum single-QTL LOD score (that is, from a single-QTL scan). The latter is not used in summary.scantwoperm, but does get calculated at each permutation, so we include it for the sake of completeness. If perm.Xsp=TRUE, this is a list of lists, for the A:A, A:X, and X:X sections, each being a list as described above. Author(s) Karl W Broman, ; Hao Wu References Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. See Also scantwo, plot.scantwoperm, summary.scantwoperm, c.scantwoperm shiftmap 229 Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=5) operm <- scantwopermhk(fake.f2, n.perm=2) summary(operm, alpha=0.05) shiftmap Shift starting points in genetic maps Description Shift starting points in a genetic map to a set of defined positions Usage shiftmap(object, offset=0) Arguments object An object of class cross (see read.cross for details) or map (see sim.map for details). offset Defines the starting position for each chromosome. This should be a single value (to be used for all chromosomes) or a vector with length equal to the number of chromosomes, defining individual starting positions for each chromosome. For a sex-specific map (as in a 4-way cross), we use the same offset for both the male and female maps. Value If the input is a map object, a map object is returned; if the input is a cross object, a cross object is returned. In either case, the positions of markers are shifted so that the starting positions are as in offset. Author(s) Karl W Broman, See Also replace.map, est.map Examples data(hyper) shiftedhyper <- shiftmap(hyper, offset=0) par(mfrow=c(1,2)) plotMap(hyper, shift=FALSE, alternate.chrid=TRUE) plotMap(shiftedhyper, shift=FALSE, alternate.chrid=TRUE) 230 sim.cross sim.cross Simulate a QTL experiment Description Simulates data for a QTL experiment using a model in which QTLs act additively. Usage sim.cross(map, model=NULL, n.ind=100, type=c("f2", "bc", "4way", "risib", "riself", "ri4sib", "ri4self", "ri8sib", "ri8self", "bcsft"), error.prob=0, missing.prob=0, partial.missing.prob=0, keep.qtlgeno=TRUE, keep.errorind=TRUE, m=0, p=0, map.function=c("haldane","kosambi","c-f","morgan"), founderGeno, random.cross=TRUE, ...) Arguments map A list whose components are vectors containing the marker locations on each of the chromosomes. model A matrix where each row corresponds to a different QTL, and gives the chromosome number, cM position and effects of the QTL. n.ind Number of individuals to simulate. type Indicates whether to simulate an intercross (f2), a backcross (bc), a phaseknown 4-way cross (4way), or recombinant inbred lines (by selfing or by sibmating, and with the usual 2 founder strains or with 4 or 8 founder strains). error.prob The genotyping error rate. missing.prob The rate of missing genotypes. partial.missing.prob When simulating an intercross or 4-way cross, this gives the rate at which markers will be incompletely informative (i.e., dominant or recessive). keep.qtlgeno If TRUE, genotypes for the simulated QTLs will be included in the output. keep.errorind If TRUE, and if error.prob > 0, the identity of genotyping errors will be included in the output. m Interference parameter; a non-negative integer. 0 corresponds to no interference. p Probability that a chiasma comes from the no-interference mechanism map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. founderGeno For 4- or 8-way RIL, the genotype data of the founder strains, as a list whose components are numeric matrices (no. markers x no. founders), one for each chromosome. random.cross For 4- or 8-way RIL, indicates whether the order of the founder strains should be randomized, independently for each RIL, or whether all RIL be derived from a common cross. In the latter case, for a 4-way RIL, the cross would be (AxB)x(CxD). ... For type = "bcsft", additional arguments passed to sim.cross.bcsft. sim.cross 231 Details Meiosis is assumed to follow the Stahl model for crossover interference (see the references, below), of which the no interference model and the chi-square model are special cases. Chiasmata on the four-strand bundle are a superposition of chiasmata from two different mechanisms. With probability p, they arise by a mechanism exhibiting no interference; the remainder come from a chi-square model with inteference parameter m. Note that m=0 corresponds to no interference, and with p=0, one gets a pure chi-square model. If a chromosomes has class X, it is assumed to be the X chromosome, and is assumed to be segregating in the cross. Thus, in an intercross, it is segregating like a backcross chromosome. In a 4-way cross, a second phenotype, sex, will be generated. QTLs are assumed to act additively, and the residual phenotypic variation is assumed to be normally distributed with variance 1. For a backcross, the effect of a QTL is a single number corresponding to the difference between the homozygote and the heterozygote. For an intercross, the effect of a QTL is a pair of numbers, (a, d), where a is the additive effect (half the difference between the homozygotes) and d is the dominance deviation (the difference between the heterozygote and the midpoint between the homozygotes). For a four-way cross, the effect of a QTL is a set of three numbers, (a, b, c), where, in the case of one QTL, the mean phenotype, conditional on the QTL genotyping being AC, BC, AD or BD, is a, b, c or 0, respectively. Value An object of class cross. See read.cross for details. If keep.qtlgeno is TRUE, the cross object will contain a component qtlgeno which is a matrix containing the QTL genotypes (with complete data and no errors), coded as in the genotype data. If keep.errorind is TRUE and errors were simulated, each component of geno will each contain a matrix errors, with 1’s indicating simulated genotyping errors. Recombinant inbred lines In the simulation of recombinant inbred lines (RIL), we simulate a single individual from each line, and no phenotypes are simulated (so the argument model is ignored). The types riself and risib are the usual two-way RIL. The types ri4self, ri4sib, ri8self, and ri8sib are RIL by selfing or sib-mating derived from four or eight founding parental strains. For the 4- and 8-way RIL, one must include the genotypes of the founding individuals; these may be simulated with simFounderSnps. Also, the output cross will contain a component cross, which is a matrix with rows corresponding to RIL and columns corresponding to the founders, indicating order of the founder strains in the crosses used to generate the RIL. The coding of genotypes in 4- and 8-way RIL is rather complicated. It is a binary encoding of which founder strains’ genotypes match the RIL’s genotype at a marker, and not that this is specific to the order of the founders in the crosses used to generate the RIL. For example, if an RIL generated from 4 founders has the 1 allele at a SNP, and the four founders have SNP alleles 0, 1, 0, 1, then the RIL allele matches that of founders B and D. If the RIL was derived by the cross (AxB)x(CxD), then the RIL genotype would be encoded 22−1 + 23−1 = 6. If the cross was derived by the cross (DxA)x(CxB), then the RIL genotype would be encoded 21−1 + 24−1 = 9. These get reorganized after calls to calc.genoprob, sim.geno, or argmax.geno, and this approach simplifies the hidden Markov model (HMM) code. 232 sim.cross For the 4- and 8-way RIL, genotyping errors are simulated only if the founder genotypes are 0/1 SNPs. Author(s) Karl W Broman, References Copenhaver, G. P., Housworth, E. A. and Stahl, F. W. (2002) Crossover interference in arabidopsis. Genetics 160, 1631–1639. Foss, E., Lande, R., Stahl, F. W. and Steinberg, C. M. (1993) Chiasma interference as a function of genetic distance. Genetics 133, 681–691. Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. Genetics 139, 1045–1056. Broman, K. W. (2005) The genomes of recombinant inbred lines Genetics 169, 1133–1146. Teuscher, F. and Broman, K. W. (2007) Haplotype probabilities for multiple-strain recombinant inbred lines. Genetics 175, 1267–1274. See Also sim.map, read.cross, fake.f2, fake.bc fake.4way, simFounderSnps Examples # simulate a genetic map map <- sim.map() ### simulate 250 intercross individuals with 2 QTLs fake <- sim.cross(map, type="f2", n.ind=250, model = rbind(c(1,45,1,1),c(5,20,0.5,-0.5))) ### simulate 100 backcross individuals with 3 QTL # a 10-cM map model after the mouse data(map10) fakebc <- sim.cross(map10, type="bc", n.ind=100, model=rbind(c(1,45,1), c(5,20,1), c(5,50,1))) ### simulate 8-way RIL by sibling mating # get lengths from the above 10-cM map L <- ceiling(sapply(map10, max)) # simulate a 1 cM map themap <- sim.map(L, n.mar=L+1, eq.spacing=TRUE) # simulate founder genotypes pg <- simFounderSnps(themap, "8") # simulate the 8-way RIL by sib mating (256 lines) ril <- sim.cross(themap, n.ind=256, type="ri8sib", founderGeno=pg) sim.geno sim.geno 233 Simulate genotypes given observed marker data Description Uses the hidden Markov model technology to simulate from the joint distribution Pr(g | O) where g is the underlying genotype vector and O is the observed multipoint marker data, with possible allowance for genotyping errors. Usage sim.geno(cross, n.draws=16, step=0, off.end=0, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), stepwidth=c("fixed", "variable", "max")) Arguments cross An object of class cross. See read.cross for details. n.draws Number of simulation replicates to perform. step Maximum distance (in cM) between positions at which the simulated genotypes will be drawn, though for step=0, genotypes are drawn only at the marker locations. off.end Distance (in cM) past the terminal markers on each chromosome to which the genotype simulations will be carried. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. stepwidth Indicates whether the intermediate points should with fixed or variable step sizes. We recommend using "fixed"; "variable" is included for the qtlbim package (http://www.ssg.uab.edu/qtlbim). The "max" option inserts the minimal number of intermediate points so that the maximum distance between points is step. Details After performing the forward-backward equations, we draw from P r(g1 = v|O) and then P r(gk+1 = v|O, gk = u). In the case of the 4-way cross, with a sex-specific map, we assume a constant ratio of female:male recombination rates within the inter-marker intervals. Value The input cross object is returned with a component, draws, added to each component of cross$geno. This is an array of size [n.ind x n.pos x n.draws] where n.pos is the number of positions at which the simulations were performed and n.draws is the number of replicates. Attributes "error.prob", "step", and "off.end" are set to the values of the corresponding arguments, for later reference. 