Last updated: 2022-02-02
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| html | b50da3b | Julien Bryois | 2022-02-02 | Build site. |
| html | b6554cb | Julien Bryois | 2022-01-28 | Build site. |
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| Rmd | 615e226 | Julien Bryois | 2022-01-25 | GWAS epigenome overlap |
library(tidyverse)
library(liftOver)path <- system.file(package="liftOver", "extdata", "hg38ToHg19.over.chain")
ch <- import.chain(path)r2_threshold <- 0.8get_gwas_linked_snps <- function(x,threshold=r2_threshold){
if(nrow(x)>1){
x <- filter(x,R2>=0.1) %>%
mutate(chr_snp=Coord %>% gsub(':.+','',.) %>% unique(),
chr_numeric=gsub('chr','',chr_snp),
start_snp=Coord %>% gsub('chr[0-9]{1,2}:','',.) %>% as.numeric(),
end_snp=start_snp) %>%
dplyr::select(chr_snp,start_snp,end_snp,everything()) %>%
dplyr::select(-Correlated_Alleles,-Dprime,-Alleles,-Coord) %>%
mutate(locus_name=paste0(chr_snp,':',min(start_snp),'_',max(end_snp))) %>%
dplyr::rename(Distance_from_index_SNP=Distance)
top_snp_df <- filter(x,Distance_from_index_SNP==0)
top_snp_pos <- top_snp_df %>% mutate(Coord=paste0(chr_snp,':',start_snp)) %>% pull(Coord)
x <- filter(x,R2>=threshold) %>%
mutate(GWAS_snp_pos=top_snp_pos) %>%
mutate(RS_Number=ifelse(RS_Number=='.',paste0(chr_snp,':',start_snp,'-',end_snp),RS_Number))
return(x)
}
else{
return(NULL)
}
}proxy_snps_pd <- readRDS('data/gwas/pd/loci_LDlinkR.allSNPs.EUR.rds')potential_causal_snps_pd <- mclapply(proxy_snps_pd,get_gwas_linked_snps,mc.cores=20) %>%
bind_rows() %>%
arrange(chr_numeric,start_snp) %>%
dplyr::select(-chr_numeric) %>%
mutate(trait='pd')proxy_snps_ad <- readRDS('data/gwas/ad/loci_LDlinkR.allSNPs.EUR.rds')potential_causal_snps_ad <- mclapply(proxy_snps_ad,get_gwas_linked_snps,mc.cores=20) %>%
bind_rows() %>%
arrange(chr_numeric,start_snp) %>%
dplyr::select(-chr_numeric) %>%
mutate(trait='ad')proxy_snps_ms <- readRDS('data/gwas/ms/loci_LDlinkR.allSNPs.EUR.rds')potential_causal_snps_ms <- mclapply(proxy_snps_ms,get_gwas_linked_snps,mc.cores=20) %>%
bind_rows() %>%
arrange(chr_numeric,start_snp) %>%
dplyr::select(-chr_numeric) %>%
mutate(trait='ms')proxy_snps_scz <- readRDS('data/gwas/scz/loci_LDlinkR.allSNPs.EUR.rds')potential_causal_snps_scz <- mclapply(proxy_snps_scz,get_gwas_linked_snps,mc.cores=20) %>%
bind_rows() %>%
arrange(chr_numeric,start_snp) %>%
dplyr::select(-chr_numeric) %>%
mutate(trait='scz')potential_causal_snps <- rbind(potential_causal_snps_pd,potential_causal_snps_ad,potential_causal_snps_ms,potential_causal_snps_scz)
write_tsv(potential_causal_snps,paste0('data/gwas_epigenome_overlap/GWAS_1kg_linked_SNPs_r',r2_threshold,'.bed'),col_names = FALSE)corces_hg38 <- readxl::read_xlsx('data/epigenome_enrichment/external_data/corces/Corces_etal_Nat_Neuro_2020_TableS4.xlsx',skip=16)corces_hg38 <- corces_hg38 %>%
gather(cell_type,present,ExcitatoryNeurons:OPCs) %>%
group_by(Peak_ID) %>%
mutate(count_present=sum(present)) %>%
ungroup() %>%
mutate(specific=ifelse(present==1 & count_present==1,1,0)) %>%
filter(present==1) %>%
mutate(study='Corces') %>%
dplyr::select(hg38_Chromosome,hg38_Start,hg38_Stop,study,cell_type,specific,Annotation) %>%
makeGRangesFromDataFrame(., TRUE)Lift to hg19
corces_hg19 <- liftOver(corces_hg38,ch) %>%
unlist() %>%
as.data.frame() %>%
as_tibble() %>%
dplyr::select(seqnames:end,study,cell_type,specific,Annotation) %>%
write_tsv(.,'data/gwas_epigenome_overlap/corces_hg19.bed',col_names=FALSE)Hg19
process_nott <- function(df,cell_type,Annotation){
df <- setNames(df,c('chr','start','end')) %>% filter(!is.na(chr))
df <- df %>%
filter(chr%in%paste0('chr',1:22)) %>%
arrange(chr,start) %>%
unique() %>%
mutate(study='Nott',cell_type=cell_type, Annotation=Annotation) %>%
