S-LDSC example analysis to estimate the enrichment of GWAS variants in neuron ATAC-seq OCR and ASoC variants

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Last updated: 2020-07-08

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---

This tutorial shows an example analysis using S-LDSC analysis to estimate the enrichment of GWAS variants in neuron ATAC-seq annotations: open chromatin regions (OCR) and allele-specific open chromatin (ASoC) variants. The analysis uses data from our paper: Zhang et al. Allele-specific open chromatin in human iPSC neurons elucidates functional noncoding disease variants. Science 2020.

Prepare annotation files and compute LD scores for annotations

Prepare annotations in BED format for ASoC binary annotations

• Convert ASoC binary annotations to BED format. The annotations can be found in /project2/xinhe/kevinluo/ldsc/annot/annot_bed/
• Annotations for ATAC-seq peaks in BED format can also be found in /project2/xinhe/kevinluo/ldsc/annot/annot_bed/.

## Prepare ASoC binary annotations in BED format for LDSC analysis
dir_annot_bed <- "/project2/xinhe/kevinluo/ldsc/annot/annot_bed/"

### ASoC_glut_anno_hg19
annot_name <- "ASoC_glut_anno_hg19"
annot_filename <- "ASoC_glut_anno_hg19.txt"
ASoC_annot <- read.table(paste0(dir_annot_bed, "/", annot_filename), header = F, stringsAsFactors = F)
colnames(ASoC_annot) <- c("chr", "SNP_POS", "annot")

ASoC_sig <- ASoC_annot[ASoC_annot$annot == 1, ]
ASoC_sig.bed <- data.frame(chr = ASoC_sig$chr, start = ASoC_sig$SNP_POS - 1, end = ASoC_sig$SNP_POS)
ASoC_sig.bed$chr <- factor(ASoC_sig.bed$chr, levels = paste0("chr", 1:22))
ASoC_sig.bed <- ASoC_sig.bed[order(ASoC_sig.bed$chr, ASoC_sig.bed$start), ]
ASoC_sig.bed <- unique(ASoC_sig.bed)
cat(nrow(ASoC_sig.bed), "SNPs with annotation:", annot_name, "\n")
write.table(ASoC_sig.bed, paste0(dir_annot_bed, "/", annot_name, ".bed"), sep = "\t", col.names = F, row.names = F, quote = F)

### ASoC_npc_anno_hg19
annot_name <- "ASoC_npc_anno_hg19"
annot_filename <- "ASoC_npc_anno_hg19.txt"
ASoC_annot <- read.table(paste0(dir_annot_bed, "/", annot_filename), header = F, stringsAsFactors = F)
colnames(ASoC_annot) <- c("chr", "SNP_POS", "annot")

ASoC_sig <- ASoC_annot[ASoC_annot$annot == 1, ]
ASoC_sig.bed <- data.frame(chr = ASoC_sig$chr, start = ASoC_sig$SNP_POS - 1, end = ASoC_sig$SNP_POS)
ASoC_sig.bed$chr <- factor(ASoC_sig.bed$chr, levels = paste0("chr", 1:22))
ASoC_sig.bed <- ASoC_sig.bed[order(ASoC_sig.bed$chr, ASoC_sig.bed$start), ]
ASoC_sig.bed <- unique(ASoC_sig.bed)
cat(nrow(ASoC_sig.bed), "SNPs with annotation:", annot_name, "\n")
write.table(ASoC_sig.bed, paste0(dir_annot_bed, "/", annot_name, ".bed"), sep = "\t", col.names = F, row.names = F, quote = F)

Compute LD scores for ATAC-seq peaks and ASoC annotations

The following code generates ldsc-friendly annotation files (annot.gz) from the annotation BED files using this R script, then computes LD scores with the annot file (annot.gz).

## Compute LD scores for ATAC-seq peak annotations
dir_code=~/projects/analysis_pipelines/code/

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch CN_all_peaks.narrowPeak.cleaned.hg19.merged

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch DN_all_peaks.narrowPeak.cleaned.hg19.merged

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch GA_all_peaks.narrowPeak.cleaned.hg19.merged

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch ips_all_peaks.narrowPeak.cleaned.hg19.merged

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch NSC_all_peaks.narrowPeak.cleaned.hg19.merged

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch NSC_all_peaks.narrowPeak.cleaned.hg19.merged

## Compute LD scores for ASoC annotations
sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch ASoC_glut_anno_hg19

sbatch ${dir_code}/ldsc_binary_annot_QTL.sbatch ASoC_npc_anno_hg19

Computed LD scores for ATAC-seq peaks and ASoC annotations can be found in /project2/xinhe/kevinluo/ldsc/annot/ldscores/.

