AdvGeneMap2018Commands

Functional Annotation

table_annovar.pl
table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Gene.vcf -remove -nastring . -protocol refGene -operation g -vcfinput
cat APOC3_Gene.vcf.hg19_multianno.txt
table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Gene.vcf -remove -nastring . -protocol refGene,knownGene,ensGene -operation g,g,g -arg '-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing' -vcfinput
awk -F'\t' '{print $1,$2,$6,$7,$8,$9,$10}' APOC3_Gene.vcf.hg19_multianno.txt
table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Region.vcf -remove -nastring . -protocol phastConsElements46way -operation r -vcfinput
table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Region.vcf -remove -nastring . -protocol gwasCatalog -operation r -vcfinput
table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_Filter.vcf -remove -nastring . -protocol gnomad_genome,gnomad_exome,popfreq_max_20150413,gme,avsnp150,dbnsfp33a,dbscsnv11,cadd13gt20,clinvar_20170905,gwava -operation f,f,f,f,f,f,f,f,f,f -vcfinput
awk -F'\t' '{print $1,$2,$103,$104}' APOC3_Filter.vcf.hg19_multianno.txt
awk -F'\t' '{print $1,$2,$6,$14}' APOC3_Filter.vcf.hg19_multianno.txt
awk -F'\t' '{print $1,$2,$15,$16,$17,$18,$19,$20,$21,$22}' APOC3_Filter.vcf.hg19_multianno.txt
awk -F'\t' '{print $1,$2,$36,$86,$70}' APOC3_Filter.vcf.hg19_multianno.txt
awk -F'\t' '{print $1,$2,$99,$100}' APOC3_Filter.vcf.hg19_multianno.txt
table_annovar.pl APOC3.vcf humandb/ -buildver hg19 -out APOC3_ANN.vcf -remove -nastring . -protocol refGene,knownGene,ensGene,wgRna,targetScanS,phastConsElements46way,tfbsConsSites,gwasCatalog,gnomad_genome,gnomad_exome,popfreq_max_20150413,gme,avsnp150,dbnsfp33a,dbscsnv11,cadd13gt20,clinvar_20170905,gwava -operation g,g,g,r,r,r,r,r,f,f,f,f,f,f,f,f,f,f -arg '-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing','-splicing 12 -exonicsplicing',,,,,,,,,,,,,,, -vcfinput

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]

GxG Interaction

./plink --noweb --ped simcasecon.ped --map simcasecon.map --assoc
./plink --noweb --ped simcasecon.ped --map simcasecon.map --fast-epistasis
./plink --noweb --ped simcasecon.ped --map simcasecon.map --fast-epistasis --caseonly
./plink --noweb --ped simcasecon.ped --map simcasecon.map --epistasis
./plink --noweb --ped simcasecon.ped --map simcasecon.map --recodeA --out recoded
./plink --noweb --ped simcasecon.ped --map simcasecon.map --make-bed --out cassiformat
R
# The following commands are in the R environment
je <-read.table("cassi.out", header=T)
je
library(ORMDR)
recoded<-read.table("recoded.raw", header=T)
head(recoded)
newdata<-recoded[7:106]
ormdrdata<-cbind(newdata,recoded$PHENOTYPE-1)
names(ormdrdata)[101]<-"casestatus"
head(ormdrdata)
mdr1<-mdr.c(ormdrdata, colresp=101, cs=1, combi=1, cv.fold = 10)
mdr1$min.comb
mdr2<-mdr.c(ormdrdata, colresp=101, cs=1, combi=2, cv.fold = 10)
mdr2$min.comb
mdr3<-mdr.c(ormdrdata, colresp=101, cs=1, combi=3, cv.fold = 10)
mdr3$min.comb
mdr1$test.erate
mdr2$test.erate
mdr3$test.erate
mdr1mean<-mean(mdr1$test.erate)
mdr2mean<-mean(mdr2$test.erate)
mdr3mean<-mean(mdr3$test.erate)
mdr1mean
mdr2mean
mdr3mean
mdr2$best.combi
mdr2$min.comb
mdr3$best.combi
mdr3$min.comb
logreg12<-glm(casestatus ~ factor(snp1_2)*factor(snp2_1), family=binomial,
data=ormdrdata)
summary(logreg12)
anova(logreg12)
pchisq(701.68,4,lower.tail=F)
pchisq(703.82,8,lower.tail=F)
logreg345<-glm(casestatus ~ factor(snp3_2)*factor(snp4_2)*factor(snp5_2),
family=binomial, data=ormdrdata)
summary(logreg345)
anova(logreg345)
pchisq(45.6,8,lower.tail=F)
q()

### The following commands are in the linux shell
./BEAM3 beam3data.txt -o beam3results
./BEAM3 beam3data.txt -o beam3results -T 10

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

RV-TDT

### Variant Annotation
vtools init rvtdt
vtools import --format vcf data/data.vcf --build hg19
vtools phenotype --from_file data/phen.txt
vtools execute ANNOVAR geneanno
vtools select variant "variant.region_type like '%splicing%'or variant.mut_type like 'nonsynonymous%' or variant.mut_type like 'frameshift%' or variant.mut_type like 'stop%'" -t func_variant
vtools export func_variant --format tped --samples 'phenotype is not null' > vat_raw.tped
# set marker name as chr_pos, needs to avoid duplicate name
sort -k4 -n vat_raw.tped | awk 'BEGIN{OFS="\t";prev="None";copy=1} {$2=$1"_"$4; $3=0; if($2==prev) {$2=$2"_"copy; copy=copy+1} else {prev=$2; copy=1}; print $0}' > vat_export.tped
vtools phenotype --out family sample_name pid mid sex phenotype > vat_export.tfam
vtools use refGene-hg19_20130904
vtools update func_variant --set 'maf=0.001' # set the maf to be 0.001
vtools select func_variant -o chr pos refGene.name2 maf --header > vat_export.anno

### Phasing Trio
plink --noweb --tfile vat_export --recode12 --me 1 1 --set-me-missing --out "recode12_noME"
sort -n -k1 -k6 -k2 recode12_noME.ped | sed 's/ /\t/g' | cut -f1,3,4,5 --complement > linkage.ped cut -f2 recode12_noME.map | awk 'BEGIN{OFS="\t";} {print "M",$0}' | sed '1i\I\tid\nA\tDisease' > linkage.dat
java -Xmx10000m -jar java/linkage2beagle.jar linkage.dat linkage.ped > pre_beagle.bgl
python script/pre_phase.py -i pre_beagle.bgl -a pre_beagle_withMissing.bgl
java -Xmx10000m -jar java/beagle.jar missing=0 trios=pre_beagle.bgl out=bgl_phased verbose=false redundant=true
gunzip bgl_phased.pre_beagle.bgl.phased.gz

### RV-TDT Analysis
python script/post_phase.py -a vat_export.anno -b bgl_phased.pre_beagle.bgl.phased -o genes/
for g in `ls genes | grep tped | cut -d"." -f1 | head -20`
do
    echo "running rvTDT on gene "${g}
    rvTDT exercise_proj -G ./genes/${g}.tped -P ./data/rvtdt.phen -M ./genes/${g}.map --adapt 500 --alpha 0.00001 --permut 2000 --lower_cutoff 0 --upper_cutoff 100 --minVariants 3 --maxMissRatio 1 done
done

Seqspark

hdfs dfs -put demo.vcf.bz2
hdfs dfs -put demo.tsv
seqspark annotation.conf
seqspark qc.conf
seqspark demo.conf

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