234 sim.map Author(s) Karl W Broman, See Also calc.genoprob, argmax.geno Examples data(fake.f2) fake.f2 <- sim.geno(fake.f2, step=2, n.draws=8) sim.map Simulate a genetic map Description Simulate the positions of markers on a genetic map. Usage sim.map(len=rep(100,20), n.mar=10, anchor.tel=TRUE, include.x=TRUE, sex.sp=FALSE, eq.spacing=FALSE) Arguments len A vector specifying the chromosome lengths (in cM) n.mar A vector specifying the number of markers per chromosome. anchor.tel If true, markers at the two telomeres will always be included, so if n.mar = 1 or 2, we’ll give just the two telomeric markers. include.x Indicates whether the last chromosome should be considered the X chromosome. sex.sp Indicates whether to create sex-specific maps, in which case the output will be a vector of 2-row matrices, with rows corresponding to the maps for the two sexes. eq.spacing If TRUE, markers will be equally spaced. Details Aside from the telomeric markers, marker positions are simulated as iid Uniform(0, L). If len or n.mar has just one element, it is expanded to the length of the other argument. If they both have just one element, only one chromosome is simulated. If eq.spacing is TRUE, markers are equally spaced between 0 and L. If anchor.tel is FALSE, telomeric markers are not included. Value A list of vectors, each specifying the locations of the markers. Each component of the list is given class A or X, according to whether it is autosomal or the X chromosome. simFounderSnps 235 Author(s) Karl W Broman, See Also sim.cross, plotMap, replace.map, pull.map Examples # simulate 4 autosomes, each with 10 markers map <- sim.map(c(100,90,80,40), 10, include.x=FALSE) plotMap(map) # equally spaced markers map2 <- sim.map(c(100,90,80,40), 10, include.x=FALSE, eq.spacing=TRUE) plot(map2) simFounderSnps Simulate founder SNPs for a multiple-strain RIL Description Simulate genotype data for the founding strains for a panel of multiple-strain RIL. Usage simFounderSnps(map, n.str=c("4","8"), pat.freq) Arguments map A list whose components are vectors containing the marker locations on each of the chromosomes. n.str Number of founding strains (4 or 8). pat.freq Frequency of SNP genotype patterns in the founder (a vector of length n.str/2 + 1): (monoallelic, SNP unique to one founder, SNP present in 2 founders, [and, for the case of 8 founders, SNP in 3/8 founders, SNP in 4/8 founders].) Details The SNPs are simulated to be in linkage equilibrium. Value A vector of the same length as there are chromosomes in map, with each component being a matrix of 0’s and 1’s, of dim n.str x n.mar. Author(s) Karl W Broman, 236 simPhyloQTL See Also sim.map, sim.cross Examples data(map10) x <- simFounderSnps(map10, "8", c(0, 0.5, 0.2, 0.2, 0.1)) simPhyloQTL Simulate a set of intercrosses for a single diallelic QTL Description Simulate a set of intercrosses with a single diallelic QTL. Usage simPhyloQTL(n.taxa=3, partition, crosses, map, n.ind=100, model, error.prob=0, missing.prob=0, partial.missing.prob=0, keep.qtlgeno=FALSE, keep.errorind=TRUE, m=0, p=0, map.function=c("haldane","kosambi","c-f","morgan")) Arguments n.taxa Number of taxa (i.e., strains). partition A vector of character strings of the form "AB|CD" or "A|BCD" indicating, for each QTL, which taxa have which allele. If missing, simulate under the null hypothesis of no QTL. crosses A vector of character strings indicating the crosses to do (for the form "AB", "AC", etc.). These will be sorted and then only unique ones used. If missing, all crosses will be simulated. map A list whose components are vectors containing the marker locations on each of the chromosomes. n.ind The number of individuals in each cross. If length 1, all crosses will have the same number of individuals; otherwise the length should be the same as crosses. model A matrix where each row corresponds to a different QTL, and gives the chromosome number, cM position and effects of the QTL (assumed to be the same in each cross in which the QTL is segregating). error.prob The genotyping error rate. missing.prob The rate of missing genotypes. partial.missing.prob When simulating an intercross or 4-way cross, this gives the rate at which markers will be incompletely informative (i.e., dominant or recessive). keep.qtlgeno If TRUE, genotypes for the simulated QTLs will be included in the output. keep.errorind If TRUE, and if error.prob > 0, the identity of genotyping errors will be included in the output. m Interference parameter; a non-negative integer. 0 corresponds to no interference. simPhyloQTL 237 p Probability that a chiasma comes from the no-interference mechanism map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. Details Meiosis is assumed to follow the Stahl model for crossover interference (see the references, below), of which the no interference model and the chi-square model are special cases. Chiasmata on the four-strand bundle are a superposition of chiasmata from two different mechanisms. With probability p, they arise by a mechanism exhibiting no interference; the remainder come from a chi-square model with inteference parameter m. Note that m=0 corresponds to no interference, and with p=0, one gets a pure chi-square model. QTLs are assumed to act additively, and the residual phenotypic variation is assumed to be normally distributed with variance 1. The effect of a QTL is a pair of numbers, (a, d), where a is the additive effect (half the difference between the homozygotes) and d is the dominance deviation (the difference between the heterozygote and the midpoint between the homozygotes). Value A list with each component being an object of class cross. See read.cross for details. The names (e.g. "AB", "AC", "BC") indicate the crosses. If keep.qtlgeno is TRUE, each cross object will contain a component qtlgeno which is a matrix containing the QTL genotypes (with complete data and no errors), coded as in the genotype data. If keep.errorind is TRUE and errors were simulated, each component of geno in each cross will each contain a matrix errors, with 1’s indicating simulated genotyping errors. Author(s) Karl W Broman, References Broman, K. W., Kim, S., An\’e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation. See Also scanPhyloQTL, inferredpartitions, summary.scanPhyloQTL, max.scanPhyloQTL, plot.scanPhyloQTL, sim.cross, read.cross Examples ## Not run: # example map; drop X chromosome data(map10) map10 <- map10[1:19] # simulate data x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"), map=map10, n.ind=150, model=c(1, 50, 0.5, 0)) 238 simulatemissingdata # run calc.genoprob on each cross x <- lapply(x, calc.genoprob, step=2) # scan genome, at each position trying all possible partitions out <- scanPhyloQTL(x, method="hk") # maximum peak max(out, format="lod") # approximate posterior probabilities at peak max(out, format="postprob") # all peaks above a threshold for LOD(best) - LOD(2nd best) summary(out, threshold=1, format="lod") # all peaks above a threshold for LOD(best), showing approx post'r prob summary(out, format="postprob", threshold=3) # plot of results plot(out) ## End(Not run) simulatemissingdata Simulates missing genotype data Description Simulate missing genotype data by removing some genotype data from the cross object Usage simulatemissingdata(cross, percentage = 5) Arguments cross An object of class cross. See read.cross for details. percentage How much of the genotype data do we need to randomly drop? Value An object of class cross with percentage Author(s) Danny Arends See Also • The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf • MQM - MQM description and references • mqmscan - Main MQM single trait analysis stepwiseqtl 239 • mqmscanall - Parallellized traits analysis • mqmaugment - Augmentation routine for estimating missing data • mqmautocofactors - Set cofactors using marker density • mqmsetcofactors - Set cofactors at fixed locations • mqmpermutation - Estimate significance levels • scanone - Single QTL scanning Examples data(multitrait) multitrait <- fill.geno(multitrait) multimissing5 <- simulatemissingdata(multitrait,perc=5) perc <- (sum(nmissing(multimissing5))/sum(ntyped(multimissing5))) stepwiseqtl Stepwise selection for multiple QTL Description Performs 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. Usage stepwiseqtl(cross, chr, pheno.col=1, qtl, formula, max.qtl=10, covar=NULL, method=c("imp", "hk"), model=c("normal", "binary"), incl.markers=TRUE, refine.locations=TRUE, additive.only=FALSE, scan.pairs=FALSE, penalties, keeplodprofile=TRUE, keeptrace=FALSE, verbose=TRUE, tol=1e-4, maxit=1000, require.fullrank=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider in search for QTL. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. pheno.col Column number in the phenotype matrix which should be used as the phenotype. One may also give character strings matching the phenotype names. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either nonintegers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations. qtl Optional QTL object (of class "qtl", as created by makeqtl) to use as a starting point. formula Optional formula to define the QTL model to be used as a starting point. max.qtl Maximum number of QTL to which forward selection should proceed. 240 stepwiseqtl covar Data frame of additive covariates. method Indicates whether to use multiple imputation or Haley-Knott regression. model The phenotype model: the usual model or a model for binary traits incl.markers If FALSE, do calculations only at points on an evenly spaced grid. refine.locations If TRUE, use refineqtl to refine the QTL locations after each step of forward and backward selection. additive.only If TRUE, allow only additive QTL models; if FALSE, consider also pairwise interactions among QTL. scan.pairs If TRUE, perform a two-dimensional, two-QTL scan at each step of forward selection. penalties Vector of three (or six) values indicating the penalty on the number of QTL terms. If three values, these are the penalties on main effects and heavy and light penalties on interactions. If six values, these include X-chr-specific penalties, and the values are: main effect for autosomes, main effect for X chr, heavy penalty on A:A interactions, light penalty on A:A interactions, penalty on A:X interactions, and penalty on X:X interactions. See the Details below. If missing, default values are used that are based on simulations of backcrosses and intercrosses with genomes modeled after that of the mouse. keeplodprofile If TRUE, keep the LOD profiles from the last iteration as attributes to the output. keeptrace If TRUE, keep information on the sequence of models visited through the course of forward and backward selection as an attribute to the output. verbose If TRUE, give feedback about progress. If verbose is an integer > 1, even more information is printed. tol Tolerance for convergence for the binary trait model. maxit Maximum number of iterations for fitting the binary trait model. require.fullrank If TRUE, give LOD=0 when covariate matrix in the linear regression is not of full rank. Details We seek to identify the model with maximal penalized LOD score. The penalized LOD score, defined in Manichaikul et al. (2009), is the LOD score for the model (the log10 likelihood ratio comparing the model to the null model with no QTL) with penalties on the number of QTL and QTL:QTL interactions. We consider QTL models allowing pairwise interactions among QTL but with an enforced hierarchy in which inclusion of a pairwise interaction requires the inclusion of both of the corresponding main effects. Additive covariates may be included, but currently we do not explore QTL:covariate interactions. Also, the penalized LOD score criterion is currently defined only for autosomal loci, and results with the X chromosome should be considered with caution. The penalized LOD score is of the form pLOD(γ) = LOD(γ) − Tm pm − Th ph − Tl pl where γ denotes a model, pm is the number of QTL in the model ("main effects"), ph is the number of pairwise interactions that will be given a heavy interaction penalty, pl is the number of pairwise interactions that will be given a light interaction penalty, Tm is the penalty on main effects, Th is the heavy interaction penalty, and Tl is the light interaction penalty. The penalties argument is the vector (Tm , Th , Tl ). If Tl is missing (penalties has a vector of length 2), we assume Tl = Th , and so all pairwise interactions are assigned the same penalty. stepwiseqtl 241 The "heavy" and "light" interaction penalties can be a bit confusing. Consider the clusters of QTL that are connected via one or more pairwise interactions. To each such cluster, we assign at most one "light" interaction penalty, and give all other pairwise interactions the heavy interaction penalty. In other words, if pi is the total number of pairwise interactions for a QTL model, we let pl be the number of clusters of connected QTL with at least one pairwise interaction, and then let ph −pi −pl . Let us give an explicit example. Consider a model with 6 QTL, and with interactions between QTL 2 and 3, QTL 4 and 5 and QTL 4 and 6 (so we have the model formula y ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6 + Q2:Q3 + Q4:Q5 There are three clusters of connected QTL: (1), (2,3) and (4,5,6). We would assign 6 main effect penalties (Tm ), 2 light interaction penalties (Tl ), and 1 heavy interaction penalty (Th ). Manichaikul et al. (2009) described a system for deriving the three penalties on the basis of permutation results from a two-dimensional, two-QTL genome scan (as calculated with scantwo). These may be calculated with the function calc.penalties. A forward/backward search method is used, with the aim to optimize the penalized LOD score criterion. That is, we seek to identify the model with maximal the penalized LOD score. The search algorithm was based closely on an algorithm described by Zeng et al. (1999). We use forward selection to a model of moderate size (say 10 QTL), followed by backward elimination all the way to the null model. The chosen model is that which optimizes the penalized LOD score criterion, among all models visited. The detailed algorithm is as follows. Note that if additive.only=TRUE, no pairwise interactions are considered. 