AdvGeneMap2018Commands
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GenABEL
# Load files
library(GenABEL)
convert.snp.tped(tped = "gwa_gabel_qtl.tped", tfam = "gwa_gabel_qtl.tfam", out = "gwa_gabel_qtl.raw", strand = "u")
g.dat <- load.gwaa.data(phen = "gwa_gabel_qtl.praw", gen = "gwa_gabel_qtl.raw", force = T)
slotNames(g.dat)
slotNames(g.dat@gtdata)
colnames(g.dat@phdata)
# sample size
sample.size <- g.dat@gtdata@nids
# number of SNPs
snps.total <- g.dat@gtdata@nsnps
print(c(sample.size, snps.total))
# Trait
summary(g.dat@phdata$disease)
hist(g.dat@phdata$disease, main="Quantitative Phenotype data summary", xlab = "Systolic pressure measure", freq = F,breaks=20, col="gray")
rug(g.dat@phdata$disease)
###
# tests for association
###
# GLM test
test.snp <- scan.glm('disease ~ CRSNP', family = gaussian(), data = g.dat)
names(test.snp)
alpha <- 5e-8
test.snp$snpnames[test.snp$P1df < alpha]
test.snp$P1df[test.snp$P1df < alpha]
# Score test
test.qt <- qtscore(disease, data = g.dat, trait = "gaussian")
slotNames(test.qt)
names(test.qt@results)
test.qt@lambda
descriptives.scan(test.qt)
rownames(results(test.qt))[results(test.qt)$P1df < alpha]
results(test.qt)$P1df[results(test.qt)$P1df < alpha]
results(test.qt)$Pc1df[results(test.qt)$Pc1df < alpha]
# QQ plot
obs <- sort(results(test.qt)$P1df)
ept <- c(1:length(obs)) / (length(obs) + 1)
plot(-log10(ept), -log10(obs), main = "GWAS QQ plot, qtl", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)")
abline(0, 1, col = "red")
abline(h = 8, lty = 2)
# Manhattan plot
plot(test.qt, col = "black")
# Adding confounders
test.qt.sex <- qtscore(disease ~ sex, data = g.dat, trait = "gaussian")
rownames(results(test.qt.sex))[results(test.qt)$P1df < alpha]
summary(lm(disease ~ sex, data = g.dat))
###
# MDS
###
gkin <- ibs(g.dat, weight = "freq")
gkin[1:10,1:10]
cps.full <- cmdscale(as.dist(.5 - gkin), eig = T, k = 10)
names(cps.full)
cps <- cps.full$points
plot(cps[,1], cps[,2], pch = g.dat@phdata$popn)
legend(-0.16, 0.06, c("TSI","MEX", "CEU"), pch = c(1,2,3))
###
# Corrected test
###
# Incorporating PCs as predictors
colnames(cps)<-c('C1','C2','C3','C4','C5','C6','C7','C8','C9','C10')
gpc.dat <- g.dat
gpc.dat@phdata<-cbind(g.dat@phdata, cps)
test.pc.a <- scan.glm('disease ~ CRSNP + C1 + C2 + C3 + C4 + C5', family=gaussian(), data = gpc.dat)
test.pc.a$snpnames[test.pc.a$P1df < alpha]
test.pc.a$P1df[test.pc.a$P1df < alpha]
test.pc.b <- qtscore(disease ~ C1 + C2 + C3 + C4 + C5, data = gpc.dat, trait = "gaussian")
test.pc.b@lambda
# scree plot
plot(cps.full$eig[1:10]/sum(cps.full$eig), axes = F, type = "b", xlab = "Components", ylim = c(0,0.05), ylab = "Proportion of Variations", main = "MDS analysis scree plot")
axis(1, 1:10)
axis(2)
# cumulative plot
plot(cumsum(cps.full$eig[1:10])/sum(cps.full$eig), axes = F, type = "b", ylim = c(0,0.2), xlab = "Components", ylab = "Proportion of Variations", main = "MDS analysis cumulative plot")
axis(1, 1:10)
axis(2)
# Genomic control
# Uncorrected GIF
test.qt@lambda
# Corrected p-value
row.names(results(test.qt))[results(test.qt)$Pc1df < alpha]
results(test.qt)$Pc1df[results(test.qt)$Pc1df < alpha]
# Check for inflation of statistic
obs <- sort(results(test.qt)$chi2.1df)
ept <- sort(qchisq(1:length(obs) / (length(obs) + 1), df = 1))
plot(ept, obs, main = "Genomic control (slope is the inflation factor)", xlab="Expected chisq, 1df", ylab="Observed chisq, 1df")
