QTL with GxE or Covariates

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QTL with GxE or Covariates adjust phenotype by covariate covariate(s) = environment(s) or other trait(s) additive covariate covariate adjustment same across genotypes “usual” analysis of covariance (ANCOVA) interacting covariate address GxE capture genotype-specific relationship among traits another way to think of multiple trait analysis examine single phenotype adjusted for others QTL II: GxE Seattle SISG: Yandell © 2005

Seattle SISG: Yandell © 2005 R/qtl & covariates additive and/or interacting covariates test for QTL after adjusting for covariates ## Get Brassica data. library(qtlbim) data(Bnapus) Bnapus <- calc.genoprob(Bnapus, step = 2, error = 0.01) ## Scatterplot of two phenotypes: 4wk & 8wk flower time. plot(Bnapus$pheno$log10flower4,Bnapus$pheno$log10flower8) ## Unadjusted IM scans of each phenotype. fl8 <- scanone(Bnapus,, find.pheno(Bnapus, "log10flower8")) fl4 <- scanone(Bnapus,, find.pheno(Bnapus, "log10flower4")) plot(fl4, fl8, col = c("blue","red"), chr = "N2", main = "blue = 4wk, red = 8wk") QTL II: GxE Seattle SISG: Yandell © 2005

Seattle SISG: Yandell © 2005 QTL II: GxE Seattle SISG: Yandell © 2005

Seattle SISG: Yandell © 2005 R/qtl & covariates additive and/or interacting covariates test for QTL after adjusting for covariates ## IM scan of 8wk adjusted for 4wk. ## Adjustment independent of genotype fl8.4 <- scanone(Bnapus,, find.pheno(Bnapus, "log10flower8"), addcov = Bnapus$pheno$log10flower4) ## Adjustment changes with genotype. intcov = Bnapus$pheno$log10flower4) plot(fl8, fl8.4a, fl8.4, chr = "N2", main = "black = 8wk, blue = addcov, red = intcov") QTL II: GxE Seattle SISG: Yandell © 2005

Seattle SISG: Yandell © 2005 QTL II: GxE Seattle SISG: Yandell © 2005

Seattle SISG: Yandell © 2005 QTL II: GxE Seattle SISG: Yandell © 2005

scatterplot adjusted for covariate ## Set up data frame with peak markers, traits. markers <- c("E38M50.133","ec2e5a","wg7f3a") tmpdata <- data.frame(pull.geno(Bnapus)[,markers]) tmpdata$fl4 <- Bnapus$pheno$log10flower4 tmpdata$fl8 <- Bnapus$pheno$log10flower8 ## Scatterplots grouped by marker. library(lattice) xyplot(fl8 ~ fl4, tmpdata, group = wg7f3a, col = c("blue","red"), cex = 2, type = c("p","r"), xlab = "log10(4wk flower time)", ylab = "log10(8wk flower time)", main = "marker at 47cM") xyplot(fl8 ~ fl4, tmpdata, group = E38M50.133, main = "marker at 80cM") QTL II: GxE Seattle SISG: Yandell © 2005

Seattle SISG: Yandell © 2005 R/qtlbim and GxE similar idea to R/qtl fixed and random additive covariates GxE with fixed covariate multiple trait analysis tools coming soon theory & code mostly in place properties under study expect in R/qtlbim later this year Samprit Banerjee (N Yi, advisor) QTL II: GxE Seattle SISG: Yandell © 2005