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mkdir genotype
wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr{1..22}.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz
# keep the 455 individuals who are reported in both the 1000 Genome Project and Geuvadis
module load vcftools
for i in `seq 1 22`
do
vcftools --gzvcf /genotype/phase3/ALL.chr$i.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz --keep 1000g.ind.sample.txt --recode --out /genotype/phase3/chr$i.1000gphase3.455
done
# exclude rare (less than 0.01 frequency) variants
vcftools --vcf /genotype/phase3/chr$i.1000gphase3.455.recode.vcf --maf 0.01 --max-maf 0.99 --recode --out /genotype/phase3/chr$i.1000gphase3.455.0.01
# keep only bi-allelic variants
vcftools --vcf /genotype/phase3/chr$i.1000gphase3.455.0.01.recode.vcf --min-alleles 2 --max-alleles 2 --recode --out /genotype/phase3/chr$i.1000gphase3.455.0.01.biallelic
Identify covariates (genetic and non-genetic)
# Convert vcf file to plink format
plink --vcf chr22.1000gphase3.455.0.01.recode.vcf --make-bed --out chr22.1000gphase3.455.0.01.recode.bed
# Run pca
plink --bfile chr22.1000gphase3.455.0.01.recode.bed --pca --out# Plot pca
library(ggplot2)
ggplot(data=pca, aes(V3,V4)) + geom_point()# Download the fastq dataset from the Geuvadis project
# sample information https://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/
# fastq files ftp://ftp.sra.ebi.ac.uk/vol1/fastq/
csvfile=getfastq.csv
for line in `cat ${csvfile} | grep -v ^#`
do
url=`echo ${line} | cut -d ',' -f 2`
wget ${url}
done
#FastQC - quality check
module load fastqc
fastqc *.fastq.gz -o /fastqc_out/
module load multiqc
multiqc . -f
#Kallisto - make index files - pseudomapping
module load kallisto
wget ftp://ftp.ensembl.org/pub/grch37/current/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh37.cdna.all.fa.gz
gunzip Homo_sapiens.GRCh37.cdna.all.fa.gz
kallisto index -i human.GRCh37.cdna.all.idx Homo_sapiens.GRCh37.cdna.all.fa
wget https://github.com/pachterlab/kallisto-transcriptome-indices/releases/download/ensembl-96/homo_sapiens.tar.gz
gunzip Homo_sapiens
# homo_sapiens/transcriptome.idx
# Gene expression quantification for all samples with Kallisto
module load kallisto
csvfile=1000g.sample.csv
for line in `cat ${csvfile} | grep -v ^#`
do
file=`echo ${line} | cut -d ',' -f 1`
kallisto quant -i human.GRCh37.cdna.all.idx -o kallisto/${file}.kallisto -n 100 -t 32 fastq2/${file}.1.fastq.gz fastq2/${file}.2.fastq.gz
donelibrary(tximport)
library(biomaRt)
update.packages()
install.packages("rlang")
## convert transcripts into genes
mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl", host="grch37.ensembl.org"))
df <- read.table("kallisto/HG00096.kallisto/abundance.tsv", header = TRUE,sep = "\t")
genes <- df$target_id
G_list <- getBM(filters= "ensembl_transcript_id_version", attributes=c('ensembl_transcript_id_version','ensembl_gene_id'),mart= mart,values=genes)
head(G_list)
write.csv(G_list, file = "transcripts.to.genes2.csv", append = FALSE, quote = FALSE, sep = ",",
eol = "\n", na = "NA", dec = ".", row.names = FALSE,
col.names = FALSE, qmethod = "double",
fileEncoding = "")
# summarize (gene expression) data from each individual
samples <-read.csv("1000g.EUR.sample.csv",header=TRUE)
files <- file.path(dir, "kallisto", samples$kallisto, "abundance.tsv")
names(files) <- paste0(samples$X)
all(file.exists(files))
txi <- tximport(files, type = "kallisto", tx2gene = tx2gene, txOut = FALSE, ignoreAfterBar = TRUE)
write.table(txi$est_counts,sep="\t", file = "EUR.counts.tsv")