1. Start at the null model, and perform a single-QTL genome scan, and choose the position giving the largest LOD score. If scan.pairs=TRUE, start with a two-dimensional, two-QTL genome scan instead. If an initial QTL model were defined through the arguments qtl and formula, start with this model and jump immediately to step 2. 2. With a fixed QTL model in hand: (a) Scan for an additional additive QTL. (b) For each QTL in the current model, scan for an additional interacting QTL. (c) If there are ≥ 2 QTL in the current model, consider adding one of the possible pairwise interactions. (d) If scan.pairs=TRUE perform a two-dimensional, two-QTL scan, seeking to add a pair of novel QTL, either additive or interacting. (e) Step to the model that gives the largest value for the model comparison criterion, among those considered at the current step. 3. Refine the locations of the QTL in the current model (if refine.locations=TRUE). 4. Repeat steps 2 and 3 up to a model with some pre-determined number of loci. 5. Perform backward elimination, all the way back to the null model. At each step, consider dropping one of the current main effects or interactions; move to the model that maximizes the model comparison criterion, among those considered at this step. Follow this with a refinement of the locations of the QTL. 6. Finally, choose the model having the largest model comparison criterion, among all models visited. In this forward/backward algorithm, it is likely best to build up to an overly large model and then prune it back. Note that there is no "stopping rule"; the chosen model is that which optimizes the model comparison criterion, among all models visited. The search can be time consuming, particularly if a two-dimensional scan is performed at each forward step. Such two-dimensional scans may be useful for identifying QTL linked in repulsion (having effects of opposite sign) or interacting QTL with limited marginal effects, but our limited experience suggests that they are not necessary; important linked or interacting QTL pairs can be picked up in the forward selection to a large model, and will be retained in the backward elimination phase. 242 stepwiseqtl Value The output is a representation of the best model, as measured by the penalized LOD score (see Details), among all models visited. This is QTL object (of class "qtl", as produced by makeqtl), with attributes "formula", indicating the model formula, and "pLOD" indicating the penalized LOD score. If keeplodprofile=TRUE, LOD profiles from the last pass through the refinement algorithm are retained as an attribute, "lodprofile", to the object. These may be plotted with plotLodProfile. If keeptrace=TRUE, the output will contain an attribute "trace" containing information on the best model at each step of forward and backward elimination. This is a list of objects of class "compactqtl", which is similar to a QTL object (as produced by makeqtl) but containing just a vector of chromosome IDs and positions for the QTL. Each will also have attributes "formula" (containing the model formula) and "pLOD" (containing the penalized LOD score. Methods imp: multiple imputation is used, as described by Sen and Churchill (2001). hk: Haley-Knott regression is used (regression of the phenotypes on the multipoint QTL genotype probabilities), as described by Haley and Knott (1992). Author(s) Karl W Broman, References Manichaikul, A., Moon, J. Y., Sen, Ś, Yandell, B. S. and Broman, K. W. (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. Genetics, 181, 1077–1086. Broman, K. W. and Speed, T. P. (2002) A model selection approach for the identification of quantitative trait loci in experimental crosses (with discussion). J Roy Stat Soc B 64, 641–656, 731–775. Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324. Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. Genetics 159, 371–387. Zeng, Z.-B., Kao, C.-H. and Basten, C. J. (1999) Estimating the genetic architecture of quantitative traits. Genetical Research, 74, 279–289. See Also calc.penalties, plotModel, makeqtl, fitqtl, refineqtl, addqtl, addpair Examples data(fake.bc) ## Not run: fake.bc <- calc.genoprob(fake.bc, step=2.5) outsw <- stepwiseqtl(fake.bc, max.qtl=3, method="hk", keeptrace=TRUE) # best model outsw strip.partials 243 plotModel(outsw) # path through model space thetrace <- attr(outsw, "trace") # plot of these par(mfrow=c(3,3)) for(i in seq(along=thetrace)) plotModel(thetrace[[i]], main=paste("pLOD =",round(attr(thetrace[[i]],"pLOD"), 2))) strip.partials Strip partially informative genotypes Description Replace all partially informative genotypes (e.g., dominant markers in an intercross) with missing values. Usage strip.partials(cross, verbose=TRUE) Arguments cross An object of class cross. See read.cross for details. verbose If TRUE, print the number of genotypes removed. Value The same class cross object as in the input, but with partially informative genotypes made missing. Author(s) Karl W Broman, See Also plotMissing, plotInfo Examples data(listeria) sum(nmissing(listeria)) listeria <- strip.partials(listeria) sum(nmissing(listeria)) 244 subset.cross subset.cross Subsetting data for QTL experiment Description Pull out a specified set of chromosomes and/or individuals from a cross object. Usage ## S3 method for class 'cross' subset(x, chr, ind, ...) ## S3 method for class 'cross' x[chr, ind] Arguments x chr ind ... An object of class cross. See read.cross for details. Optional vector specifying which chromosomes to keep or discard. This may be a logical, numeric, or character string vector. See Details, below. Optional vector specifying which individuals to keep discard. This may be a logical, numeric or chacter string vector. See Details, below. Ignored at this point. Details The chr argument may be a logical vector with length equal to the number of chromosomes in the input cross x. Alternatively, it should be a vector of character strings referring to chromosomes by name. Numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. If the ind argument is a logical vector (TRUE/FALSE), it should have length equal to the number of individuals in the input cross x. The individuals with corresponding TRUE values are retained. If the ind argument is numeric, it should have values either between 1 and the number of individuals in the input cross x (in which case these individuals will be retained), or it should have values between -1 and -n, where n is the number of individuals in the input cross x, in which case all except these individuals will be retained. If the input cross object x contains individual identifiers (a phenotype column labeled "id" or "ID"), and if the ind argument contains character strings, then these will be matched against the individual identifiers. If all values in ind are preceded by a -), we omit those individuals whose IDs match those in ind. Otherwise, we retain those individuals whose IDs match those in ind. Value The input cross object, but with only the specified subset of the data. Author(s) Karl W Broman, See Also pull.map, drop.markers, subset.map subset.map 245 Examples data(fake.f2) fake.f2.A <- subset(fake.f2, chr=c("5","13")) fake.f2.B <- subset(fake.f2, ind = -c(1,5,10)) fake.f2.C <- subset(fake.f2, chr=1:5, ind=1:50) data(listeria) y <- pull.pheno(listeria, 1) listeriaB <- subset(listeria, ind = (!is.na(y) & y < 264)) # individual identifiers listeria$pheno$ID <- paste("mouse", 1:nind(listeria), sep="") listeriaC <- subset(listeria, ind=c("mouse1","mouse11","mouse21")) listeriaD <- subset(listeria, ind=c("-mouse1","-mouse11","-mouse21")) # you can also use brackets (like temp <- listeria[c("5","13"),] # temp <- listeria[ , 1:10] # temp <- listeria[5, 1:10] # subset.map matrix with rows=chromosomes and columns=individuals) chr 5 and 13 first ten individuals chr 5 for first ten individuals Subsetting chromosomes for a genetic map Description Pull out a specified set of chromosomes from a map object. Usage ## S3 method for class 'map' subset(x, ...) ## S3 method for class 'map' x[...] Arguments x A list whose components are vectors of marker locations. ... Vector of chromosome indices. Value The input map object, but with only the specified subset of chromosomes. Author(s) Karl W Broman, See Also subset.cross 246 subset.scanone Examples data(map10) map10 <- subset(map10, chr=1:5) # you can also use brackets map10 <- map10[2:3] subset.scanone Subsetting the results of a genome scan Description Pull out a specified set of chromosomes and/or LOD columns from scanone output. Usage ## S3 method for class 'scanone' subset(x, chr, lodcolumn, ...) Arguments x An object of class scanone, output from scanone. chr Optional vector specifying which chromosomes to keep. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. lodcolumn A vector specifying which LOD columns to keep (or, if negative), omit. These should be between 1 and the number of LOD columns in the input x. ... Ignored at this point. Value The input scanone object, but with only the specified subset of the data. Author(s) Karl W Broman, See Also summary.scanone, scanone Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=2.5) out <- scanone(fake.bc, method="hk", pheno.col=1:2) summary(subset(out, chr=18:19), format="allpeaks") subset.scanoneperm 247 subset.scanoneperm Subsetting permutation test results Description Pull out results for a specified set LOD columns from permutation results from scanone. Usage ## S3 method for class 'scanoneperm' subset(x, repl, lodcolumn, ...) ## S3 method for class 'scanoneperm' x[repl, lodcolumn] Arguments x Permutation results from scanone, run with n.perm>0. repl A vector specifying which permutation replicates to keep or (if negative) omit. lodcolumn A vector specifying which LOD columns to keep or (if negative) omit. These should be between 1 and the number of LOD columns in the input x. ... Ignored at this point. Value The input scanone permutation results, but with only the specified subset of the data. Author(s) Karl W Broman, See Also summary.scanoneperm, scanone, c.scanoneperm, cbind.scanoneperm, rbind.scanoneperm Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=5) operm <- scanone(fake.bc, method="hk", pheno.col=1:2, n.perm=25) operm2 <- subset(operm, lodcolumn=2) # alternatively operm2alt <- operm[,2] 248 subset.scantwo subset.scantwo Subsetting the results of a 2-d genome scan Description Pull out a specified set of chromosomes and/or LOD columns from scantwo output. Usage ## S3 method for class 'scantwo' subset(x, chr, lodcolumn, ...) Arguments x An object of class scantwo, output from scantwo. chr Optional vector specifying which chromosomes to keep. