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gwas.r
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gwas.r
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##############################################################################################################################################
### AMM -- R script for GWAS corecting for population structure (similar to EMMAX and P3D)
###
#######
#
##
##
##
#
##REQUIRED DATA & FORMAT
#library(doMC) # only required for multi-core
#library(gaston) # only required if AI algorithm is used
#requires functions from the original emma function (Kang et al. 2008, Genetics) to calculate the exact p-value if update.top_snps=TRUE or if AI=FALSE
# source ('emma.r') needs to be sourced before.
#PHENOTYPE - Y: a n by m matrix, where n=number of individuals and the rownames(Y) contains the individual names
#GENOTYPE - X: a n by m matrix, where n=number of individuals, m=number of SNPs, with rownames(X)=individual names, and colnames(X)=SNP names
#KINSHIP - K: a n by n matrix, with rownames(K)=colnames(K)=individual names, you can calculate K as IBS matrix using the emma package K<-emma.kinship(t(X))
# or use the vanRaden Kinship matrix from the function G.matrix in the R-package snpReady
#each of these data being sorted in the same way, according to the individual name
#
#
#SNP INFORMATION - SNP_INFO: a data frame having at least 3 columns:
# - 1 named 'SNP', with SNP names (same as colnames(X)),
# - 1 named 'Chr', with the chromosome number to which belong each SNP
# - 1 named 'Pos', with the position of the SNP onto the chromosome it belongs to.
#######
###WARNINGS####
# mc enabled to use on linux
amm_gwas<-function(Y,X,K,p=0.001,m=2,run=TRUE,AI=FALSE,calculate.effect.size=FALSE,include.lm=FALSE,include.kw=FALSE,use.SNP_INFO=FALSE,update.top_snps=FALSE,report=TRUE,plot_h=FALSE, mc = FALSE, cores ='all') {
stopifnot(is.numeric(Y[,1]))
Y_ <- Y[order(Y[,1]),]
Y <- as.matrix(Y_[,m])
rownames(Y) <- as.integer(Y_[,1])
Y <- na.omit(Y)
XX <- X[rownames(X) %in% rownames(Y),]
XX<-XX[order(as.numeric(rownames(XX))),]
if(report == TRUE) {
cat ('GWAS performed on', length(which(rownames(Y)%in%rownames(X))),'ecotypes, ', nrow(Y)-length(which(rownames(Y)%in%rownames(X))),'values excluded','\n')
}
if(use.SNP_INFO == FALSE) {
options(stringsAsFactors = FALSE)
if(report == TRUE) {
cat('SNP_INFO file created','\n')
}
SNP_INFO<-data.frame(cbind(colnames(X),matrix(nrow=ncol(X),ncol=2,data=unlist(strsplit(colnames(X),split='- ')),byrow=TRUE)))
colnames(SNP_INFO) <-c('SNP','Chr','Pos')
SNP_INFO[,2]<-as.numeric(SNP_INFO[,2])
SNP_INFO[,3]<-as.numeric(SNP_INFO[,3])
} else {
if(report == TRUE) { cat('User definied SNP_INFO file is used','\n')}}
Y1 <- as.matrix(Y[rownames(Y) %in% rownames(XX),])
colnames(Y1) <- colnames(Y)
rownames(Y1) <- rownames(Y)[rownames(Y)%in%rownames(XX)]
ecot_id <- as.integer(rownames(Y1))
K1 <- K[rownames(K) %in% ecot_id,]
K2 <- K1[,colnames(K1) %in% ecot_id]
K_ok <- as.matrix(K2)
K_ok<-K_ok[order(as.numeric(rownames(K_ok))),order(as.numeric(colnames(K_ok)))]
a <- rownames(K_ok)
n <- length(a)
K_stand <- (n-1)/sum((diag(n)-matrix(1,n,n)/n)*K_ok)*K_ok
Y <- Y1[which(rownames(Y1)%in%a),]
X_ <- XX[which(rownames(XX)%in%a),]
rm(X,XX)
gc()
AC_1 <- data.frame(colnames(X_),apply(X_,2,sum))
colnames(AC_1) <- c('SNP','AC_1')
MAF_1 <- data.frame(AC_1,AC_0=nrow(X_)-AC_1$AC_1)
MAF_2 <- data.frame(MAF_1,MAC=apply(MAF_1[,2:3],1,min))
MAF_3 <- data.frame(MAF_2,MAF=(MAF_2$MAC/nrow(X_)))
MAF_ok <- merge(SNP_INFO,MAF_3,by='SNP')
rm(AC_1,MAF_1,MAF_2,MAF_3)
#Filter for MAF
MAF <- subset(MAF_ok,MAF<p)[,1]
X_ok <- X_[,!colnames(X_) %in% MAF]
rm(MAF,X_)
# REML
if (AI==TRUE) {
require(gaston)
null <-lmm.aireml(Y,K=K_stand)
herit<-null$tau/(null$tau+null$sigma2)