write_tsv(.,paste0('data/epigenome_enrichment/external_data/nott/',cell_type,'.',Annotation,'.bed'),col_names = FALSE)
}astro_enh <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=5) %>%
process_nott(.,cell_type='Astrocytes',Annotation='Enhancer')astro_promoter <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=6) %>%
process_nott(.,cell_type='Astrocytes',Annotation='Promoter')neuronal_enh <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=7) %>%
process_nott(.,cell_type='Neurons',Annotation='Enhancer')neuronal_promoter <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=8) %>%
process_nott(.,cell_type='Neurons',Annotation='Promoter')oligo_enh <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=9) %>%
process_nott(.,cell_type='Oligodendrocytes',Annotation='Enhancer')oligo_promoter <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=10) %>%
process_nott(.,cell_type='Oligodendrocytes',Annotation='Promoter')micro_enhancer <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=11) %>%
process_nott(.,cell_type='Microglia',Annotation='Enhancer')micro_promoter <- readxl::read_xlsx('data/epigenome_enrichment/external_data/nott/Nott_etal_Science2019_Table_S5.xlsx',skip=0,sheet=12) %>%
process_nott(.,cell_type='Microglia',Annotation='Promoter')source ~/.bashrc
cd data/epigenome_enrichment/external_data/nott
ml bedtools/2.25.0-goolf-1.7.20
bedtools intersect -v -a Astrocytes.Enhancer.bed -b Microglia.Enhancer.bed Neurons.Enhancer.bed Oligodendrocytes.Enhancer.bed > Astrocytes.Enhancer_specific.bed
bedtools intersect -v -a Microglia.Enhancer.bed -b Astrocytes.Enhancer.bed Neurons.Enhancer.bed Oligodendrocytes.Enhancer.bed > Microglia.Enhancer_specific.bed
bedtools intersect -v -a Neurons.Enhancer.bed -b Astrocytes.Enhancer.bed Microglia.Enhancer.bed Oligodendrocytes.Enhancer.bed > Neurons.Enhancer_specific.bed
bedtools intersect -v -a Oligodendrocytes.Enhancer.bed -b Astrocytes.Enhancer.bed Microglia.Enhancer.bed Neurons.Enhancer.bed > Oligodendrocytes.Enhancer_specific.bedsource ~/.bashrc
cd data/epigenome_enrichment/external_data/nott
ml bedtools/2.25.0-goolf-1.7.20
bedtools intersect -v -a Astrocytes.Promoter.bed -b Microglia.Promoter.bed Neurons.Promoter.bed Oligodendrocytes.Promoter.bed > Astrocytes.Promoter_specific.bed
bedtools intersect -v -a Microglia.Promoter.bed -b Astrocytes.Promoter.bed Neurons.Promoter.bed Oligodendrocytes.Promoter.bed > Microglia.Promoter_specific.bed
bedtools intersect -v -a Neurons.Promoter.bed -b Astrocytes.Promoter.bed Microglia.Promoter.bed Oligodendrocytes.Promoter.bed > Neurons.Promoter_specific.bed
bedtools intersect -v -a Oligodendrocytes.Promoter.bed -b Astrocytes.Promoter.bed Microglia.Promoter.bed Neurons.Promoter.bed > Oligodendrocytes.Promoter_specific.bedspecific_files <- list.files('data/epigenome_enrichment/external_data/nott',pattern='specific.bed',full.names = TRUE)
Nott_specific_hg19 <- tibble(files=specific_files) %>%
mutate(file_content=map(files,read_tsv,col_names=FALSE)) %>%
unnest(file_content) %>%
dplyr::select(-files) %>%
mutate(id=paste0(X1,':',X2,'-',X3,'-',X5,'-',X6))nott_files <- list.files('data/epigenome_enrichment/external_data/nott',pattern='.bed',full.names = TRUE) %>% grep('specific',.,value=TRUE,invert=TRUE)
nott_hg19 <- tibble(files=nott_files) %>%
mutate(file_content=map(files,read_tsv,col_names=FALSE)) %>%
unnest(file_content) %>%
dplyr::select(-files) %>%
mutate(id=paste0(X1,':',X2,'-',X3,'-',X5,'-',X6)) %>%
mutate(specific=ifelse(id%in%Nott_specific_hg19$id,1,0)) %>%
setNames(c('seqnames','start','end','study','cell_type','Annotation','id','specific')) %>%
dplyr::select(seqnames,start,end,study,cell_type,specific,Annotation)Get all peaks discovered in corces et al. and nott et al.