abline(0, 1, col = "red")
abline(0, test.qt@lambda[1], lty = 2)
# Definition of GIF
# Conventional definition
median(results(test.qt)$chi2.1df)/0.456
# GenABEL definition
lm(obs~ept)$coef[2]
# QQ plot
obs <- sort(results(test.qt)$Pc1df)
ept <- c(1:length(obs)) / (length(obs) + 1)
plot(-log10(ept), -log10(obs), main = "GWAS QQ plot adj. via Genomic Control", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)")
abline(0, 1, col = "red")
abline(h = 8, lty = 2)
# EIGENSTRAT
adj.gkin = gkin
diag(adj.gkin) = hom(g.dat)$Var
# naxes = 3 is default value
test.eg <- egscore(disease, data = g.dat, kin = adj.gkin, naxes = 2)
descriptives.scan(test.eg)
snp.eg <- row.names(results(test.eg))[results(test.eg)$P1df < alpha]
pvalue.eg <- results(test.eg)$P1df[results(test.eg)$P1df < alpha]
lambda.eg <- test.eg@lambda
snp.eg
pvalue.eg
lambda.eg
# Change #PCs
for (k in 1:10){
test.tmp <- egscore(disease, data = g.dat, kin = adj.gkin, naxes = k)
print(test.tmp@lambda$estimate)
}
# QQ plot
obs <- sort(results(test.eg)$Pc1df)
ept <- c(1:length(obs)) / (length(obs) + 1)
qqplot(-log10(ept), -log10(obs), main = "GWAS QQ plot adj. w/ EIGENSTRAT", xlab="Expected -log10(pvalue)", ylab="Observed -log10(pvalue)")
abline(0, 1, col = "red")
abline(h = 8, lty = 2)
# Manhattan plot comparison
plot(test.qt, col = "black")
add.plot(test.eg, col = "gray", pch = 3)
legend("topright", c("Original plot","After correction w/ EIGENSTRAT"), pch = c(1,3))
###
# Basic test, binary trait
###
# load files to GenABEL
convert.snp.tped(tped = "gwa_gabel.tped", tfam = "gwa_gabel.tfam", out = "gwa_gabel.raw", strand = "u")
b.dat <- load.gwaa.data(phen = "gwa_gabel.praw", gen = "gwa_gabel.raw", force = T)
slotNames(b.dat)
slotNames(b.dat@gtdata)
colnames(b.dat@phdata)
# sample size
b.dat@gtdata@nids
# number of cases and controls
case.size <- length(which(b.dat@phdata$disease == 1))
control.size <- length(which(b.dat@phdata$disease == 0))
case.size
control.size
# number of SNPs
snpsb.total <- b.dat@gtdata@nsnps
# GLM test
testb.snp <- scan.glm('disease ~ CRSNP', family = binomial(), data = b.dat)
names(testb.snp)
alpha <- 5e-8
testb.snp$snpnames[testb.snp$P1df < alpha]
testb.snp$P1df[testb.snp$P1df < alpha]
# Score test
testb.qt <- qtscore(disease, data = b.dat, trait = "binomial")
slotNames(testb.qt)
descriptives.scan(testb.qt)
row.names(results(testb.qt))[results(testb.qt)$P1df < alpha]
results(testb.qt)$P1df[results(testb.qt)$P1df < alpha]
results(testb.qt)$Pc1df[results(testb.qt)$Pc1df < alpha]
Plink - Part 1 - Data QC
plink --file GWAS
plink --file GWAS --mind 0.10 --recode --out GWAS_clean_mind
plink --file GWAS_clean_mind --maf 0.05 --recode --out MAF_greater_5
plink --file GWAS_clean_mind --exclude MAF_greater_5.map --recode --out MAF_less_5
plink --file MAF_greater_5 --geno 0.05 --recode --out MAF_greater_5_clean
plink --file MAF_less_5 --geno 0.01 --recode --out MAF_less_5_clean
plink --file MAF_greater_5_clean --merge MAF_less_5_clean.ped MAF_less_5_clean.map --recode --out GWAS_MAF_clean
plink --file GWAS_MAF_clean --mind 0.03 --recode --out GWAS_clean2
plink --file GWAS_clean2 --check-sex --out GWAS_sex_checking
#### in R - open R by simply typing R
setwd("to_your_working_directory/")
sexcheck = read.table("GWAS_sex_checking.sexcheck", header=T)
names(sexcheck)
sex_problem = sexcheck[which(sexcheck$STATUS=="PROBLEM"),]
