Hi all, I want to associate mortality with ~100K SNPs, in 6,500 samples that are divided up into 60 breeds. So it's important to account for population stratification in my analysis. I'm using egscore (the eigenstrat method) for the association (and I've tried using the polygen and grammar packages too). The problem is that I cannot get lambda (the inflation factor) to go below the accepted inflation threshold of 1.1, it seems to converge around 1.5-1.6 when I include an increasing number of PC axes into the analysis. Then, I also tried to use the polygen and grammar packages, but the same thing happens. Here is the code that I am using: library(GenABEL) #Load the data as a gwaa.class my.geno.data <- load.gwaa.data(phenofile "pheno.dat",genofile="youroutput.dat") #Calculate the IBS matrix kin<-ibs(my.geno.data, weight="freq", snpfreq=NULL) diag(kin) <-hom(my.geno.data)$Var # Estimation of polygenic model, This estimates the residuals when the effect of covariates breeds are factored out. polygen <-polygenic(mortality~breed,kin=kin,my.geno.data) grammarobject <-qtscore(polygen$pgres,data=my.geno.data,clam=FALSE) ....so then you found that the lambda was still > 1.1 #So then I used egscore on the output from polygen output <-egscore(polygen$pgresidualY,data=my.geno.data,kin=kin,naxes=X) ....where I iteratively included an increasing number of PC axes (naxes=X), lambda still > 1.1, and doesn't change if I run egscore on the raw data instead of the environmental residuals (again iterating through axes), and it also doesn't change regardless of if I include breed as a co-variate, or as a stratification variable. output <-egscore(mortality~breed,data=my.geno.data,kin=kin,naxes=X) Am I doing something wrong? What else can I try to properly account for population stratification in an association between trait and SNPs? Thanks [[alternative HTML version deleted]]