M <- solve(chol(null$tau*K_stand+null$sigma2*diag(dim(K_stand)[1])))
H2 <- seq(0,1,length=101)
lik <- lmm.diago.likelihood(h2 = H2, Y = Y, eigenK = eigen(K_stand))
minh<-min(H2[which(exp(lik$likelihood-max(lik$likelihood))>0.9)])
maxh<-max(H2[which(exp(lik$likelihood-max(lik$likelihood))>0.9)])
# covH1<-matrix(nrow=2,ncol=2,data=c(null$vg,null$ve,null$ve,null$vg))
# se_h1<-deltamethod( ~ x1/(x1+x2),c(null$vg,null$ve),covH1)
if (report==TRUE) {
cat('pseudo-heritability estimate is',round(herit,digits=3),'with a 90% CI between',minh,'and',maxh,'.\n')
}
if(plot_h==TRUE) {
pdf(file='heritability_likelihood.pdf')
plot(H2, exp(lik$likelihood-max(lik$likelihood)), type="l", ylab="relative likelihood")
points(herit,exp(null$logL-max(lik$likelihood)),pch=16,col='red')
abline(h=0.90,lty=2,col='red')
cat('A heritability likelihood plot is generated','\n')
dev.off()
}
} else {
Xo<-rep(1,nrow(X_ok))
ex<-as.matrix(Xo)
null<-emma.REMLE(Y,ex,K_stand)
herit<-null$vg/(null$vg+null$ve)
M <- solve(chol(null$vg*K_stand+null$ve*diag(dim(K_stand)[1])))
if (report==TRUE) {
cat('pseudo-heritability estimate is',round(herit,digits=3),'\n')
}
}
if (run==FALSE&AI==TRUE) {
cat('no GWAS performed','\n')
return(c(herit,minh,maxh))
}
if (run==FALSE&AI==FALSE) {
return(herit)}
if (run==TRUE) {
Y_t <- crossprod(M,Y)
int_t <- crossprod(M,(rep(1,length(Y))))
if(calculate.effect.size == TRUE) {
models1 <- apply(X_ok,2,function(x){summary(lm(Y_t~0+int_t+crossprod(M,x)))$coeff[2,]})
bet <- models1[1,]
se <- models1[2,]
### variance explained from betas veb
# bet^2*var(X_ok[,t])/var(Y[,n]) = veb/(1-veb)
veb <- bet[1]^2*var(X_ok[,1])/var(Y)/(1+bet[1]^2*var(X_ok[,1])/var(Y))
for( t in 2:ncol(X_ok)) {
veb <- c(veb,bet[t]^2*var(X_ok[,t])/var(Y)/(1+bet[t]^2*var(X_ok[,t])/var(Y)))
}
# similar (but slightly different) to RSS method below or to adjusted R2 from lm call
out_models<-data.frame(SNP=colnames(models1),Pval=models1[4,],variance_explained=veb,beta=bet,se_beta=se)
} else {
#EMMAX SCAN
RSS_env <- rep(sum(lsfit(int_t,Y_t,intercept = FALSE)$residuals^2),ncol(X_ok))
if(mc == FALSE) {
R1_full <- apply(X_ok,2,function(x){sum(lsfit(cbind(int_t,crossprod(M,x)),Y_t,intercept = FALSE)$residuals^2)})
} else {
require(doMC)
if(cores == "all") {
registerDoMC(system("cat /proc/cpuinfo | grep processor | wc -l", intern = TRUE))
} else {
registerDoMC(cores)
}
R1_full <- foreach(i = 1:ncol(X_ok), .combine = "c") %dopar%
sum(lsfit(cbind(int_t,crossprod(M,X_ok[,i])),Y_t,intercept = FALSE)$residuals^2)
}
pa<-nrow(Y1)
F_1 <- ((RSS_env-R1_full)/1)/(R1_full/(pa-3))
pval_Y1 <- pf(F_1,1,(pa-2),lower.tail=FALSE)
snp <- colnames(X_ok)
out_models <- data.frame(SNP=snp,Pval=pval_Y1,variance_explained=1-R1_full/RSS_env)
}
output <- merge(MAF_ok,out_models,by='SNP')
if(include.lm == TRUE) {
RSS_env_ <- rep(sum(lsfit(rep(1,length(Y)),Y,intercept = FALSE)$residuals^2),ncol(X_ok))
R1_full_ <- apply(X_ok,2,function(x){sum(lsfit(x,Y,intercept = TRUE)$residuals^2)})
pa<-nrow(Y1)
F_1_ <- ((RSS_env_-R1_full_)/1)/(R1_full_/(pa-3))
pval_Y1_lm <- pf(F_1_,1,(pa-3),lower.tail=FALSE)
snp <- colnames(X_ok)
out_models_lm <- data.frame(SNP=snp,Pval_lm=pval_Y1_lm)
output <- merge(output,out_models_lm,by='SNP')
}
if(include.kw == TRUE) {
kw <- apply(X_ok,2,function(x){kruskal.test(Y,x)$p.value})
KW <- cbind(names(kw),kw)
colnames(KW) <- c('SNP','Pval_kw')
output <- merge(output,KW,by='SNP')
}
## update tp SNPs with correct model
if(update.top_snps == FALSE) {
return(output)
} else {
oi <- output[order(output[,8]),][1:update.top_snps,1]
xs <- t(X_ok[,colnames(X_ok)%in%oi])
auto <- emma.ML.LRT (Y, xs, K_ok)
up <- data.frame(SNP=rownames(xs),update=auto$ps[,1])
for(i in 1:update.top_snps) {
output[which(output[,1]==up[i,1]),]$Pval <- up[i,2]
}
return(output)
}
}
}