Partition heritability using S-LDSC

https://github.com/bulik/ldsc/wiki/Partitioned-Heritability

Prepare GWAS summary statistics in LDSC format

Convert GWAS summary statistics to the .sumstats format using munge_sumstats.py

See this page for details

Converted GWAS summary statistics (LDSC format) are available in /project2/xinhe/kevinluo/GWAS/GWAS_summary_stats/GWAS_from_Min/ldsc_format/

Partition heritability

The following code estimates the partitioned heritability and enrichment for annotations

#!/bin/bash

#SBATCH --job-name=sldsc
#SBATCH --output=sldsc_%J.out
#SBATCH --error=sldsc_%J.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G

dir_GWAS=$1
trait=$2
prefix_annot=$3
dir_sLDSC_output=$4

dir_LDSC=/project2/xinhe/kevinluo/ldsc
dir_ldsc_annot=/project2/xinhe/kevinluo/ldsc/annot/ldscores
dir_baselineLD=/project2/xinhe/kevinluo/ldsc/LDSCORE/1000G_Phase3_baselineLD_v1.1_ldscores

conda activate ldsc

echo "GWAS trait: ${trait}"

dir_out=${dir_sLDSC_output}/${trait}/baselineLDv1.1
mkdir -p ${dir_out}

python $HOME/softwares/ldsc/ldsc.py \
--h2 ${dir_GWAS}/${trait}.sumstats.gz \
--ref-ld-chr ${dir_baselineLD}/baselineLD.,${dir_ldsc_annot}/${prefix_annot}/${prefix_annot}. \
--frqfile-chr ${dir_LDSC}/LDSCORE/1000G_Phase3_frq/1000G.EUR.QC. \
--w-ld-chr ${dir_LDSC}/LDSCORE/1000G_Phase3_weights_hm3_no_MHC/weights.hm3_noMHC. \
--overlap-annot --print-cov --print-coefficients --print-delete-vals \
--out ${dir_out}/${trait}_${prefix_annot}_baselineLDv1.1

Run S-LDSC across a number of GWAS traits over the ATAC-seq peaks and ASoC annotations.

Results are saved in /project2/xinhe/kevinluo/ldsc/results/sLDSC_neuron_ATACseq_examples/

TRAITS=("ADHD" "IBD" "BMI" "height" "SCZ" "BIP" "MDD" "iPSYCH_ASD" "Intelligence" "Education" "Neuroticism" "Alzheimer" "Parkinson")
dir_GWAS=/project2/xinhe/kevinluo/GWAS/GWAS_summary_stats/GWAS_from_Min/ldsc_format/
dir_sLDSC_output=/project2/xinhe/kevinluo/ldsc/results/sLDSC_neuron_ATACseq_examples/
dir_code=~/projects/analysis_pipelines/code/

for trait in "${TRAITS[@]}"
do
  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} CN_all_peaks.narrowPeak.cleaned.hg19.merged ${dir_sLDSC_output}

  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} DN_all_peaks.narrowPeak.cleaned.hg19.merged ${dir_sLDSC_output}

  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} GA_all_peaks.narrowPeak.cleaned.hg19.merged ${dir_sLDSC_output}

  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} ips_all_peaks.narrowPeak.cleaned.hg19.merged ${dir_sLDSC_output}

  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} NSC_all_peaks.narrowPeak.cleaned.hg19.merged ${dir_sLDSC_output}

  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} ASoC_glut_anno_hg19 ${dir_sLDSC_output}

  sbatch ${dir_code}/sldsc_annot_baselineLD_separate.sbatch ${dir_GWAS} ${trait} ASoC_npc_anno_hg19 ${dir_sLDSC_output}
done

Extract and plot S-LDSC enrichment results

annot_list <- c("iN-Glut ASoC", "NPC ASoC", "iN-GA OCR", "iN-DN OCR", "NPC OCR", "iN-Glut OCR", "iPSC OCR")
trait_name_list <- c("SCZ", "BIP", "MDD", "Intelligence", "IBD")

library(ggplot2)
library(foreach)
library(doParallel)

Loading required package: iterators

Loading required package: parallel

## Plot enrichment
ggplot_enrichment <- function(result_sLDSC, xlim = NULL, title = "Enrichment"){
  
  Enrichment <- result_sLDSC$Enrichment
  Enrichment_CI_L <- result_sLDSC$Enrichment - 1.96*result_sLDSC$Enrichment_std_error
  Enrichment_CI_H <- result_sLDSC$Enrichment + 1.96*result_sLDSC$Enrichment_std_error