write.table(txi$tpm,sep="\t", file = "EUR.TPM.tsv")library('biomaRt')
mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl", host="grch37.ensembl.org"))
df <- read.csv(file.choose(), header = T, sep = ",")
gene <- df$gene
G_list <- getBM(filters= "ensembl_gene_id", attributes=c('ensembl_gene_id', 'hgnc_symbol','description','chromosome_name','start_position','end_position'),mart= mart,values=gene)
G_list2<-merge(df,G_list,by.x="gene",by.y="ensembl_gene_id",all.x=T)
write.csv(G_list2, file = "EUR.gene.TPM.csv")
## only genes with CPM (counts per million)>0.5 in more than half of the total samples
## are kept for further analysis to avoid false positives in the eQTL
d1 <- read.table("EUR.counts.tsv", header = T,row.names=1,sep = "\t")
d1_scaled <- apply(d1, 2, function(x){x/sum(x)*1000000})
d1_cleaned<-d1_scaled[rowSums(d1_scaled > 0.5) > length(d1[1,])/2, ]
write.csv(d1_cleaned, file = "EUR.CPM.survived.csv")matall<-read.table(file = 'Yoruba.TPM.filtered.tsv', sep = '\t', header = T,row.names=1)
matall<-as.matrix(matall,row.names=1,header=T)
head(matall)
matfilter <- matall[rowSums(matall) > 90,]
hist(mat[,1])
hist(mat[1,],breaks=20)
mat<-normalize.quantiles(matfilter)
mat<-scale (mat)
tmat<-t(mat)
stmat<-scale (tmat)
mat1<-t(stmat)
head(mat1)
hist(mat1[,1])
hist(mat1[1,],breaks=40)
class(mat)
colnames(mat)<-matal
write.csv(mat1, file = "Yoruba.TPM.scaled.csv")# Convert vcf file to plink format
plink --vcf chr22.1000gphase3.455.0.01.recode.vcf --make-bed --out chr22.1000gphase3.455.0.01.recode.bed
# Run pca
plink --bfile chr22.1000gphase3.455.0.01.recode.bed --pca --out
# Plot pca
library(ggplot2)
ggplot(data=pca, aes(V3,V4)) + geom_point()
# surrogate variable analysis
library(sva)
mm <- model.matrix(~ population, colData(ddsTxi))
mm0 <- model.matrix(~ 1, colData(ddsTxi))
norm.cts <- norm.cts[rowSums(norm.cts) > 10,]
fit <- svaseq(norm.cts, mod=mm, mod0=mm0)
# 50 SVs are found. I use 7 SVs
sva7 = sva(norm.cts , mm, mm0, n.sv=7)
write.csv(sva7$sv[,1:7], file = "sva7.csv")library("DESeq2")
colData <-read.csv("1000g.sample.csv",header = T,row.names=1)
class(colData$date)
colData$date1<-factor(colData$date)
ddsTxi <- DESeqDataSetFromTximport(txi,
colData = colData,
design = ~ sex+population+performer)
colData <-read.csv("1000g.EUR.YRI.csv",header = T,row.names=1)
ddsEurYri <- DESeqDataSetFromTximport(txi,
colData = colData,
design = ~ sex+population+V1+V2+V3+V4+V5+V6+V7)
keep <- rowsum(counds(ddsEurYri)) >=455
ddsEurYri <- ddsEurYri[keep,]
ddsEY<-DESeq(ddsEurYri)
deg <- results(ddsEY, contrast=c("population","EUR","Yoruba"))
write.csv(deg, file = "E-GEUV_EUR_Yoruba.csv")# prepare input bed.gz and index files
module load bedtools
bedtools sort -i British.gene.TPM.bed -header > British.gene.TPM.sorted.bed
bgzip British.gene.TPM.sorted.bed && tabix -p bed British.gene.TPM.sorted.bed.gz
# prepare input vcf.gz and index files
module load htslib
module load tabix
for i in `seq 1 22`
do
bgzip genotype/phase3/chr$i.1000gphase3.Yoruba.0.01.biallelic.recode.vcf && tabix -p vcf genotype/phase3/chr$i.1000gphase3.Yoruba.0.01.biallelic.recode.vcf.gz
done
for i in `seq 1 22`
do
bgzip genotype/phase3/chr$i.1000gphase3.EUR.0.01.biallelic.recode.vcf && tabix -p vcf genotype/phase3/chr$i.1000gphase3.EUR.0.01.biallelic.recode.vcf.gz
done
# run fastQTL
## https://github.com/francois-a/fastqtl
for i in `seq 1 22`
do
./bin/fastQTL.static fastQTL --vcf /project2/xuanyao/marie/E-GEUV-1/genotype/phase3/chr$i.1000gphase3.EUR.0.01.biallelic.recode.vcf.gz --bed GEUV/EUR.gene.TPM.sorted.bed.gz --region $i:1-249250621 --threshold 0.001 --permute 1000 --out GEUV/EUR.chr$i.permute.0.001.txt --cov GEUV/EUR.cov.txt --normal
