##Before you start, please make sure you copied all your GWAS_clean4 plink files from yesterday's exercises into your current working directory

########

plink --file GWAS_clean4 --genome --mds-plot 10


#### in R


mydata = read.table("mds_components.txt", header=T)


mydata$pch[mydata$Group==1 ] <-15
mydata$pch[mydata$Group==2 ] <-16
mydata$pch[mydata$Group==3 ] <-2

jpeg(mds.jpeg, height=1000, width=1000)
plot(mydata$C1, mydata$C2 ,pch=mydata$pch)
dev.off()



########

plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --logistic --adjust --out unadj

plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.mds --covar-name C1 --logistic --adjust --out C1

plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.mds --covar-name C1-C2 --logistic --adjust --out C1-C2

####in R

broadqq<-function(pvals, title)
{
	observed<-sort(pvals)
	lobs<--(log10(observed))

	expected<-c(1:length(observed))
	lexp<- -log10(expected/(length(expected)+1))

	plot(c(0,7), c(0,7), col="red", lwd=3, type="l", xlab="Expected(-logP)", ylab="Observed(-logP)",xlim=c(0,max(lobs)), ylim=c(0,max(lobs)), las=1, xaxs="i", yaxs="i", bty="l", main=title)
	points(lexp, lobs, pch=23, cex=0.4, bg="black")}


jpeg("qqplot_compare.jpeg", height=1000, width=1000)
par(mfrow=c(2,1))
aff_unadj<-read.table("unadj.assoc.logistic", header=TRUE)
aff_unadj.add.p<-aff_unadj[aff_unadj$TEST==c("ADD"),]$P
broadqq(aff_unadj.add.p,"Some Trait Unadjusted")
aff_C1C2<-read.table("C1-C2.assoc.logistic", header=TRUE)
aff_C1C2.add.p<-aff_C1C2[aff_C1C2$TEST==c("ADD"),]$P
broadqq(aff_C1C2.add.p, "Some Trait Adjusted")
dev.off()



gws_unadjusted=aff_unadj[which(aff_unadj$P<0.0000001),]
gws_unadjusted

gws_adjusted=aff_C1C2[which(aff_C1C2$P<0.0000001),]
gws_adjusted

q()