QTL Mapping in R. March 16, 2010
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1 QTL Mapping in R March 16, 2010
2 R qtl library(qtl) R Library: qtl sim.map: constructs a genetic map sim.cross: simulates marker, QTL, and phenotype data calc.genoprob: calculates conditional probabilities scanone: produces a QTL map OR performs permutation tests summary.scanone: identify QTLs and calculatie permutation thresholds add.threshold: adds a permutation threshold to QTL plot cim: performs composite interval mapping find.marker: finds a marker near a specified position pull.geno: extracts genotype data from a given marker The residual phenotypic variation is normally distributed with variance 1. QTL effect: difference between the homozygote and the heterozygote March 16, 2010 QTL Mapping in R 2 / 19
3 Ghost QTL set.seed(1) n<-200 map<-sim.map(len=100,n.mar=21,include.x=false,eq.spacing=true) cross<-sim.cross(map,model = rbind(c(1,22,1),c(1,73,1)), type="bc",n.ind=n) crossb<-calc.genoprob(cross,step=1) est<-scanone(crossb) plot(est,lwd=4,main="simulated Backcross") perm1<-scanone(crossb,chr=1,n.perm=500) thresh<-summary(perm1,alpha=.05)[1] thresh add.threshold(est,perm=perm1,col="orange",lwd=4) abline(v=22,col="magenta",lwd=4) abline(v=73,col="magenta",lwd=4) [1] March 16, 2010 QTL Mapping in R 3 / 19
4 Ghost QTL 15 Simulated Backcross 10 lod Map position (cm) March 16, 2010 QTL Mapping in R 4 / 19
5 Ghost QTL est.cim.10 <- cim(crossb, n.marcovar=4, window=10) est.cim.20 <- cim(crossb, n.marcovar=4, window=20) plot(est,est.cim.10,est.cim.20,col=c("black","red","blue"),lwd=3,lty=1:3) add.threshold(est,perm=perm1,col="orange",lwd=4) abline(v=22,col="magenta",lwd=4) abline(v=73,col="magenta",lwd=4) axis(1,at=22,line=-1) axis(1,at=73,line=-1) March 16, 2010 QTL Mapping in R 5 / 19
6 Composite Interval Mapping lod Map position (cm)
7 Why we need the window mar1<- find.marker(crossb,1,22) mar2<- find.marker(crossb,1,73) g1<-pull.geno(crossb)[,mar1] g2<-pull.geno(crossb)[,mar2] est2<-scanone(crossb, addcovar=cbind(g1,g2)) plot(est2,lwd=3,main="covariates chosen near the QTLs") add.threshold(est2,perm=perm1,col="orange",lwd=3) abline(v=22,col="magenta",lwd=4) abline(v=73,col="magenta",lwd=4) March 16, 2010 QTL Mapping in R 7 / 19
8 2.0 Covariates chosen near the QTLs 1.5 lod Map position (cm)
9 The Dataset hyper data(hyper) nind(hyper); nphe(hyper); nchr(hyper); totmar(hyper); nmar(hyper); [1] 250 [1] 2 [1] 20 [1] X summary(hyper) Backcross No. individuals: 250 No. phenotypes: 2 Percent phenotyped: No. chromosomes: 20 Autosomes: X chr: X Total markers: 174 No. markers: Percent genotyped: 47.7 Genotypes (%): BB:50.2 BA:49.8 March 16, 2010 QTL Mapping in R 9 / 19
10 plot(hyper) Missing genotypes X Location (cm) Genetic map bp X Chromosome sex Frequency female male March 16, 2010 QTL Mapping in R 10 / 19
11 plot.missing(hyper, reorder=true) 250 Missing genotypes X 200 Individuals March 16, 2010 QTL Mapping in R 11 / 19
12 More Commands plot(hyper) plot.missing(hyper) plot.map(hyper) plot.pheno(hyper, pheno.col=1) plot.map(hyper, chr=c(1, 4, 6, 7, 15), show.marker.names=true) plot.missing(hyper, reorder=true) hyper <- drop.nullmarkers(hyper) totmar(hyper) hyper <- est.rf(hyper) newmap <- est.map(hyper, error.prob=0.01) March 16, 2010 QTL Mapping in R 12 / 19
13 plot.rf(hyper) Markers Pairwise recombination fractions and LOD scores X X March 16, 2010 QTL Mapping in R 13 / 19
14 plot.map(hyper, newmap) 0 20 Comparison of genetic maps Location (cm) X Chromosome March 16, 2010 QTL Mapping in R 14 / 19
15 More Commands hyper <- replace.map(hyper, newmap) hyper <- calc.errorlod(hyper, error.prob=0.01) top.errorlod(hyper) chr id marker errorlod D16Mit D16Mit D16Mit D16Mit D16Mit D16Mit D13Mit D13Mit D16Mit D16Mit id<-top.errorlod(hyper)$id plot.info(hyper,chr=c(1,4)) info <- plot.info(hyper) summary(info) March 16, 2010 QTL Mapping in R 15 / 19
16 plot.geno(hyper, chr=16, ind=id) Chromosome 16 Location (cm) Individual March 16, 2010 QTL Mapping in R 16 / 19
17 plot.info(hyper) 1.0 Missing information 0.8 misinfo.entropy X Chromosome March 16, 2010 QTL Mapping in R 17 / 19
18 plot.info(hyper,chr=13) 1.0 Missing information 0.8 misinfo.entropy Map position (cm) March 16, 2010 QTL Mapping in R 18 / 19
19 Importing Data mydata <- read.cross("csv", "/Users/berg/Documents/courses/phs 516/slides/lecture 6", "plantdata.csv",genotypes=c("a","h")) summary(mydata) Backcross No. individuals: 200 No. phenotypes: 28 Percent phenotyped: No. chromosomes: 11 Autosomes: Total markers: 231 No. markers: Percent genotyped: 100 Genotypes (%): AA:49.7 AB:50.3 March 16, 2010 QTL Mapping in R 19 / 19
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