bed <- rbind(corces_hg19,nott_hg19) %>%
arrange(seqnames,start) %>%
write_tsv(.,'data/gwas_epigenome_overlap/epigenomic_marks.bed',col_names=FALSE)Add distance from enhancer to closest TSS
gtf <- rtracklayer::import('data/gencode/gencode.v39lift37.annotation.gtf.gz') %>%
as.data.frame() %>%
dplyr::filter(type=='gene',gene_type=='protein_coding') %>%
dplyr::select(seqnames,start,end,strand,gene_id,gene_name) %>%
mutate(TSS_start=ifelse(strand=='+',start,end),
TSS_end=ifelse(strand=='+',start,end)) %>%
as_tibble() %>%
mutate(gene=paste0(gene_name,'_',gsub('\\..+','',gene_id))) %>%
filter(seqnames %in% paste0('chr',1:22)) %>%
mutate(seqnames=as.character(seqnames)) %>%
arrange(seqnames,start) %>%
dplyr::select(seqnames,TSS_start,TSS_end,gene) %>%
write_tsv(.,'data/gwas_epigenome_overlap/gencode.v39lift37.annotation.protein_coding.1_22.TSS.bed',col_names = FALSE)source ~/.bashrc
ml bedtools/2.25.0-goolf-1.7.20
cd data/gwas_epigenome_overlap
bedtools intersect -wa -wb -a epigenomic_marks.bed -b GWAS_1kg_linked_SNPs_r0.8.bed > GWAS_epigenomic_marks.bed
sort -k1,1V -k2,2n GWAS_epigenomic_marks.bed > GWAS_epigenomic_marks.sorted.bed
sort -k1,1V -k2,2n -k3,3n gencode.v39lift37.annotation.protein_coding.1_22.TSS.bed > gencode.v39lift37.annotation.protein_coding.1_22.TSS.sorted.bed
bedtools closest -d -t first -wa -a GWAS_epigenomic_marks.sorted.bed -b gencode.v39lift37.annotation.protein_coding.1_22.TSS.sorted.bed > GWAS_epigenomic_marks.closest_gene.bedAdd distance to coloc genes
read_coloc <- function(path){
coloc <- read_tsv(path) %>%
dplyr::select(locus,ensembl,symbol,tissue,PP.H4.abf) %>%
dplyr::rename(phenotype=ensembl,hgnc_symbol=symbol,locus_name=locus) %>%
filter(PP.H4.abf>0.7)
#If coloc was performed for the same genes located in loci in close proximity, take the best
coloc <- coloc %>% group_by(phenotype,tissue) %>%
slice_max(n=1,order_by=PP.H4.abf,with_ties=FALSE) %>%
ungroup()
return(coloc)
}coloc_ms <- read_coloc('output/coloc/coloc.ms.txt') %>% mutate(trait='ms')
coloc_ad <- read_coloc('output/coloc/coloc.ad.txt') %>% mutate(trait='ad')
coloc_scz <- read_coloc('output/coloc/coloc.scz.txt') %>% mutate(trait='scz')
coloc_pd <- read_coloc('output/coloc/coloc.pd.txt') %>% mutate(trait='pd')coloc <- rbind(coloc_ms,coloc_ad,coloc_scz,coloc_pd) %>%
arrange(-PP.H4.abf) %>%
mutate(gene=paste0(hgnc_symbol,'_',phenotype)) %>%
dplyr::select(gene,tissue,PP.H4.abf,trait)gtf_coloc <- inner_join(gtf,coloc,by='gene') %>%
mutate(chr_numeric = as.numeric(gsub('chr','',seqnames))) %>%
arrange(chr_numeric,TSS_start) %>%
dplyr::select(-chr_numeric) %>%
write_tsv('data/gwas_epigenome_overlap/coloc_genes.bed',col_names = FALSE)source ~/.bashrc
ml bedtools/2.25.0-goolf-1.7.20
cd data/gwas_epigenome_overlap
bedtools closest -d -t all -wa -a GWAS_epigenomic_marks.closest_gene.bed -b coloc_genes.bed > GWAS_epigenomic_marks.closest_gene.closest_coloc.beddir.create('output/gwas_epigenome_overlap',showWarnings = FALSE)