sex_problem
q()
##################################
plink --file GWAS_clean2 --genome --out duplicates
#### in R
setwd("to_your_working_directory/")
dups = read.table("duplicates.genome", header = T)
problem_pairs = dups[which(dups$PI_HAT > 0.4),]
problem_pairs
problem_pairs = dups[which(dups$PI_HAT > 0.05),]
myvars = c("FID1", "IID1", "FID2", "IID2", "PI_HAT")
problem_pairs[myvars]
q()
######
plink --file GWAS_clean2 --remove IBS_excluded.txt --recode --out GWAS_clean3
plink --file GWAS_clean3 --het
###### in R
Dataset <- read.table("plink.het", header=TRUE, sep="", na.strings="NA", dec=".", strip.white=TRUE)
mean(Dataset$F)
sd(Dataset$F)
jpeg("hist.jpeg", height=1000, width=1000)
hist(scale(Dataset$F), xlim=c(-4,4))
dev.off()
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$P < 0.0000009),]
hwe_prob
q()
##########
plink --file GWAS_clean3 --exclude HWE_out.txt --recode --out GWAS_clean4
Plink - Part 2 - Controlling for Substructure
plink --file GWAS_clean4 --genome --cluster --mds-plot 10
#### in R
mydata = read.table("mds_components.txt", header=T)
mydata$pch[mydata$Group==1 ] <-15
mydata$pch[mydata$Group==2 ] <-16
mydata$pch[mydata$Group==3 ] <-2
jpeg("mds.jpeg", height=500, width=500)
plot(mydata$C1, mydata$C2 ,pch=mydata$pch)
dev.off()
q()
######
plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --logistic --adjust --out unadj
plink --file GWAS_clean4 --genome --cluster --pca 10 header
plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.eigenvec --covar-name PC1 --logistic --adjust --out PC1
plink --file GWAS_clean4 --pheno pheno.txt --pheno-name Aff --covar plink.eigenvec --covar-name PC1-PC2 --logistic --adjust --out PC1-PC2
#### 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") }
jpeg("qqplot_compare.jpeg", height=1000, width=500)
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("PC1-PC2.assoc.logistic", header=TRUE)
aff_C1C2.add.p<-aff_C1C2[aff_C1C2$TEST==c("ADD"),]$P
broadqq(aff_C1C2.add.p, "Some Trait Adjusted for PC1 and PC2")
dev.off()
gws_unadj = aff_unadj[which(aff_unadj$P < 0.0000001),]
gws_unadj
gws_adjusted = aff_C1C2[which(aff_C1C2$P < 0.0000001),]
gws_adjusted
VAT
vtools -h vtools init VATDemo vtools import *.vcf.gz --var_info DP filter --geno_info DP_geno --build hg18 -j1 vtools liftover hg19 head phenotypes.csv vtools phenotype --from_file phenotypes.csv --delimiter "," vtools show project vtools show tables vtools show table variant vtools show samples vtools show genotypes vtools show fields vtools select variant --count vtools show genotypes > GenotypeSummary.txt head GenotypeSummary.txt vtools output variant "max(DP)" "min(DP)" "avg(DP)" "stdev(DP)" "lower_quartile(DP)" "upper_quartile(DP)" --header vtools select variant "filter=’PASS’" --count vtools select variant "filter=’PASS’" -o "max(DP)" "min(DP)" "avg(DP)" "stdev(DP)" "lower_quartile(DP)" "upper_quartile(DP)" --header vtools update variant --from_stat ’total=#(GT)’ ’num=#(alt)’ ’het=#(het)’ ’hom=#(hom)’ ’other=#(other)’ ’minDP=min(DP_geno)’ ’maxDP=max(DP_geno)’ ’meanDP=avg(DP_geno)’ ’maf=maf()’ vtools show fields vtools show table variant vtools update variant --from_stat ’totalGD10=#(GT)’ ’numGD10=#(alt)’ ’hetGD10=#(het)’ ’homGD10=#(hom)’ ’otherGD10=#(other)’ ’mafGD10=maf()’ --genotypes "DP_geno > 10" vtools show fields vtools show table variant vtools output variant chr