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. lodcolumn A vector specifying which LOD columns to keep (or, if negative), omit. These should be between 1 and the number of LOD columns in the input x. ... Ignored at this point. Value The input scantwo object, but with only the specified subset of the data. Author(s) Karl W Broman, See Also summary.scantwo, scantwo Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc) out <- scantwo(fake.bc, method="hk", pheno.col=1:2) summary(subset(out, chr=18:19)) subset.scantwoperm 249 subset.scantwoperm Subsetting two-dimensional permutation test results Description Pull out results for a specified set LOD columns from permutation results from scantwo. Usage ## S3 method for class 'scantwoperm' subset(x, repl, lodcolumn, ...) ## S3 method for class 'scantwoperm' x[repl, lodcolumn] Arguments x Permutation results from scantwo, run with n.perm>0. repl A vector specifying which permutation replicates to keep or (if negative) omit. Ignored in case of X-chr specific permutations lodcolumn A vector specifying which LOD columns to keep or (if negative) omit. These should be between 1 and the number of LOD columns in the input x. ... Ignored at this point. Value The input scantwo permutation results, but with only the specified subset of the data. Author(s) Karl W Broman, See Also summary.scantwoperm, scantwo, c.scantwoperm, rbind.scantwoperm Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=0) operm <- scantwo(fake.bc, method="hk", pheno.col=1:2, n.perm=5) operm2 <- subset(operm, lodcolumn=2) # alternatively operm2alt <- operm[,2] 250 summary.fitqtl summary.cross Print summary of QTL experiment Description Print summary information about a cross object. Usage ## S3 method for class 'cross' summary(object, ...) Arguments object An object of class cross. See read.cross for details. ... Ignored at this point. Value An object of class summary.cross containing a variety of summary information about the cross (this is generally printed automatically). Author(s) Karl W Broman, See Also read.cross, plot.cross, nind, nmar, nchr, totmar, nphe Examples data(fake.f2) summary(fake.f2) summary.fitqtl Summary of fit of qtl model Description Print summary information about the results of fitqtl. Usage ## S3 method for class 'fitqtl' summary(object, pvalues=TRUE, simple=FALSE, ...) summary.qtl 251 Arguments object pvalues simple ... Output from fitqtl. If FALSE, don’t include p-values in the summary. If TRUE, don’t include p-values or sums of squares in the summary. Ignored at this point. Value An object of class summary.fitqtl, which is not all that different than the input, but when printed gives summary information about the results. Author(s) Hao Wu; Karl W Broman, See Also fitqtl, makeqtl, scanqtl Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 8, 13) qp <- c(26, 56, 28) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") # fit model with 3 interacting QTLs interacting # (performing a drop-one-term analysis) lod <- fitqtl(fake.f2, pheno.col=1, qtl, formula=y~Q1*Q2*Q3, method="hk") summary(lod) summary.qtl Print summary of a QTL object Description Print summary information about a qtl object. Usage ## S3 method for class 'qtl' summary(object, ...) Arguments object ... An object of class qtl, created by makeqtl. Ignored at this point. 252 summary.ripple Value An object of class summary.qtl, which is just a data.frame containing the chromosomes, positions, and number of possible genotypes for each QTL. Author(s) Karl W Broman, See Also makeqtl Examples data(fake.f2) # take out several QTLs and make QTL object qc <- c(1, 6, 13) qp <- c(25.8, 33.6, 18.63) fake.f2 <- subset(fake.f2, chr=qc) fake.f2 <- calc.genoprob(fake.f2, step=2, err=0.001) qtl <- makeqtl(fake.f2, qc, qp, what="prob") summary(qtl) summary.ripple Print summary of ripple results Description Print marker orders, from the output of the function ripple, for which the log10 likelihood relative to the initial order is above a specified cutoff. Usage ## S3 method for class 'ripple' summary(object, lod.cutoff = -1, ...) Arguments object An object of class ripple, the output of the function ripple. lod.cutoff Only marker orders with LOD score (relative to the initial order) above this cutoff will be displayed. For output of ripple in the case of minimization of the number of obligate crossovers, we double this argument and treat it as a cutoff for the number of obligate crossovers. ... Ignored at this point. summary.scanone 253 Value An object of class summary.ripple, whose rows correspond to marker orders with likelihood (or number of obligate crossovers) within some cutoff of the initial order. If no marker order, other than the initial one, has likelihood within the specified range, the initial and next-best orders are returned. Author(s) Karl W Broman, See Also ripple, est.map, est.rf Examples ## Not run: data(badorder) rip1 <- ripple(badorder, 1, 7) summary(rip1) rip2 <- ripple(badorder, 1, 2, method="likelihood") summary(rip2) badorder <- switch.order(badorder, 1, rip2[2,]) ## End(Not run) summary.scanone Summarize the results of a genome scans Description Print the rows of the output from scanone that correspond to the maximum LOD for each chromosome, provided that they exceed some specified thresholds. Usage ## S3 method for class 'scanone' summary(object, threshold, format=c("onepheno", "allpheno", "allpeaks", "tabByCol", "tabByChr"), perms, alpha, lodcolumn=1, pvalues=FALSE, ci.function=c("lodint", "bayesint"), ...) Arguments object An object output by the function scanone. threshold LOD score thresholds. Only peaks with LOD score above this value will be returned. This could be a single number or (for formats other than "onepheno") a threshold for each LOD score column. If alpha is specified, threshold should not be. format Format for the output. See Details, below. 254 summary.scanone perms Optional permutation results used to derive thresholds or to calculate genomescan-adjusted p-values. This must be consistent with the object input, in that it must have the same number of LOD score columns, though it can have just one column of permutation results, in which case they are reused for all LOD score columns in the scanone output, object. (These can also be permutation results from scantwo, which permutations for a one-dimensional scan.) alpha If perms are included, this is the significance level used to calculate thresholds for determining which peaks to pull out. If threshold is specified, alpha should not be. lodcolumn If format="onepheno", this indicates the LOD score column to focus on. This should be a single number between 1 and the number of LOD columns in the object input. pvalues If TRUE, include columns with genome-scan-adjusted p-values in the results. This requires that perms be provided. ci.function For formats "tabByCol" and "tabByChr", indicates the function to use to get approximate confidence intervals for QTL location. ... For formats "tabByCol" and "tabByChr", additional arguments are passed to the function indicated by ci.function (for example, drop for lodint or prob for bayesint, or expandtomarkers for either). Details This function is used to report loci deemed interesting from a one-QTL genome scan (by scanone). For format="onepheno", we focus on a single LOD score column, indicated by lodcolumn. The single largest LOD score peak on each chromosome is extracted. If threshold is specified, only those peaks with LOD meeting the threshold will be returned. If perms and alpha are specified, a threshold is calculated based on the permutation results in perms for the significance level alpha. If neither threshold nor alpha are specified, the peak on each chromosome is returned. Again note that with this format, only the LOD score column indicated by lodcolumn is considered in deciding which chromosomes to return, but the LOD scores from other columns, at the position with maximum LOD score in the lodcolumn column, are also returned. For format="allpheno", we consider all LOD score columns, and pull out the position, on each chromosome, showing the largest LOD score. The output thus may contain multiple rows for a chromosome. Here threshold may be a vector of LOD score thresholds, one for each LOD score column, in which case only those positions for which a LOD score column exceeded its threshold are given. If threshold is a single number, it is applied to all of the LOD score columns. If alpha is specified, it must be a single significance level, applied for all LOD score columns, and again perms must be specified, and these are used to calculate the LOD score threshold for the significance level alpha. For format="allpeaks", the output will contain, for each chromosome, the maximum LOD score for each LOD score column, at the position at which it achieved its maximum. Thus, the output will contain no more than one row per chromosome, but will contain the position and maximum LOD score for each of the LOD score columns. The arguments threshold and alpha may be specified as for the "allpheno" format. The results for a chromosome are returned if at least one of the LOD score columns exceeded its threshold. For format="tabByCol", there will be a separate table for each LOD score column, with a single peak per chromosome. Included are columns indicating chromosome, peak position, lower and upper limits of the confidence interval calculated via lodint or bayesint, and lod score. The output for format="tabByChr", is similar to that of format="tabByCol", but with results organized by chromosome rather than by LOD score column. summary.scanone 255 If pvalues=TRUE, and perms is specified, genome-scan-adjusted p-values are calculated for each LOD score column, and there are additional columns in the output containing these p-values. In the case that X-chromosome specific permutations were performed (with perm.Xsp=TRUE in scanone), autosome- and X-chromosome specific thresholds and p-values are calculated by the method in Broman et al. (2006). Value An object of class summary.scanone, to be printed by print.summary.scanone. Author(s) Karl W Broman, References Broman, K. W., Sen, Ś, Owens, S. E., Manichaikul, A., Southard-Smith, E. M. and Churchill G. A. (2006) The X chromosome in quantitative trait locus mapping. Genetics, 174, 2151–2158. See Also scanone, plot.scanone, max.scanone, subset.scanone, c.scanone, summary.scanoneperm c.scanoneperm Examples data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=5) # genome scan by Haley-Knott regression out <- scanone(fake.bc, method="hk") # permutation tests ## Not run: operm <- scanone(fake.bc, method="hk", n.perm=1000) # peaks for all chromosomes summary(out) # results with LOD >= 3 summary(out, threshold=3) # the same, but also showing the p-values summary(out, threshold=3, perms=operm, pvalues=TRUE) # results with LOD meeting the 0.05 threshold from the permutation results summary(out, perms=operm, alpha=0.05) # the same, also showing the p-values summary(out, perms=operm, alpha=0.05, pvalues=TRUE) ##### summary with multiple phenotype results out2 <- scanone(fake.bc, pheno.col=1:2, method="hk") 256 summary.scanoneboot # permutations ## Not run: operm2 <- scanone(fake.bc, pheno.col=1:2, method="hk", n.perm=1000) # results with LOD >= 2 for the 1st phenotype and >= 1 for the 2nd phenotype # using format="allpheno" summary(out2, thr=c(2, 1), format="allpheno") # The same with format="allpeaks" summary(out2, thr=c(2, 1), format="allpeaks") # The same with p-values summary(out2, thr=c(2, 1), format="allpeaks", perms=operm2, pvalues=TRUE) # results with LOD meeting the 0.05 significance level by the permutations # using format="allpheno" summary(out2, format="allpheno", perms=operm2, alpha=0.05) # The