res <- read_tsv('data/gwas_epigenome_overlap/GWAS_epigenomic_marks.closest_gene.closest_coloc.bed',col_names = FALSE) %>%
setNames(c(colnames(bed),colnames(potential_causal_snps),'chr_gene','start_gene','end_gene','closest_TSS_from_regulatory_region','distance_to_closest_TSS','chr_coloc_gene','start_coloc_gene','end_coloc_gene','closest_coloc_TSS_from_regulatory_region','cell_type_coloc','PP.H4.abf','trait_coloc','distance_to_closest_coloc_TSS')) %>%
dplyr::select(-MAF,-chr_gene,-start_gene,-end_gene,-chr_coloc_gene,-start_coloc_gene,-end_coloc_gene) %>%
#add_count(RS_Number,name = 'n_SNP_overlap') %>%
dplyr::rename(chr_epigenome=seqnames,start_epigenome=start,end_epigenome=end,study_epigenome=study,Annotation_study=Annotation) %>%
arrange(chr_epigenome,start_epigenome) %>%
write_tsv(.,'output/gwas_epigenome_overlap/GWAS_epigenomic_marks.closest_gene.closest_coloc.txt')Get regions of interest
res <- res %>% mutate(cell_type_coloc_parsed = case_when(
cell_type_coloc %in% c('Excitatory neurons','Inhibitory neurons') ~'Neurons',
cell_type_coloc %in% c('OPCs / COPs') ~'OPCs',
TRUE ~ cell_type_coloc
)) %>%
mutate(cell_type_parsed = case_when(
cell_type %in% c('ExcitatoryNeurons','InhibitoryNeurons') ~ 'Neurons',
TRUE ~cell_type
)) %>%
mutate(width=end_epigenome-start_epigenome+1)Only keep SNPs overlaping epigenomic marks that are located less than 100kb from the TSS of a coloc gene, for which the epigenomic mark was detected in the coloc cell types.
potential_sequences_of_interest <- filter(res,distance_to_closest_coloc_TSS<100000,trait==trait_coloc,cell_type_coloc_parsed==cell_type_parsed)eqtl_files_to_read <- potential_sequences_of_interest %>%
dplyr::select(chr_epigenome,cell_type_coloc) %>% unique()get_eqtl_pvalue <- function(i){
df <- data.table::fread(paste0('data_sensitive/eqtl/PC70_nominal/',make.names(eqtl_files_to_read$cell_type_coloc[i]),'.',gsub('chr','',eqtl_files_to_read$chr_epigenome[i]),'.gz')) %>%
filter(V2%in%potential_sequences_of_interest$RS_Number,
V1%in%potential_sequences_of_interest$closest_coloc_TSS_from_regulatory_region) %>%
dplyr::select(closest_coloc_TSS_from_regulatory_region=V1,RS_Number=V2,p_eqtl=V4,beta_eqtl=V5) %>%
as_tibble() %>%
mutate(cell_type_coloc=eqtl_files_to_read$cell_type_coloc[i])
}eqtl_pvalues <- mclapply(1:nrow(eqtl_files_to_read),get_eqtl_pvalue,mc.cores=20) %>% bind_rows()potential_sequences_of_interest <- left_join(potential_sequences_of_interest,eqtl_pvalues,by=c('closest_coloc_TSS_from_regulatory_region','RS_Number','cell_type_coloc'))potential_sequences_of_interest <- potential_sequences_of_interest %>% dplyr::select(chr_epigenome:study_epigenome,cell_type_epigenome=cell_type,specific,RS_Number,R2,RegulomeDB,Function,locus_name,trait_coloc,closest_coloc_TSS_from_regulatory_region,distance_to_closest_coloc_TSS,cell_type_coloc,PP.H4.abf,p_eqtl) %>%
write_csv('data/gwas_epigenome_overlap/GWAS_epigenomic_marks.closest_gene.closest_coloc.filtered.csv')cd data/gwas_epigenome_overlap