pos maf mafGD10 --header --limit 20 vtools phenotype --set "RACE=0" --samples "filename like ’YRI%’" vtools phenotype --set "RACE=1" --samples "filename like ’CEU%’" vtools show samples --limit 10 vtools update variant --from_stat ’CEU_mafGD10=maf()’ --genotypes ’DP_geno>10’ --samples "RACE=1" vtools update variant --from_stat ’YRI_mafGD10=maf()’ --genotypes ’DP_geno>10’ --samples "RACE=0" vtools output variant chr pos mafGD10 CEU_mafGD10 YRI_mafGD10 --header --limit 10 vtools phenotype --from_stat ’CEU_totalGD10=#(GT)’ ’CEU_numGD10=#(alt)’ --genotypes ’DP_geno>10’ --samples "RACE=1" vtools phenotype --from_stat ’YRI_totalGD10=#(GT)’ ’YRI_numGD10=#(alt)’ --genotypes ’DP_geno>10’ --samples "RACE=0" vtools phenotype --output sample_nameCEU_totalGD10CEU_numGD10YRI_totalGD10YRI_numGD10 --header vtools execute ANNOVAR geneanno vtools output variant chr pos ref alt mut_type --limit 20 --header vtools_report trans_ratio variant -n num vtools_report trans_ratio variant -n numGD10 vtools select variant "DP<15" -t to_remove vtools show tables vtools remove variants to_remove -v0 vtools show tables vtools remove genotypes "DP_geno<10" -v0 vtools select variant "mut_type like ’non%’ or mut_type like ’stop%’ or region_type=’splicing’" -t v_funct vtools show tables vtools show samples --limit 5 vtools select variant --samples "RACE=1" -t CEU mkdir -p ceu cd ceu vtools init ceu --parent ../ --variants CEU --samples "RACE=1" --build hg19 vtools show project vtools select variant "CEU_mafGD10>=0.05" -t common_ceu vtools select v_funct "CEU_mafGD10<0.01" -t rare_ceu vtools use refGene vtools show annotation refGene vtools associate -h vtools show tests vtools show test LinRegBurden vtools associate common_ceu BMI --covariate SEX -m "LinRegBurden --alternative 2" -j1 --to_db EA_CV > EA_CV.asso.res grep -i error *.log less EA_CV.asso.res sort -g -k7 EA_CV.asso.res | head vtools show fields vtools associate rare_ceu BMI --covariate SEX -m "LinRegBurden --alternative 2" -g refGene.name2 -j1 --to_db EA_RV > EA_RV.asso.res grep -i error *.log | tail -10 less EA_RV.asso.res sort -g -k6 EA_RV.asso.res | head vtools associate rare_ceu BMI --covariate SEX -m "VariableThresholdsQt --alternative 2 -p 100000 --adaptive 0.0005" -g refGene.name2 -j1 --to_db EA_RV > EA_RV_VT.asso.res grep -i error *.log | tail -10 less EA_RV_VT.asso.res sort -g -k6 EA_RV_VT.asso.res | head vtools select rare_ceu "refGene.name2=’ABCC1’" -o chr pos ref alt CEU_mafGD10 numGD10 mut_type --header cd .. vtools select variant --samples "RACE=0" -t YRI mkdir -p yri cd yri vtools init yri --parent ../ --variants YRI --samples "RACE=0" --build hg19 vtools select variant "YRI_mafGD10>=0.05" -t common_yri vtools select v_funct "YRI_mafGD10<0.01" -t rare_yri vtools use refGene vtools associate common_yri BMI --covariate SEX -m "LinRegBurden --alternative 2" -j1 --to_db YA_CV > YA_CV.asso.res vtools associate rare_yri BMI --covariate SEX -m "LinRegBurden --alternative 2" -g refGene.name2 -j1 --to_db YA_RV > YA_RV.asso.res vtools associate rare_yri BMI --covariate SEX -m "VariableThresholdsQt --alternative 2 -p 100000 --adaptive 0.0005" -g refGene.name2 -j1 --to_db YA_RV > YA_RV_VT.asso.res cd .. vtools_report meta_analysis ceu/EA_RV_VT.asso.res yri/YA_RV_VT.asso.res --beta 5 --pval 6 --se 7 -n 2 --link 1 > ME\ TA_RV_VT.asso.res cut -f1,3 META_RV_VT.asso.res | head