same with p-values summary(out2, format="allpheno", perms=operm2, alpha=0.05, pvalues=TRUE) # The same with format="allpeaks" summary(out2, format="allpeaks", perms=operm2, alpha=0.05, pvalues=TRUE) # format="tabByCol" summary(out2, format="tabByCol", perms=operm2, alpha=0.05, pvalues=TRUE) # format="tabByChr", but using bayes intervals summary(out2, format="tabByChr", perms=operm2, alpha=0.05, pvalues=TRUE, ci.function="bayesint") # format="tabByChr", but using 99% bayes intervals summary(out2, format="tabByChr", perms=operm2, alpha=0.05, pvalues=TRUE, ci.function="bayesint", prob=0.99) summary.scanoneboot Bootstrap confidence interval for QTL location Description Calculates a bootstrap confidence interval for QTL location, using the bootstrap results from scanoneboot. Usage ## S3 method for class 'scanoneboot' summary(object, prob=0.95, expandtomarkers=FALSE, ...) Arguments object Output from scanoneboot. prob Desired coverage. expandtomarkers If TRUE, the interval is expanded to the nearest flanking markers. ... Ignored at this point. summary.scanoneperm 257 Value An object of class scanone, indicating the position with the maximum LOD, and indicating endpoints for the estimated bootstrap confidence interval. Author(s) Karl W Broman, See Also scanoneboot, plot.scanoneboot, lodint, bayesint Examples ## Not run: data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=1, err=0.001) bootoutput <- scanoneboot(fake.f2, chr=13, method="hk") summary(bootoutput) ## End(Not run) summary.scanoneperm LOD thresholds from scanone permutation results Description Print the estimated genome-wide LOD thresholds on the basis of permutation results from scanone (with n.perm > 0). Usage ## S3 method for class 'scanoneperm' summary(object, alpha=c(0.05, 0.10), controlAcrossCol=FALSE, ...) Arguments object Output from the function scanone with n.perm > 0. alpha Genome-wide significance levels. controlAcrossCol If TRUE, control error rate not just across the genome but also across the columns of LOD scores. ... Ignored at this point. 258 summary.scanoneperm Details If there were autosomal data only or scanone was run with perm.Xsp=FALSE, genome-wide LOD thresholds are given; these are the 1-α quantiles of the genome-wide maximum LOD scores from the permutations. If there were autosomal and X chromosome data and scanone was run with perm.Xsp=TRUE, autosome- and X-chromsome-specific LOD thresholds are given, by the method described in Broman et al. (2006). Let LA and LX be total the genetic lengths of the autosomes and X chromosome, respectively, and let LT = LA + LX Then in place of α, we use αA = 1 − (1 − α)LA /LT as the significance level for the autosomes and αX = 1 − (1 − α)LX /LT as the significance level for the X chromosome. The result is a list with two matrices, one for the autosomes and one for the X chromosome. If controlAcrossCol=TRUE, we use a trick to control the error rate not just across the genome but also across the LOD score columns. Namely, we convert each column of permutation results to ranks, and then for each permutation replicate we find the maximum rank across the columns. We then find the appropriate quantile of the maximized ranks, and then backtrack to the corresponding LOD score within each of the columns. See Burrage et al. (2010), right column on page 118. Value An object of class summary.scanoneperm, to be printed by print.summary.scanoneperm. If there were X chromosome data and scanone was run with perm.Xsp=TRUE, there are two matrices in the results, for the autosome and X-chromosome LOD thresholds. Author(s) Karl W Broman, References Broman KW, Sen Ś, Owens SE, Manichaikul A, Southard-Smith EM, Churchill GA (2006) The X chromosome in quantitative trait locus mapping. Genetics, 174, 2151–2158. Burrage LC, Baskin-Hill AE, Sinasac DS, Singer JB, Croniger CM, Kirby A, Kulbokas EJ, Daly MJ, Lander ES, Broman KW, Nadeau JH (2010) Genetic resistance to diet-induced obesity in chromosome substitution strains of mice. Mamm Genome, 21, 115–129. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. See Also scanone, summary.scanone, plot.scanoneperm Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=2.5) summary.scanPhyloQTL 259 operm1 <- scanone(fake.f2, n.perm=100, method="hk") summary(operm1) operm2 <- scanone(fake.f2, n.perm=100, method="hk", perm.Xsp=TRUE) summary(operm2) # Add noise column fake.f2$pheno$noise <- rnorm(nind(fake.f2)) operm3 <- scanone(fake.f2, pheno.col=c("phenotype", "noise"), n.perm=10, method="hk") summary(operm3) summary(operm3, controlAcrossCol=TRUE, alpha=c(0.05, 0.36)) summary.scanPhyloQTL Summarize the results a genome scan to map a QTL to a phylogenetic tree Description Print the maximum LOD scores for each partition on each chromosome, from the results of scanPhyloQTL. Usage ## S3 method for class 'scanPhyloQTL' summary(object, format=c("postprob", "lod"), threshold, ...) Arguments object An object output by the function scanPhyloQTL. format Indicates whether to provide LOD scores or approximate posterior probabilities; see Details below. threshold A threshold determining which chromosomes should be output; see Details below. ... Ignored at this point. Details This function is used to report chromosomes deemed interesting from a one-QTL genome scan to map QTL to a phylogenetic tree (by scanPhyloQTL). For format="lod", the output contains the maximum LOD score for each partition on each chromosome (which do not necessarily occur at the same position). The position corresponds to the peak location for the partition with the largest LOD score on that chromosome. The last column is the overall maximum LOD (across partitions) on that chromosome. The second-to-last column is the inferred partition (i.e., that with the largest LOD score. The third-to-last column is the difference between the LOD score for the best partition and that for the second-best. For format="postprob", the final column contains the maximum LOD score across partitions. But instead of providing the LOD scores for each partition, these are converted to approximate posterior probabilities under the assumption of a single diallelic QTL on that chromosome: on each chromosome, we take 10LOD for the partitions and rescale them to sum to 1. The threshold argument is applied to the last column (the maximum LOD score across partitions). 260 summary.scanPhyloQTL Value An object of class summary.scanPhyloQTL, to be printed by print.summary.scanPhyloQTL. Author(s) Karl W Broman, References Broman, K. W., Kim, S., An\’e, C. and Payseur, B. A. Mapping quantitative trait loci to a phylogenetic tree. In preparation. See Also scanPhyloQTL, plot.scanPhyloQTL, max.scanPhyloQTL, summary.scanone, inferredpartitions, simPhyloQTL Examples ## Not run: # example map; drop X chromosome data(map10) map10 <- map10[1:19] # simulate data x <- simPhyloQTL(4, partition="AB|CD", crosses=c("AB", "AC", "AD"), map=map10, n.ind=150, model=c(1, 50, 0.5, 0)) # run calc.genoprob on each cross x <- lapply(x, calc.genoprob, step=2) # scan genome, at each position trying all possible partitions out <- scanPhyloQTL(x, method="hk") # maximum peak max(out, format="lod") # approximate posterior probabilities at peak max(out, format="postprob") # all peaks above a threshold for LOD(best) - LOD(2nd best) summary(out, threshold=1, format="lod") # all peaks above a threshold for LOD(best), showing approx post'r prob summary(out, format="postprob", threshold=3) # plot of results plot(out) ## End(Not run) summary.scantwo summary.scantwo 261 Summarize the results of a two-dimensional genome scan Description Summarize the interesting aspects of the results of scantwo. Usage ## S3 method for class 'scantwo' summary(object, thresholds, what=c("best", "full", "add", "int"), perms, alphas, lodcolumn=1, pvalues=FALSE, allpairs=TRUE, ...) Arguments object An object of class scantwo, the output of the function scantwo. thresholds A vector of length 5, giving LOD thresholds for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores. See Details, below. what Indicates for which LOD score the maximum should be reported. See Details, below. perms Optional permutation results used to derive thresholds or to calculate genomescan-adjusted p-values. This must be consistent with the object input, in that it must have the same number of LOD score columns, though it can have just one column of permutation results, in which case they are assumed to apply to any chosen LOD score column. alphas If perms are included, these are the significance levels used to calculate thresholds for determining which peaks to pull out. It should be a vector of length 5, giving significance levels for the full, conditional-interactive, interaction, additive, and conditional-additive LOD scores. (It can also be a single number, in which case it is assumed that the same value is used for all five LOD scores.) If thresholds is specified, alphas should not be. lodcolumn If the scantwo results contain LOD scores for multiple phenotypes, this argument indicates which to use in the summary. Only one LOD score column may be considered at a time. pvalues If TRUE, include columns with genome-scan-adjusted p-values in the results. This requires that perms be provided. allpairs If TRUE, all pairs of chromosomes are considered. If FALSE, only self-self pairs are considered, so that one may more conveniently check for possible linked QTL. ... Ignored at this point. Details If what="best", we calculate, for each pair of chromosomes, the maximum LOD score for the full model (two QTL plus interaction) and the maximum LOD score for the additive model. The difference between these is a LOD score for a test for interaction. We also calculate the difference 262 summary.scantwo between the maximum full LOD and the maximum single-QTL LOD score for the two chromosomes; this is the LOD score for a test for a second QTL, allowing for epistasis, which we call either the conditional-interactive or "fv1" LOD score. Finally, we calculate the difference between the maximum additive LOD score and the maximum single-QTL LOD score for the two chromosomes; this is the LOD score for a test for a second QTL, assuming that the two QTL act additively, which we call either the conditional-additive or "av1" LOD score. Note that the maximum full LOD and additive LOD are allowed to occur in different places. If what="full", we find the maximum full LOD and extract the additive LOD at the corresponding pair of positions; we derive the other three LOD scores for that fixed pair of positions. If what="add", we find the maximum additive LOD and extract the full LOD at the corresponding pair of positions; we derive the other three LOD scores for that fixed pair of positions. If what="int", we find the pair of positions for which the difference between the full and additive LOD scores is largest, and then calculate the five LOD scores at that pair of positions. If thresholds or alphas is provided (and note that when alphas is provided, perms must also), we extract just those pairs of chromosomes for which either (a) the full LOD score exceeds its thresholds and either the conditional-interactive LOD or the interaction LOD exceed their threshold, or (b) the additive LOD score exceeds its threshold and the conditional-additive LOD exceeds its threshold. The thresholds or alphas must be given in the order full, cond-int, int, add, cond-add. Thresholds may be obtained by a permutation test with scantwo, but these are extremely timeconsuming. 