done
for i in `seq 1 22`
do
./bin/fastQTL.static fastQTL --vcf /project2/xuanyao/marie/E-GEUV-1/genotype/phase3/chr$i.1000gphase3.Yoruba.0.01.biallelic.recode.vcf.gz --bed GEUV/Yoruba.gene.TPM.sorted.bed.gz --region $i:1-249250621 --threshold 0.001 --permute 1000 --out GEUV/Yoruba.chr$i.permute.0.001.txt --cov GEUV/Yoruba.cov.txt --normal
donesetwd("/project2/xuanyao/marie/E-GEUV-1")
library(ggplot2)
library("ggrepel")
library("plyr")
library(gplots)
library(reshape2)
## remove multi-allelic SNPs reported as bi-allelic SNPs which confuse the result
EUR1<-subset(EUR2,
EUR2$gene.SNP!="ENSG00000188659.rs542232278"&
EUR2$gene.SNP!="ENSG00000255769.rs145926341"&
EUR2$gene.SNP!="ENSG00000255769.rs371891811"&
EUR2$gene.SNP!="ENSG00000259328.rs145926341"&
EUR2$gene.SNP!="ENSG00000259323.rs145926341"&
EUR2$gene.SNP!="ENSG00000259472.rs145926341"&
EUR2$gene.SNP!="ENSG00000103942.rs1610794"&
EUR2$gene.SNP!="ENSG00000103342.rs140839133"&
EUR2$gene.SNP!="ENSG00000159202.rs77094622"&
EUR2$gene.SNP!="ENSG00000189050.rs112549034"&
EUR2$gene.SNP!="ENSG00000108592.rs138776605"&
EUR2$gene.SNP!="ENSG00000256771.rs10650867"&
EUR2$gene.SNP!="ENSG00000134330.rs139337028"&
EUR2$gene.SNP!="ENSG00000125991.rs139252705"&
EUR2$gene.SNP!="ENSG00000214078.rs142898689"&
EUR2$gene.SNP!="ENSG00000087586.rs5842156"&
EUR2$gene.SNP!="ENSG00000171522.rs139036988"&
EUR2$gene.SNP!="ENSG00000196284.rs112143344"&
EUR2$gene.SNP!="ENSG00000135316.rs71553453"&
EUR2$gene.SNP!="ENSG00000232559.rs377632592"&
EUR2$gene.SNP!="ENSG00000131558.rs141161799"&
EUR2$gene.SNP!="ENSG00000162441.rs36126617"&
EUR2$gene.SNP!="ENSG00000162441.rs151178549"&
EUR2$gene.SNP!="ENSG00000142794.rs35506192"&
EUR2$gene.SNP!="ENSG00000142794.rs145038894"&
EUR2$gene.SNP!="ENSG00000116128.rs10657777"&
EUR2$gene.SNP!="ENSG00000117280.rs149256505"&
EUR2$gene.SNP!="ENSG00000107719.rs138584752"&
EUR2$gene.SNP!="ENSG00000198561.rs142865693"&
EUR2$gene.SNP!="ENSG00000087365.rs5792377"&
EUR2$gene.SNP!="ENSG00000162341.rs111786372"&
EUR2$gene.SNP!="ENSG00000110092.rs59333593"&
EUR2$gene.SNP!="ENSG00000111215.rs61604574"&
EUR2$gene.SNP!="ENSG00000165502.rs141634854"&
EUR2$gene.SNP!="ENSG00000104093.rs138911097")
YRI1<-subset(YRI2, YRI2$gene.SNP!="ENSG00000143106.rs545041240"&
YRI2$gene.SNP!="ENSG00000078403.rs528760884"&
YRI2$gene.SNP!="ENSG00000167996.rs150035626"&
YRI2$gene.SNP!="ENSG00000111252.rs367797687"&
YRI2$gene.SNP!="ENSG00000172458.rs112879834"&
YRI2$gene.SNP!="ENSG00000183044.rs367630500"&
YRI2$gene.SNP!="ENSG00000154874.rs138555657"&
YRI2$gene.SNP!="ENSG00000011132.rs71166969"&
YRI2$gene.SNP!="ENSG00000125991.rs142898689"&
YRI2$gene.SNP!="ENSG00000198832.rs35065681"&
YRI2$gene.SNP!="ENSG00000128268.rs142897975"&
YRI2$gene.SNP!="ENSG00000161013.rs71591436"&
YRI2$gene.SNP!="ENSG00000170727.rs60257564")
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Oslo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 httr_1.4.7 cli_3.6.2 knitr_1.45
[5] rlang_1.1.3 xfun_0.42 stringi_1.8.3 processx_3.8.3
[9] promises_1.3.0 jsonlite_1.8.8 glue_1.7.0 rprojroot_2.0.4
[13] git2r_0.33.0 htmltools_0.5.7 httpuv_1.6.15 ps_1.7.6
[17] sass_0.4.8 fansi_1.0.6 rmarkdown_2.26 jquerylib_0.1.4
[21] tibble_3.2.1 evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
[25] lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1 compiler_4.3.1
[29] fs_1.6.3 pkgconfig_2.0.3 Rcpp_1.0.12 rstudioapi_0.15.0
[33] later_1.3.2 digest_0.6.34 R6_2.5.1 utf8_1.2.4
[37] pillar_1.9.0 callr_3.7.5 magrittr_2.0.3 bslib_0.6.1
[41] tools_4.3.1 cachem_1.0.8 getPass_0.2-4