Introduction
sim1000G allows for easy simulation of unrelated individuals starting from sequencing 1000 genomes varians.
The following, somewhat complicated example, showcases the following:
- how to extract regions from 1000 genomes
- simulate individuals from the CEU+TSI+GBR individuals.
- simulate individuals from the ASW individuals.
- combinte the two simulated data into one dataset.
- perform SKAT test for the region and examine the effects of population stratification
The original 1000 genomes VCF files are obtained from 1000 genomes ftp site, at the location:
http://hgdownload.cse.ucsc.edu/gbdb/hg19/1000Genomes/phase3/
Generating IDs of individuals to extract
sink(tempfile())
ped_file_1000genomes = system.file("examples", "20130606_g1k.ped", package = "sim1000G")
ped = read.table(ped_file_1000genomes,h=T,as=T,sep="\t")
pop1 = c("CEU","TSI","GBR")
id1 = ped$Individual.ID [ ped$Population %in% pop1 ]
id2 = ped$Individual.ID [ ped$Population == "ASW" ]
pop_map = ped$Population
names(pop_map) = ped$Individual.ID
write_sample_files = 0
if(write_sample_files == 1) {
cat(c(id1,id2),file="/tmp/samples1.txt",sep="\n")
# cat(c(id2),file="/tmp/samples2.txt",sep="\n")
}Extracting variant sets using bcftools
We extract the CEU,TSI,GBR and ASW samples from a region of chromosome 4 from 73MBp to 74MBp using bcftools. The following command are run in the shell:
#77356278-77703432
INPUT_VCF=ALL.chr4.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz
bcftools view -S /tmp/samples1.txt -r 4:73000000-74000000 --force-samples $INPUT_VCF > /tmp/chr4-80.vcf
bcftools filter -i 'AF>0 && EUR_AF>0 && AFR_AF>0' < /tmp/chr4-80.vcf | gzip > /tmp/chr4-80-filt.vcf.gzReading the vcf file
library(sim1000G)
vcf_file = "/tmp/chr4-80-filt.vcf.gz"
if(1) {
examples_dir = system.file("examples", package = "sim1000G")
vcf_file = file.path(examples_dir, "region-chr4-93-TMEM156.vcf.gz" )
}
vcf = readVCF(vcf_file, maxNumberOfVariants = 200 , min_maf = 0.02, max_maf = 0.32)
table( pop_map[ vcf$individual_ids ])
C = cor(t( vcf$gt1+vcf$gt2))^2
gplots::heatmap.2(C,col=rev( heat.colors(100) ) ,Rowv=F,Colv=F,trace="none",breaks=seq(0,1,l=101))
#gplots::heatmap.2(cor(t(vcf2$gt1))^2,col=rev( heat.colors(100) ) ,Rowv=F,Colv=F,trace="none")
if(0) {
ids = vcf$individual_ids
id_pop1 = which(ids %in% id1)
id_pop2 = which(ids %in% id2)
gplots::heatmap.2(cor(t( vcf$gt1[,id_pop1]+vcf$gt2[,id_pop1]))^2,col=rev( heat.colors(100) ) ,Rowv=F,Colv=F,trace="none",breaks=seq(0,1,l=101))
gplots::heatmap.2(cor(t( vcf$gt1[,id_pop2]+vcf$gt2[,id_pop2]))^2,col=rev( heat.colors(100) ) ,Rowv=F,Colv=F,trace="none",breaks=seq(0,1,l=101))
}Combining the two datasets and running SKAT
library(SKAT)
loadSimulation("pop1")
plot(apply(genotypes,2,mean), apply(genotypes2,2,mean))
gt = rbind(genotypes,genotypes2)
#gt = genotypes
dim(gt)
maf = apply(gt,2,mean,na.rm=T)/2
apply(gt,2,function(x) sum(is.na(x)))
flip = which(maf > 0.5) ; gt[,flip] = 2 - gt[,flip]
#gt = genotypes
dim(gt)
maf = apply(gt,2,mean,na.rm=T)/2
plot(maf)
sum(maf==0)
apply(gt,2,function(x) sum(is.na(x)))
flip = which(maf > 0.5)
gt[,flip] = 2 - gt[,flip]
dim(gt)
effect_sizes = rep(0, ncol(gt))
nvar = length(effect_sizes)
s = sample(1:nvar, 33)
effect_sizes[s] = 5
apply(gt[,s],1,sum)
predictor2 = function(b, geno) {
x = b[1]
for(i in 1:ncol(geno)) { x = x + b[i+1] * ( geno[,i] > 0) + b[i+1] * ( geno[,i] > 1) }
exp(x) / (1+exp(x) )
}
p =predictor2 ( c(-1.5,effect_sizes) , gt)
phenotype = rbinom( length(p) , 1 , p )
#phenotype = sample(phenotype)
obj<-SKAT_Null_Model(phenotype ~ 1, out_type="D")
library(SKAT)
SKATBinary((gt),obj)$p.valueGenetic maps in sim1000G
Through the functions readGeneticMap and downloadGeneticMap, we provide the functionality to automatically download genetic maps for GRCH37 build of the human genome.