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, conditionalinteractive, 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. Value An object of class summary.scantwo, to be printed by print.summary.scantwo; Output of addpair Note that, for output from addpair in which the new loci are indicated explicitly in the formula, the summary provided by summary.scantwo is somewhat special. All arguments except allpairs and thresholds (and, of course, the input object) are ignored. If the formula is symmetric in the two new QTL, the output has just two LOD score columns: lod.2v0 comparing the full model to the model with neither of the new QTL, and lod.2v1 comparing the full model to the model with just one new QTL. If the formula is not symmetric in the two new QTL, the output has three LOD score columns: lod.2v0 comparing the full model to the model with neither of the new QTL, lod.2v1b comparing the full model to the model in which the first of the new QTL is omitted, and lod.2v1a comparing the full model to the model with the second of the new QTL omitted. The thresholds argument should have length 1 or 2, rather than the usual 5. Rows will be retained if lod.2v0 is greater than thresholds[1] and lod.2v1 (or either of lod.2v1a or lod.2v1b) is greater than thresholds[2]. (If a single thresholds is given, we assume that thresholds[2]==0.) The older version The previous version of this function is still available, though it is now named summaryScantwoOld. summary.scantwo 263 We much prefer the revised function. However, while we are confident that this function (and the permutations in scantwo) are calculating the relevant statistics, the appropriate significance levels for these relatively complex series of statistical tests is not yet completely clear. Author(s) Karl W Broman, See Also scantwo, plot.scantwo, max.scantwo, condense.scantwo Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=5) out.2dim <- scantwo(fake.f2, method="hk") # All pairs of chromosomes summary(out.2dim) # Chromosome pairs meeting specified criteria summary(out.2dim, thresholds=c(9.1, 7.1, 6.3, 6.3, 3.3)) # Similar, but ignoring the interaction LOD score in the rule summary(out.2dim, thresholds=c(9.1, 7.1, Inf, 6.3, 3.3)) # Pairs having largest interaction LOD score, if it's > 4 summary(out.2dim, thresholds=c(0, Inf, 4, Inf, Inf), what="int") # permutation test to get thresholds; run in two batches # and then combined with c.scantwoperm ## Not run: operm.2dimA <- scantwo(fake.f2, method="hk", n.perm=500) operm.2dimB <- scantwo(fake.f2, method="hk", n.perm=500) operm.2dim <- c(operm.2dimA, operm.2dimB) ## End(Not run) # estimated LOD thresholds summary(operm.2dim) # Summary, citing significance levels and so estimating thresholds # from the permutation results summary(out.2dim, perms=operm.2dim, alpha=rep(0.05, 5)) # Similar, but ignoring the interaction LOD score in the rule summary(out.2dim, perms=operm.2dim, alpha=c(0.05, 0.05, 0, 0.05, 0.05)) # Similar, but also getting genome-scan-adjusted p-values summary(out.2dim, perms=operm.2dim, alpha=c(0.05, 0.05, 0, 0.05, 0.05), pvalues=TRUE) 264 summary.scantwoperm summary.scantwoperm LOD thresholds from scantwo permutation results Description Print the estimated genome-wide LOD thresholds on the basis of permutation results from scantwo (with n.perm > 0). Usage ## S3 method for class 'scantwoperm' summary(object, alpha=c(0.05, 0.10), ...) Arguments object Output from the function scantwo with n.perm > 0. alpha Genome-wide significance levels. ... Ignored at this point. Details We take the 1 − α quantiles of the individual LOD scores. In the case of X-chr-specific permutations, we use the combined length of the autosomes, LA , and the length of the X chromosome, LX , and calculate the area of the A:A, A:X, and X:X regions as L2A /2, LA LX , and L2X /2, and then use the nominal significance levels of 1 − (1 − α)p , where p is the proportional area for that region. Value An object of class summary.scantwoperm, to be printed by print.summary.scantwoperm. Author(s) Karl W Broman, References Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971. See Also scantwo, summary.scantwo, plot.scantwoperm Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=0) ## Not run: operm <- scantwo(fake.f2, n.perm=100, method="hk") summary(operm) summaryMap summaryMap 265 Print summary of a genetic map Description Print summary information about a map object. Usage ## S3 method for class 'map' summary(object, ...) summaryMap(object, ...) Arguments object An object of class map, which is a list of vectors (or, for a sex-specific map, 2-row matrices), each specifying the locations of the markers. The object can also be of class cross, in which case the function pull.map is used to extract the genetic map from the object. ... Ignored at this point. Value An object of class summary.map, which is just a data.frame containing the number of markers, length, the average inter-marker spacing, and the maximum distance between markers, for each chromosome and overall. An attribute sexsp indicates whether the map was sex-specific. Author(s) Karl W Broman, See Also chrlen, pull.map, summary.cross Examples data(map10) summary(map10) 266 summaryScantwoOld summaryScantwoOld Summarize the results of a two-dimensional genome scan Description Summarize the interesting aspects of the results of scantwo; this is the version of summary.scantwo that was included in R/qtl version 1.03 and earlier. Usage summaryScantwoOld(object, thresholds = c(0, 0, 0), lodcolumn=1, type = c("joint","interaction"), ...) Arguments object An object of class scantwo, the output of the function scantwo. thresholds A vector of length three, giving LOD thresholds for the joint LOD, interaction LOD and single-QTL conditional LOD. Negative threshold values are taken relative to the maximum joint, interaction, or individual QTL LOD, respectively. lodcolumn If the scantwo results contain LOD scores for multiple phenotypes, this argument indicates which to use in the summary. type Indicates whether to pick peaks with maximal joint or interaction LOD. ... Ignored at this point. Details For each pair of chromosomes, the pair of loci for which the LOD score (either joint or interaction LOD, according to the argument type) is a maximum is considered. The pair is printed only if its joint LOD score exceeds the joint threshold and either (a) the interaction LOD score exceeds its threshold or (b) both of the loci have conditional LOD scores that are above the conditional LOD threshold, where the conditional LOD score for locus q1 , LOD(q1 |q2 ), is the log10 likelihood ratio comparing the model with q1 and q2 acting additively to the model with q2 alone. In the case the results of scanone are not available, the maximum locus pair for each chromosome is printed whenever its joint LOD exceeds the joint LOD threshold. The criterion used in this summary is due to Gary Churchill and Śaunak Sen, and deserves careful consideration and possible revision. Value An object of class summary.scantwo.old, to be printed by print.summary.scantwo.old. Pairs of loci meeting the specified criteria are printed, with their joint LOD, interaction LOD, and the conditional LOD for each locus, along with single-point P-values calculated by the χ2 approximation. P-values are printed as − log10 (P ). If the input scantwo object does not include the results of scanone, the interaction and conditional LOD thresholds are ignored, and all pairs of loci for which the joint LOD exceeds its threshold are printed, though without their conditional LOD scores. switch.order 267 Author(s) Hao Wu; Karl W Broman, ; Brian Yandell See Also summary.scantwo, scantwo, plot.scantwo, max.scantwo Examples data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=5) out.2dim <- scantwo(fake.f2, method="hk") # All pairs of loci summaryScantwoOld(out.2dim) # Pairs meeting specified criteria summaryScantwoOld(out.2dim, c(7, 3, 3)) # Pairs with both conditional LODs > 2 summaryScantwoOld(out.2dim,c(0,1000,2)) # Pairs with interaction LOD is above 3 summaryScantwoOld(out.2dim,c(0,3,1000)) switch.order Switch the order of markers on a chromosome Description Switch the order of markers on a specified chromosome to a specified new order. Usage switch.order(cross, chr, order, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), maxit=4000, tol=1e-6, sex.sp=TRUE) Arguments cross An object of class cross. See read.cross for details. chr The chromosome for which the marker order is to be switched. Only one chromosome is allowed. (This should be a character string referring to the chromosomes by name.) order A vector of numeric indices defining the new marker order. The vector may have length two more than the number of markers, for easy in use with the output of the function ripple. error.prob Assumed genotyping error rate (passed to est.map). map.function Map function to be used (passed to est.map). maxit Maximum number of EM iterations to perform. 268 switchAlleles tol Tolerance for determining convergence. sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. Value The input cross object, but with the marker order on the specified chromosome updated, and with any derived data removed (except for recombination fractions, if present, which are not removed); the genetic map for the relevant chromosome is re-estimated. Author(s) Karl W Broman, See Also flip.order, ripple, clean.cross Examples data(fake.f2) fake.f2 <- switch.order(fake.f2, 1, c(1,3,2,4:7)) switchAlleles Switch alleles at selected markers Description Switch alleles at selected markers in a cross object. Usage switchAlleles(cross, markers, switch=c("AB", "CD", "ABCD", "parents")) Arguments cross An object of class cross. See read.cross for details. markers Names of markers whose alleles are to be switched. switch For a 4-way cross, indicates how to switch the alleles (A for B, C for D, both A for B and C for D), or both A for C and B for D (parents). Details For a backcross, we exchange homozygotes (AA) and heterozygotes (AB). For doubled haploids and recombinant inbred lines, we exchange the two homozygotes. For an intercross, we exchange the two homozygotes, and exchange C (i.e., not AA) and D (i.e., not BB). (The heterozygotes in an intercross are left unchanged.) For a 4-way cross, we consider the argument switch, and the exchanges among the genotypes are more complicated. table2map 269 Value The input cross object, with alleles at selected markers switched. Author(s) Karl W Broman, See Also checkAlleles, est.rf, geno.crosstab Examples data(fake.f2) geno.crosstab(fake.f2, "D5M391", "D5M81") # switch homozygotes at marker D5M391 fake.f2 <- switchAlleles(fake.f2, "D5M391") geno.crosstab(fake.f2, "D5M391", "D5M81") ## Not run: fake.f2 <- est.rf(fake.f2) checkAlleles(fake.f2) ## End(Not run) table2map Convert a table of marker positions to a map object. Description Convert a data frame with marker positions to a map object. Usage table2map(tab) Arguments tab A data frame with two columns: chromosome and position. The row names are the marker names. Value A map object: a list whose components (corresponding to chromosomes) are vectors of marker positions. Author(s) Karl W Broman, 270 top.errorlod See Also map2table, pull.map, est.map Examples tab <- data.frame(chr=c(1,1,1,1,2,2,2,2,3,3,3,3), pos=c(0,2,4,8,0,2,4,8,0,2,4,8)) rownames(tab) <- paste0("marker", 1:nrow(tab)) map <- table2map(tab) top.errorlod List genotypes with large error LOD scores Description Prints those genotypes with error LOD scores above a specified cutoff. Usage top.errorlod(cross, chr, cutoff=4, msg=TRUE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. cutoff Only those genotypes with error LOD scores above this cutoff will be listed. msg If TRUE, print a message if there are no apparent errors. Value A data.frame with 4 columns, whose rows correspond to the genotypes that are possibly in error. The four columns give the chromosome number, individual number, marker name, and error LOD score. Author(s) Karl W Broman, See Also calc.errorlod, plotGeno, plotErrorlod totmar 271 Examples data(hyper) # Calculate error LOD scores hyper <- calc.errorlod(hyper,error.prob=0.01) # Print those above a