cut -d ',' -f 7 GWAS_epigenomic_marks.closest_gene.closest_coloc.filtered.csv | sort -u |grep -v 'RS_Number' > snps_4_SNP2TFBS.txtUse webtool at: https://ccg.epfl.ch/snp2tfbs/snpselect.php
if(file.exists('data/gwas_epigenome_overlap/SNP2TFBS_out.txt')){
snp2tfbs <- read_tsv('data/gwas_epigenome_overlap/SNP2TFBS_out.txt',col_names = FALSE) %>%
dplyr::select(RS_Number=X7,X6) %>% mutate(SNP2TFBS_disrupted_TFmotif=gsub('.+TF=|;Score.+','',X6)) %>%
dplyr::select(-X6)
potential_sequences_of_interest <- left_join(potential_sequences_of_interest,snp2tfbs,by='RS_Number')
write_csv(potential_sequences_of_interest,'output/gwas_epigenome_overlap/GWAS_epigenomic_marks.closest_gene.closest_coloc.filtered.SNP2TFBS.txt')
}
sessionInfo()R version 4.0.1 (2020-06-06)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /pstore/apps/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblasp-r0.3.1.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] liftOver_1.12.0
[2] Homo.sapiens_1.3.1
[3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[4] org.Hs.eg.db_3.11.4
[5] GO.db_3.11.4
[6] OrganismDbi_1.30.0
[7] GenomicFeatures_1.40.0
[8] AnnotationDbi_1.50.0
[9] Biobase_2.48.0
[10] rtracklayer_1.48.0
[11] GenomicRanges_1.40.0
[12] GenomeInfoDb_1.24.0
[13] IRanges_2.22.2
[14] S4Vectors_0.26.1
[15] BiocGenerics_0.34.0
[16] gwascat_2.20.1
[17] forcats_0.5.0
[18] stringr_1.4.0
[19] dplyr_1.0.0
[20] purrr_0.3.4
[21] readr_1.3.1
[22] tidyr_1.1.0
[23] tibble_3.0.1
[24] ggplot2_3.3.3
[25] tidyverse_1.3.0
[26] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-148 matrixStats_0.56.0
[3] bitops_1.0-6 fs_1.4.1
[5] lubridate_1.7.9 bit64_0.9-7
[7] progress_1.2.2 httr_1.4.1
[9] rprojroot_1.3-2 tools_4.0.1
[11] backports_1.1.7 R6_2.4.1
[13] DBI_1.1.0 colorspace_1.4-1
[15] withr_2.2.0 prettyunits_1.1.1
[17] tidyselect_1.1.0 curl_4.3
[19] bit_1.1-15.2 compiler_4.0.1
[21] git2r_0.27.1 graph_1.66.0
[23] cli_2.0.2 rvest_0.3.5
[25] xml2_1.3.2 DelayedArray_0.14.0
[27] scales_1.1.1 RBGL_1.64.0
[29] askpass_1.1 rappdirs_0.3.1
[31] Rsamtools_2.4.0 digest_0.6.25
[33] rmarkdown_2.2 XVector_0.28.0
[35] pkgconfig_2.0.3 htmltools_0.5.1.1
[37] dbplyr_1.4.4 rlang_0.4.10
[39] readxl_1.3.1 rstudioapi_0.11
[41] RSQLite_2.2.0 generics_0.0.2
[43] jsonlite_1.6.1 BiocParallel_1.22.0
[45] RCurl_1.98-1.2 magrittr_1.5
[47] GenomeInfoDbData_1.2.3 Matrix_1.2-18
[49] Rcpp_1.0.6 munsell_0.5.0
[51] fansi_0.4.1 lifecycle_0.2.0
[53] stringi_1.4.6 whisker_0.4
[55] yaml_2.2.1 SummarizedExperiment_1.18.1
[57] zlibbioc_1.34.0 BiocFileCache_1.12.0
[59] grid_4.0.1 blob_1.2.1
[61] promises_1.1.1 crayon_1.3.4
[63] lattice_0.20-41 Biostrings_2.56.0
[65] haven_2.3.1 hms_0.5.3
[67] knitr_1.28 pillar_1.4.4
[69] biomaRt_2.44.4 reprex_0.3.0
[71] XML_3.99-0.3 glue_1.4.1
[73] evaluate_0.14 BiocManager_1.30.10
[75] modelr_0.1.8 vctrs_0.3.1
[77] httpuv_1.5.4 cellranger_1.1.0
[79] openssl_1.4.1 gtable_0.3.0
[81] assertthat_0.2.1 xfun_0.14
[83] broom_0.5.6 later_1.1.0.1
[85] GenomicAlignments_1.24.0 memoise_1.1.0
[87] ellipsis_0.3.1