Ler/Shah QTL mapping

Quantitative Trait Locus Analysis of Growth-Related Traits in a New Arabidopsis Recombinant Inbred Population1 Mohamed E. El-Lithy, Emile J.M. Clerkx, Gerda J. Ruys, Maarten Koornneef and Dick Vreugdenhil* Plant Physiol. 2004 May; 135(1): 432–443. doi: 10.1104/pp.103.036814.

R statistical programming language

• http://cran.r-project.org download from a local mirror. Any platform 2.3.1

• In R, • > install.packages(c(“bqtl”,”qtl”))• update.packages()

– Even for fresh install, perform weekly

• You may like WinEdt– For text editing…. – > install.packages(“RWinEdt”)

• Need, genotype data in R/QTL format– Little excel formating work

• Example from Werner et al– http://naturalvariation.org/werner– Get

• Genotype/Phenotype data for Lz-0 x Ler F2 (A = Lz, B = Ler, H = het)

• and script for QTL mapping output

• Line names and numbers from – Arabidopsis.org

• search seed stock CS24481

Read in data

Example Ler.Shah.csv

Missing data is NA, marker names must only have “.” not “-”, or “/”Row1 header, row2 blank for traits chr for markers, row3 cM or blank

Genetic Map

Download setup filehttp://naturalvariation.org/qtl/lershah.csv

We will add our trait data…

Read in code• > setwd("../Ler.Shah") # set working directory• > #install.packages("qtl")• > library(qtl) # load qtl packag• > lersha <- read.cross(“csv”,file = "lershah.csv")• --Read the following data:• 114 individuals• 66 markers• 1 phenotypes• --Estimating genetic map in: read.cross.csv(dir, file, na.strings, genotypes,

estimate.map, • > summary(lersha)• F2 intercross• No. individuals: 114 • No. phenotypes: 1 • Percent phenotyped: 100 • No. chromosomes: 5 • Total markers: 66 • No. markers: 15 13 10 15 13 • Percent genotyped: 98.8 • Genotypes (%): AA:54 AB:0 BB:46 not BB:0 not AA:0 • > plot(lersha)

> source(“adjustri.R”)> plot.map(adjustri(lersha, expand = F, type = "self"))

> image(pull.geno(lersha),col = topo.colors(2), xlab = “RILs”, ylab = “markers”)

Copyright ©2004 American Society of Plant Biologists

El-Lithy, M. E., et al. Plant Physiol. 2004;135:444-458

Frequency distribution of nonnormalized data of some traits in the Ler x Sha RIL population

> im <- adjustri(scanone(lersha,pheno.col = 1), expand = F, type = "self") Warning message:First running calc.genoprob. in: scanone(lersha, pheno.col = 1) > plot(im,main = "names QTL Interval Map",ylim = c(0,10))> abline(h = 3) # standard threshold..

95% by perms

sc2 <- scantwo(lersha,pheno.col = 1)plot(sc2,main = "names QTL 2D Map")

bQTL• map.list <- pull.map(adjustri(lersha, expand = F, type = "self"))• tmp.frm <- list()• for ( i in 1:length(map.list))• tmp.frm[[i]] <- data.frame(chr.num = i,• Marker = 1:length(map.list[[i]]),• marker.names = names(map.list[[i]]),• cM = as.numeric(map.list[[i]]) )• map.dx <- do.call("rbind", tmp.frm )• lersha.map.2 <- make.map.frame(map.dx,reso=2)• lersha.ana.2 <-

make.analysis.obj(lersha$pheno,lersha.map.2,pull.geno(lersha),• marker.levels=ri.levels(1,3,"NA"),• method="RI.self")• save(lersha.ana.2, file = "lersha.ana.2.RData", compress = T)• plot(lersha.ana.2)

Plot bQTL map

bQTL Interval Map

imap <- bqtl(CSstockNum ~ locus(all),lersha.ana.2)

# LOD conversion

lodL <- (loglik(imap)-min(loglik(imap)))*2/4.6

par(mfrow=c(2,3))

plot(lersha.ana.2,lodL)

bQTL Bayesian multigene

## linear bayes plots 10 gene modellersha.LB.10LD <- linear.bayes(CSstockNum ~ locus(all), lersha.ana.2, rparm = 1, specs = list(gene.number= 1:10,burn.in=1,n.cycles=c(0,0,200,rep(100,7))) )

## posterior odds plotsplot(lersha.ana.2, lersha.LB.10LD$loc.posterior, ylab = "Posterior Odds Ratio")matplot(lersha.LB.10LD$specs$gene.number, lersha.LB.10LD$odds/ max(lersha.LB.10LD$odds), xlab = "gene number", ylab = "relative fit", main = "multigene fit",type = "l")

## additive coefsplot( ndc .ana.2, lersha.LB.10LD$coefficients, ylab = "additive effect")plot(1,1,type="n",main = "additive coefs")

Posterior Odds ratio

Additive effects