specified cutoff top.errorlod(hyper,cutoff=4) totmar Determine the total number of markers Description Determine the total number of markers in a cross or map object. Usage totmar(object) Arguments object An object of class cross (see read.cross for details) or map (see sim.map for details). Value The total number of markers in the input. Author(s) Karl W Broman, See Also read.cross, plot.cross, summary.cross, nind, nchr, nmar, nphe Examples data(fake.f2) totmar(fake.f2) map <- pull.map(fake.f2) totmar(map) 272 transformPheno transformPheno Transformation of the phenotypes in a cross object Description Transform phenotypes in a cross object; by default use a logarithmic transformation, though any function may be used. Usage transformPheno(cross, pheno.col=1, transf=log, ...) Arguments cross An object of class cross. See read.cross for details. pheno.col A vector of numeric indices or character strings (indicating phenotypes by name) of phenotypes to be transformed. transf The function to use in the transformation. ... Additional arguments, to be passed to transf. Value The input cross object with the transformed phenotypes Author(s) Danny Arends See Also mqmscan, scanone Examples data(multitrait) # Log transformation of all phenotypes multitrait.log <- transformPheno(multitrait, pheno.col=1:nphe(multitrait)) # Square-root transformation of all phenotypes multitrait.sqrt <- transformPheno(multitrait, pheno.col=1:nphe(multitrait), transf=sqrt) tryallpositions tryallpositions 273 Test all possible positions for a marker Description Try all possible positions for a marker, keeping all other markers fixed, and evaluate the log likelihood and estimate the chromosome length. Usage tryallpositions(cross, marker, chr, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), m=0, p=0, maxit=4000, tol=1e-6, sex.sp=TRUE, verbose=TRUE) Arguments cross An object of class cross. See read.cross for details. marker Character string with name of the marker to move about. chr A vector specifying which chromosomes to test for the position of the marker. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. error.prob Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). map.function Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.) m Interference parameter for the chi-square model for interference; a non-negative integer, with m=0 corresponding to no interference. This may be used only for a backcross or intercross. p Proportion of chiasmata from the NI mechanism, in the Stahl model; p=0 gives a pure chi-square model. This may be used only for a backcross or intercross. maxit Maximum number of EM iterations to perform. tol Tolerance for determining convergence. sex.sp Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross. verbose If TRUE, print information on progress. Value A data frame (actually, an object of class "scanone", so that one may use plot.scanone, summary.scanone, etc.) with each row being a possible position for the marker. The first two columns are the chromosome ID and position. The third column is a LOD score comparing the hypotheses that the marker is in that position versus the hypothesis that it is not linked to that chromosome. In the case of a 4-way cross, with sex.sp=TRUE, there are two additional columns with the estimated female and male genetic lengths of the respective chromosome, when the marker is in that position. 274 typingGap With sex.sp=FALSE, or for other types of crosses, there is one additional column, with the estimated genetic length of the respective chromosome, when the marker is in that position. The row names indicate the nearest flanking markers for each interval. Author(s) Karl W Broman, See Also droponemarker, est.map, ripple, est.rf, switch.order, movemarker Examples data(fake.bc) tryallpositions(fake.bc, "D7M301", 7, error.prob=0, verbose=FALSE) typingGap Maximum distance between genotyped markers Description Calculates, for each individual on each chromosome, the maximum distance between genotyped markers. Usage typingGap(cross, chr, terminal=FALSE) Arguments cross An object of class cross. See read.cross for details. chr Optional vector indicating the chromosomes to consider. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. terminal If TRUE, just look at terminal typing gaps (from the terminal markers to the first typed marker). Details We consider not just the distances between internal genotypes, but also distances from the beginning of the chromosome to the first typed marker, and similarly for the end of the chromosome. (The start and end of a chromosome are taken to be the locations of the initial and final markers.) If terminal=TRUE, we look only at those beginning and end distances. Value A matrix with rows corresponding to individuals and columns corresponding to chromosomes. (If there is just one chromosome, it is a numeric vector rather than a matrix.) write.cross 275 Author(s) Karl W Broman, See Also ntyped, nmissing, locateXO Examples data(hyper) plot(typingGap(hyper, chr=5), ylab="Maximum gap between typed markers (cM)", ylim=c(0, diff(range(pull.map(hyper,chr=5)[[1]])))) plot(typingGap(hyper, chr=4), ylab="Maximum gap between typed markers (cM)", ylim=c(0, diff(range(pull.map(hyper,chr=4)[[1]])))) plot(typingGap(hyper, chr=4, terminal=TRUE), ylab="Maximum gap between chr end and typed marker (cM)", ylim=c(0, diff(range(pull.map(hyper,chr=4)[[1]])))) write.cross Write data for a QTL experiment to a file Description Data for a QTL experiment is written to a file (or files). Usage write.cross(cross, format=c("csv", "csvr", "csvs", "csvsr", "mm", "qtlcart", "gary", "qtab", "mapqtl", "tidy"), filestem="data", chr, digits=NULL, descr) Arguments cross format filestem chr digits descr An object of class cross. See read.cross for details. Specifies whether to write the data in comma-delimited, rotated comma-delimited, Mapmaker, QTL Cartographer, Gary Churchill’s, QTAB, MapQTL format. A character string giving the first part of the output file names (the bit before the dot). In Windows, use forward slashes ("/") or double backslashes ("\\") to specify directory trees. A vector specifying for which chromosomes genotype data should be written. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. Number of digits to which phenotype values and genetic map positions should be rounded. If NULL (the default), they are not rounded. Character string description; used only with format="qtab". 276 write.cross Details Comma-delimited formats: a single csv file is created in the formats "csv" or "csvr". Two files are created (one for the genotype data and one for the phenotype data) for the formats "csvs" and "csvsr"; if filestem="file", the two files will be names "file_gen.csv" and "file_phe.csv". See the help file for read.cross for details on these formats. Mapmaker format: Data is written to two files. Suppose filestem="file". Then "file.raw" will contain the genotype and phenotype data, and "file.prep" will contain the necessary code for defining the chromosome assignments, marker order, and inter-marker distances. QTL Cartographer format: Data is written to two files. Suppose filestem="file". Then "file.cro" will contain the genotype and phenotype data, and "file.map" will contain the genetic map information. Note that cross types are converted to QTL Cartographer cross types as follows: riself to RF1, risib to RF2, bc to B1 and f2 to RF2. Gary’s format: Data is written to six files. They are: "geno.data" - genotype data; "pheno.data" - phenotype data; "chrid.dat" - the chromosome identifier for each marker; "mnames.txt" - the marker names; "markerpos.txt" - the marker positions; "pnames.txt" - the phenotype names QTAB format: See documentation. MapQTL format: See documentation. Tidy format: Data is written to three files, "stem_gen.csv", "stem_phe.csv", and "stem_map.csv" (where stem is taken from the filestem argument. Author(s) Karl W Broman, ; Hao Wu; Brian S. Yandell; Danny Arends; Aaron Wolen See Also read.cross Examples ## Not run: data(fake.bc) # comma-delimited format write.cross(fake.bc, "csv", "Data/fakebc", c(1,5,13)) # rotated comma-delimited format write.cross(fake.bc, "csvr", "Data/fakebc", c(1,5,13)) # split comma-delimited format write.cross(fake.bc, "csvs", "Data/fakebc", c(1,5,13)) # split and rotated comma-delimited format write.cross(fake.bc, "csvsr", "Data/fakebc", c(1,5,13)) # Mapmaker format write.cross(fake.bc, "mm", "Data/fakebc", c(1,5,13)) xaxisloc.scanone 277 # QTL Cartographer format write.cross(fake.bc, "qtlcart", "Data/fakebc", c(1,5,13)) # Gary's format write.cross(fake.bc, "gary", c(1,5,13)) ## End(Not run) xaxisloc.scanone Get x-axis locations in scanone plot Description Get x-axis locations for given cM positions on given chromosomes in a plot from plot.scanone) Usage xaxisloc.scanone(out, thechr, thepos, chr, gap=25) Arguments out An object of class "scanone", as output by scanone. This must be identical to what was used in the call to plot.scanone. thechr Chromosome IDs at which x-axis locations are to be determined. thepos Chromosome positions at which x-axis locations are to be determined. chr Optional vector specifying which chromosomes were plotted. This must be identical to what was used in the call to plot.scanone. gap Gap separating chromosomes (in cM). This must be identical to what was used in the call to plot.scanone. Details This function allows you to identify the x-axis locations in a plot of genome scan results, produced by plot.scanone. This is useful for adding annotations, such as text or arrows. The arguments out, chr, and gap must match what was used in the call to plot.scanone. The arguments thechr and thepos indicate the genomic positions for which x-axis locations are desired. If they both have length > 1, they must have the same length. If one has length > 1 and one has length 1, the one with length 1 is expanded to match. Value A numeric vector of x-axis locations. Author(s) Karl W Broman, See Also plot.scanone, add.threshold 278 xaxisloc.scanone Examples data(hyper) hyper <- calc.genoprob(hyper) out <- scanone(hyper, method="hk") plot(out, chr=c(1, 4, 6, 15)) # add arrow and text to indicate peak LOD score mxout <- max(out) x <- xaxisloc.scanone(out, mxout$chr, mxout$pos, chr=c(1,4,6,15)) arrows(x+30, mxout$lod, x+5, mxout$lod, len=0.1, col="blue") text(x+35, mxout$lod, "the peak", col="blue", adj=c(0, 0.5)) Index plot.scanoneboot, 160 plot.scanoneperm, 161 plot.scanPhyloQTL, 162 plot.scantwo, 163 plot.scantwoperm, 166 plotErrorlod, 167 plotGeno, 168 plotInfo, 170 plotLodProfile, 171 plotMap, 173 plotMissing, 175 plotModel, 176 plotPheno, 177 plotPXG, 178 plotRF, 180 xaxisloc.scanone, 277 ∗Topic manip c.cross, 32 c.scanone, 33 c.scanoneperm, 34 c.scantwo, 35 c.scantwoperm, 36 cbind.scanoneperm, 41 cbind.scantwoperm, 42 clean.cross, 47 clean.scantwo, 48 convert.map, 53 convert.scanone, 54 convert.scantwo, 55 convert2riself, 56 convert2risib, 57 convert2sa, 58 drop.dupmarkers, 60 drop.markers, 60 drop.nullmarkers, 61 findDupMarkers, 81 flip.order, 87 interpPositions, 98 jittermap, 99 movemarker, 113 pickMarkerSubset, 154 pull.markers, 186 replace.map, 200 ∗Topic IO read.cross, 188 readMWril, 195 write.cross, 275 ∗Topic arith arithscan, 26 arithscanperm, 27 ∗Topic datagen sim.cross, 230 sim.map, 234 simFounderSnps, 235 simPhyloQTL, 236 ∗Topic datasets badorder, 28 bristle3, 30 bristleX, 31 fake.4way, 72 fake.bc, 73 fake.f2, 74 hyper, 95 listeria, 100 locations, 102 map10, 105 mapthis, 107 multitrait, 144 ∗Topic hplot add.cim.covar, 10 add.threshold, 11 effectplot, 64 effectscan, 67 geno.image, 91 mqmplot.cistrans, 126 mqmplot.clusteredheatmap, 127 mqmplot.cofactors, 129 mqmplot.directedqtl, 130 mqmplot.heatmap, 131 mqmplot.multitrait, 132 mqmplot.permutations, 133 mqmplot.singletrait, 134 plot.cross, 155 plot.qtl, 156 plot.rfmatrix, 157 plot.scanone, 158 279 280 replacemap.scanone, 