plink --file GWAS

plink --file GWAS --exclude plink.nof --recode --out GWAS_clean

plink -file GWAS_clean -mind 0.10 -recode -out GWAS_clean_mind

plink -file GWAS_clean_mind -maf 0.01 -mind 0.02 -geno 0.05 -recode -out GWAS_clean2

plink -file GWAS_clean2 -genome -out duplicates


#### in R

setwd("to_your_working_directory/")
dups = read.table(duplicates.genome, header = T)

problem_pairs = dups[which(dups$PI_HAT > 0.4),]
problem_pairs

problem_pairs = dups[which(dups$PI_HAT > 0.05),]
myvars = c("FID1", "IID1", "FID2", "IID2", "PI_HAT")
problem_pairs[myvars]

q()

##################################


plink --file GWAS_clean2 --remove IBS_excluded.txt --recode --out GWAS_clean3

plink -file GWAS_clean3 -exclude HWE_out.txt -check-sex -out GWAS_sex_checking

plink -file GWAS_clean3 -het



#### in R

setwd("to_your_working_directory/")

Dataset <- read.table("plink.het", header=TRUE, sep="", na.strings="NA", dec=".", strip.white=TRUE)


mean(Dataset$F)
sd(Dataset$F)

hist(scale(Dataset$F), xlim = c(-4 ,4))

q()

######

plink -file GWAS_clean3 -pheno pheno.txt -pheno-name Aff -hardy 

##### in R

hardy = read.table(plink.hwe, header = T)
names(hardy)
hwe_prob = hardy[which(hardy$P < 0.0000009),]
hwe_prob

q()
##########

plink -file GWAS_clean3 -exclude HWE_out.txt -recode -out GWAS_clean4

plink -file GWAS_clean4 -exclude HWE_out.txt -check-sex -out GWAS_sex_checking


######## in R

sexcheck = read.table("GWAS_sex_checking.sexcheck", header=T)
names(sexcheck)
sex_problem = sexcheck[which(sexcheck$STATUS=="PROBLEM"),]
sex_problem
q()
##############

plink -file GWAS_clean4 -genome -mds-plot 10

#### in R

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


mydata$color[mydata$Group==1 ] <-"red"
mydata$color[mydata$Group==2] <-"green"
mydata$color[mydata$Group ==3] <-"blue"

plot(mydata$C1, mydata$C2 ,col=mydata$color,pch=19)

q()

######

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=.4, bg="black") 
}

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 Unadjusted")

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

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



q()
###############