201 replacemap.scantwo, 202 strip.partials, 243 subset.cross, 244 subset.map, 245 subset.scanone, 246 subset.scanoneperm, 247 subset.scantwo, 248 subset.scantwoperm, 249 switch.order, 267 ∗Topic models A starting point, 6 addcovarint, 12 addint, 14 addpair, 18 addqtl, 20 cim, 45 fitqtl, 82 fitstahl, 85 MQM, 114 mqmautocofactors, 118 mqmfind.marker, 120 mqmpermutation, 122 mqmprocesspermutation, 135 mqmscan, 136 mqmscanall, 138 mqmscanfdr, 140 mqmsetcofactors, 141 scanone, 206 scanonevar, 216 scanPhyloQTL, 219 scanqtl, 221 scantwo, 223 scantwopermhk, 227 stepwiseqtl, 239 ∗Topic print chrlen, 44 condense.scantwo, 52 inferredpartitions, 97 max.scanone, 109 max.scanPhyloQTL, 110 max.scantwo, 112 nchr, 145 nind, 146 nmar, 146 nphe, 148 nqtl, 149 qtlversion, 188 summary.cross, 250 summary.fitqtl, 250 summary.qtl, 251 summary.ripple, 252 INDEX summary.scanone, 253 summary.scanoneperm, 257 summary.scanPhyloQTL, 259 summary.scantwo, 261 summary.scantwoperm, 264 summaryMap, 265 summaryScantwoOld, 266 top.errorlod, 270 totmar, 271 ∗Topic univar plotInfo, 170 ∗Topic utilities addloctocross, 16 addmarker, 17 addtoqtl, 22 allchrsplits, 24 argmax.geno, 25 bayesint, 29 calc.errorlod, 37 calc.genoprob, 38 calc.penalties, 40 checkAlleles, 43 chrnames, 45 cleanGeno, 49 comparecrosses, 50 comparegeno, 50 compareorder, 51 countXO, 59 dropfromqtl, 62 droponemarker, 63 est.map, 69 est.rf, 71 fill.geno, 75 find.flanking, 76 find.marker, 77 find.markerindex, 78 find.markerpos, 78 find.pheno, 79 find.pseudomarker, 80 formLinkageGroups, 88 formMarkerCovar, 89 geno.crosstab, 90 geno.table, 92 getid, 93 groupclusteredheatmap, 94 inferFounderHap, 96 locateXO, 101 lodint, 103 makeqtl, 104 map2table, 106 markerlrt, 108 markernames, 108 INDEX mqmaugment, 116 mqmextractmarkers, 119 mqmgetmodel, 121 mqmplot.circle, 124 mqmtestnormal, 143 nmissing, 147 nqrank, 148 ntyped, 150 nullmarkers, 151 orderMarkers, 151 phenames, 153 pull.argmaxgeno, 181 pull.draws, 182 pull.geno, 183 pull.genoprob, 184 pull.map, 185 pull.pheno, 186 pull.rf, 187 reduce2grid, 196 refineqtl, 197 reorderqtl, 199 replaceqtl, 203 rescalemap, 204 ripple, 205 scanoneboot, 214 shiftmap, 229 sim.geno, 233 simulatemissingdata, 238 summary.scanoneboot, 256 switchAlleles, 268 table2map, 269 transformPheno, 272 tryallpositions, 273 typingGap, 274 +.scanone, 211 +.scanone (arithscan), 26 +.scanoneperm (arithscanperm), 27 +.scantwo (arithscan), 26 +.scantwoperm (arithscanperm), 27 -.scanone (arithscan), 26 -.scanoneperm (arithscanperm), 27 -.scantwo (arithscan), 26 -.scantwoperm (arithscanperm), 27 .Rprofile, 6 [.cross (subset.cross), 244 [.map (subset.map), 245 [.scanoneperm (subset.scanoneperm), 247 [.scantwoperm (subset.scantwoperm), 249 A starting point, 6 abline, 11 add.cim.covar, 10, 47 add.threshold, 11, 159, 277 281 addcovarint, 12, 15 addint, 13, 14, 20, 22 addloctocross, 16, 125 addmarker, 17 addpair, 13, 15, 18, 22, 113, 165, 242, 262 addqtl, 13, 15, 20, 20, 242 addtoqtl, 20, 22, 22, 62, 84, 105, 149, 199, 203 allchrsplits, 24 argmax.geno, 25, 39, 47, 75, 76, 87, 96, 181, 182, 200, 231, 234 arithscan, 26 arithscanperm, 27 attr, 84, 138 badorder, 28, 71, 108, 181 barplot, 177, 178 bayesint, 29, 103, 215, 216, 254, 257 bristle3, 30, 72–74, 95, 100 bristleX, 31, 31, 32, 72–74, 95, 100 c.cross, 32, 194 c.scanone, 33, 36, 41, 42, 255 c.scanoneperm, 34, 42, 247, 255 c.scantwo, 35 c.scantwoperm, 35, 36, 42, 226, 228, 249 calc.errorlod, 7, 37, 39, 49, 87, 168, 169, 270 calc.genoprob, 7, 8, 18, 21, 26, 33, 35, 38, 47, 60, 61, 76, 80, 87, 89, 96, 104, 105, 113, 170, 171, 184, 186, 196, 197, 200, 208, 211, 217, 221, 224, 225, 231, 234 calc.penalties, 40, 241, 242 cbind.scanone (c.scanone), 33 cbind.scanoneperm, 34, 35, 41, 247 cbind.scantwo (c.scantwo), 35 cbind.scantwoperm, 37, 42 checkAlleles, 43, 71, 269 chrlen, 44, 265 chrnames, 45, 109, 153 cim, 10, 45, 123, 140 clean, 99 clean.cross, 47, 60, 61, 186, 194, 268 clean.scantwo, 47, 48, 224 cleanGeno, 38, 49, 59, 102 colors, 159, 163 comparecrosses, 50 comparegeno, 50 compareorder, 51 condense.scantwo, 52, 263 contour, 164 convert.map, 53 282 convert.scanone, 54, 56 convert.scantwo, 55, 55 convert2riself, 56, 57 convert2risib, 56, 57 convert2sa, 58 countXO, 49, 59, 102, 152 data, 6 drop.dupmarkers, 60 drop.markers, 17, 47, 60, 60, 61, 64, 81, 93, 154, 186, 244 drop.nullmarkers, 7, 47, 60, 61, 61, 81, 93, 151, 186 dropfromqtl, 23, 62, 84, 105, 149, 199, 203 droponemarker, 63, 274 effectplot, 64, 68, 77, 80, 179 effectscan, 66, 67, 179 est.map, 7, 25, 29, 54, 58, 64, 69, 71, 86, 106, 107, 137, 152, 174, 189, 200–202, 204–206, 229, 253, 267, 270, 274 est.rf, 7, 25, 29, 43, 64, 70, 71, 87, 88, 107, 108, 113, 157, 181, 187, 188, 206, 253, 269, 274 example, 6 fake.4way, 31, 32, 72, 73, 74, 95, 100, 232 fake.bc, 31, 32, 72, 73, 74, 95, 100, 232 fake.f2, 31, 32, 72, 73, 74, 95, 100, 107, 232 fill.geno, 26, 46, 47, 75, 89, 96, 117 find.flanking, 76, 77, 79, 80, 179 find.marker, 66, 76, 77, 79, 80, 90, 179 find.markerindex, 78 find.markerpos, 76–78, 78, 80 find.pheno, 79 find.pseudomarker, 66, 76, 77, 79, 80 findDupMarkers, 81 fitqtl, 13, 15, 20, 22, 23, 62, 82, 104, 105, 149, 199, 203, 222, 242, 250, 251 fitstahl, 70, 85 flip.order, 87, 268 formLinkageGroups, 88, 107, 152 formMarkerCovar, 89 formula, 12, 14, 18, 21, 82, 198, 221 geno.crosstab, 43, 90, 269 geno.image, 91, 176 geno.table, 61, 90, 92, 171, 186 getid, 93 groupclusteredheatmap, 94 heatmap, 128 help.start, 6 INDEX hist, 160, 161, 166, 177, 178 hyper, 6, 31, 32, 72–74, 95, 100 image, 91, 131, 165, 167, 168, 175, 176, 180, 181 inferFounderHap, 96 inferredpartitions, 97, 111, 163, 220, 237, 260 interpPositions, 98 jittermap, 99 listeria, 31, 32, 72–74, 95, 100 locateXO, 49, 59, 101, 275 locations, 102 lodint, 30, 103, 215, 216, 254, 257 makeqtl, 12–15, 18, 20–23, 62, 82, 84, 104, 122, 149, 156, 157, 173, 176, 177, 197–199, 203, 222, 239, 242, 251, 252 map10, 105 map2table, 70, 106, 185, 270 mapthis, 107 markerlrt, 87, 108 markernames, 45, 108, 153 max.scanone, 8, 109, 111, 211, 255 max.scanPhyloQTL, 97, 110, 163, 220, 237, 260 max.scantwo, 9, 19, 53, 112, 226, 263, 267 movemarker, 25, 52, 64, 113, 274 MQM, 16, 94, 114, 115, 117–120, 122, 124, 125, 127–135, 138, 139, 141–143, 238 mqmaugment, 16, 94, 115, 116, 117–120, 122, 124, 125, 127–135, 138, 139, 141–143, 239 mqmautocofactors, 16, 94, 115, 117, 118, 118, 119, 120, 122–125, 127–136, 138, 139, 141–143, 239 mqmextractmarkers, 119 mqmfind.marker, 120 mqmgetmodel, 121, 138 mqmpermutation, 16, 94, 115, 117–120, 122, 122, 124, 125, 127–133, 135, 138, 139, 141–143, 239 mqmplot.circle, 124 mqmplot.cistrans, 16, 126 mqmplot.clusteredheatmap, 94, 127 mqmplot.cofactors, 129 mqmplot.directedqtl, 130 mqmplot.heatmap, 131 mqmplot.multitrait, 132 mqmplot.permutations, 133 INDEX mqmplot.singletrait, 134, 137 mqmprocesspermutation, 120, 135 mqmscan, 16, 94, 115, 117–120, 122–125, 127–135, 136, 138–143, 238, 272 mqmscanall, 16, 94, 115, 117–120, 122, 124–135, 138, 138, 139, 141–143, 239 mqmscanfdr, 140 mqmsetcofactors, 16, 94, 115, 117–120, 122, 124, 125, 127–133, 135, 136, 138, 139, 141, 141, 142, 143, 239 mqmtestnormal, 137, 143 multitrait, 102, 103, 144 nchr, 145, 146–148, 250, 271 nind, 145, 146, 147, 148, 150, 250, 271 nmar, 145, 146, 146, 148, 250, 271 nmissing, 51, 147, 150, 275 nphe, 145–147, 148, 250, 271 nqrank, 148 nqtl, 149 ntyped, 147, 150, 275 nullmarkers, 61, 151 orderMarkers, 88, 107, 151 par, 159, 163, 169 phenames, 45, 109, 153 pickMarkerSubset, 154 plot, 67, 132, 159, 172, 174, 176, 179 plot.cross, 6, 91, 145–148, 155, 174, 176, 178, 194, 250, 271 plot.map (plotMap), 173 plot.qtl, 129, 156 plot.rfmatrix, 71, 157, 181, 188 plot.scanone, 8, 10, 11, 24, 47, 64, 92, 110, 132, 134, 137, 157, 158, 162, 163, 165, 170, 171, 196, 197, 211, 255, 273, 277 plot.scanoneboot, 160, 216, 257 plot.scanoneperm, 161, 258 plot.scanPhyloQTL, 97, 111, 162, 220, 237, 260 plot.scantwo, 8, 19, 113, 163, 226, 263, 267 plot.scantwoperm, 166, 228, 264 plotErrorlod, 37, 38, 167, 190, 192, 270 plotGeno, 7, 37, 91, 168, 168, 190, 192, 270 plotInfo, 170, 243 plotLodProfile, 171, 198, 199, 242 plotMap, 6, 7, 58, 70, 106, 155–157, 173, 178, 185, 235 plotMissing, 6, 91, 155, 171, 175, 178, 243 plotModel, 176, 242 283 plotPheno, 155, 177 plotPXG, 66, 68, 76, 77, 80, 178 plotRF, 71, 108, 157, 180, 188 points, 10, 159 polyplot, 133 pull.argmaxgeno, 181, 182–184 pull.draws, 182, 182, 183, 184 pull.geno, 89, 182, 183, 184, 187 pull.genoprob, 89, 182, 183, 184 pull.map, 44, 99, 100, 106, 109, 183, 185, 187, 200, 235, 244, 265, 270 pull.markers, 17, 60, 61, 154, 186 pull.pheno, 183, 186 pull.rf, 71, 157, 181, 187 qnorm, 149 qtl-package (A starting point), 6 qtlversion, 188 rank, 149 rbind.scanoneperm, 247 rbind.scanoneperm (c.scanoneperm), 34 rbind.scantwoperm, 249 rbind.scantwoperm (c.scantwoperm), 36 read.cross, 6, 12, 14, 16–18, 21, 23–25, 29, 31, 33, 37, 38, 43, 45–47, 49–52, 56, 57, 59–61, 63, 69, 71–82, 85, 87–96, 99–101, 104, 107–109, 113, 116, 118, 120, 123, 125, 126, 128–131, 136, 139, 140, 142–148, 150–153, 155, 167, 168, 170, 175, 177, 178, 180–187, 188, 195–197, 200, 203–205, 207, 214, 216–219, 221, 223, 227, 229, 231–233, 237–239, 243, 244, 250, 267, 268, 270–276 read.csv, 17 read.table, 190, 191, 194, 195 readMWril, 195 reduce2grid, 196 refineqtl, 13, 15, 20, 22, 30, 84, 103, 171–173, 197, 222, 240, 242 reorderqtl, 23, 62, 84, 105, 149, 199, 203 replace.map, 7, 54, 70, 100, 185, 200, 204, 229, 235 replacemap.cross, 201, 202 replacemap.cross (replace.map), 200 replacemap.scanone, 201, 202 replacemap.scantwo, 201, 202 replaceqtl, 23, 62, 84, 105, 149, 199, 203 rescalemap, 99, 204 ripple, 25, 29, 52, 59, 64, 152, 181, 205, 252, 253, 267, 268, 274 rug, 166 284 save.image, 8 scanone, 7, 8, 11, 16, 22, 26–30, 33–35, 40–42, 46, 47, 54, 55, 68, 80, 89, 92, 94, 103, 109, 110, 115, 117–120, 122–125, 127–133, 135, 137–143, 157–161, 165, 196, 197, 201, 206, 215–217, 219, 220, 225, 226, 239, 246, 247, 253–255, 257, 258, 266, 272, 277 scanoneboot, 160, 214, 256, 257 scanonevar, 216 scanonevar.meanperm, 217 scanonevar.varperm, 218 scanPhyloQTL, 97, 110, 111, 162, 163, 219, 237, 259, 260 scanqtl, 13, 15, 19–22, 84, 173, 198, 199, 221, 251 scantwo, 8, 9, 18–20, 26–28, 35–37, 40–42, 48, 52, 53, 55, 56, 112, 113, 164–167, 202, 211, 223, 228, 241, 248, 249, 254, 261–264, 266, 267 scantwopermhk, 227 set.seed, 28 shapiro.test, 143 shiftmap, 99, 229 sim.cross, 29, 72–74, 86, 107, 194, 196, 230, 235–237 sim.geno, 7, 8, 18, 21, 26, 33, 35, 39, 47, 68, 75, 76, 87, 96, 104, 105, 179, 182, 196, 197, 200, 208, 211, 221, 224, 225, 231, 233 sim.map, 98, 106, 145, 146, 204, 229, 232, 234, 236, 271 simFounderSnps, 231, 232, 235 simPhyloQTL, 97, 111, 163, 220, 236, 260 simulatemissingdata, 238 stepwiseqtl, 40, 41, 177, 239 strip.partials, 243 subset.cross, 33, 93, 167–169, 190, 194, 244, 245 subset.map, 244, 245 subset.scanone, 246, 255 subset.scanoneperm, 247 subset.scantwo, 248 subset.scantwoperm, 249 summary.cross, 6, 44, 50, 60, 93, 100, 145–148, 150, 191, 194, 250, 265, 271 summary.fitqtl, 84, 250 summary.map (summaryMap), 265 summary.qtl, 105, 149, 251 summary.ripple, 206, 252 INDEX summary.scanone, 8, 24, 34, 47, 64, 110, 157, 159, 171, 211, 217, 246, 253, 258, 260, 273 summary.scanoneboot, 160, 215, 216, 256 summary.scanoneperm, 8, 11, 35, 42, 135, 161, 211, 217, 247, 255, 257 summary.scanPhyloQTL, 97, 111, 163, 220, 237, 259 summary.scantwo, 9, 19, 36, 48, 53, 112, 113, 165, 225, 226, 248, 261, 264, 266, 267 summary.scantwoperm, 37, 42, 167, 226, 228, 249, 264 summaryMap, 44, 265 summaryScantwoOld, 262, 266 switch.order, 25, 52, 64, 87, 114, 206, 267, 274 switchAlleles, 43, 268 table2map, 106, 269 top.errorlod, 7, 37, 38, 93, 168, 169, 190, 192, 270 totmar, 145–148, 150, 250, 271 transformPheno, 272 tryallpositions, 273 typingGap, 274 write.cross, 194, 275 xaxisloc.scanone, 11, 159, 277
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