  ## truncate at 1
  Enrichment[Enrichment < 1] <- 1
  Enrichment_CI_L[Enrichment_CI_L < 1] <- 1
  Enrichment_CI_H[Enrichment_CI_H < 1] <- 1
  
  p <- ggplot(result_sLDSC, aes(x = Enrichment, y = Category, colour = Color))+
    geom_point()+
    xlab("Enrichment")+
    ggtitle(title)+
    geom_errorbarh(aes(xmin = Enrichment - 1.96*Enrichment_std_error, 
                       xmax = Enrichment + 1.96*Enrichment_std_error, height = 0.1))+ 
    facet_wrap(Disease~.,ncol = 3)+
    theme_bw()  + 
    geom_vline(xintercept = 1,linetype="dotted", colour = "red")+
    theme(axis.ticks = element_blank(),  
          panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"), 
          axis.text = element_text(face="bold",size = 12, colour = "black"),
          axis.title = element_text(face="bold",size = 12),
          strip.text = element_text(face="bold",size = 12), 
          panel.spacing.x = unit(0.6,units = "cm"), 
          axis.title.y = element_blank(), 
          legend.position = "none", 
          plot.title = element_text(hjust = 0.5))
  if(!is.null(xlim)){
    p <- p + coord_cartesian(xlim = xlim)
  }
  print(p)
}

ggplot_log2_enrichment <- function(result_sLDSC, xlim = NULL, title = "Enrichment"){
  
  result_sLDSC$Enrichment_CI_L <- result_sLDSC$Enrichment - 1.96*result_sLDSC$Enrichment_std_error
  result_sLDSC$Enrichment_CI_H <- result_sLDSC$Enrichment + 1.96*result_sLDSC$Enrichment_std_error

  ## truncate at 1
  result_sLDSC$Enrichment[result_sLDSC$Enrichment < 1] <- 1
  result_sLDSC$Enrichment_CI_L[result_sLDSC$Enrichment_CI_L < 1] <- 1
  result_sLDSC$Enrichment_CI_H[result_sLDSC$Enrichment_CI_H < 1] <- 1

  p <- ggplot(result_sLDSC, aes(x = log2(Enrichment), y = Category, colour = Color))+
    geom_point()+
    xlab("log2(Enrichment)")+
    ggtitle(title)+
    geom_errorbarh(aes(xmin = log2(Enrichment_CI_L), 
                       xmax = log2(Enrichment_CI_H), height = 0.1))+ 
    facet_wrap(Disease~.,ncol = 3)+
    theme_bw()  + 
    geom_vline(xintercept = 0,linetype="dotted", colour = "red")+
    theme(axis.ticks = element_blank(),  
          panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"), 
          axis.text = element_text(face="bold",size = 12, colour = "black"),
          axis.title = element_text(face="bold",size = 12),
          strip.text = element_text(face="bold",size = 12), 
          panel.spacing.x = unit(0.6,units = "cm"), 
          axis.title.y = element_blank(), 
          legend.position = "none", 
          plot.title = element_text(hjust = 0.5))
  if(!is.null(xlim)){
    p <- p + coord_cartesian(xlim = xlim)
  }
  print(p)
}

ggplot_heritability <- function(result_sLDSC, xlim = NULL, title = "Heritability"){
  ## Proportion of heritability
  p <- ggplot(result_sLDSC, aes(x = Prop._h2*100, y = Category, colour = Color))+
    geom_point()+
    xlab("Heritability %")+
    ggtitle(title)+
    geom_errorbarh(aes(xmin = (Prop._h2-1.96*Prop._h2_std_error)*100, 
                       xmax = (Prop._h2+1.96*Prop._h2_std_error)*100, height = 0.1))+ 
    facet_wrap(Disease~.,ncol = 3)+
    theme_bw()  + 
    geom_vline(xintercept = 0,linetype="dotted", colour = "red")+
    theme(axis.ticks = element_blank(),  
          panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"), 
          axis.text = element_text(face="bold",size = 12, colour = "black"),
          axis.title = element_text(face="bold",size = 12),
          strip.text = element_text(face="bold",size = 12), 
          panel.spacing.x = unit(0.6,units = "cm"), 
          axis.title.y = element_blank(), 
          legend.position = "none", 
          plot.title = element_text(hjust = 0.5))
  if(!is.null(xlim)){
    p <- p + coord_cartesian(xlim = xlim)
  }
  print(p)
}

## change names for traits
change_annot_names <- function(annot_list){
  annot_list <- gsub("^CN$","iN-Glut OCR", annot_list)
  annot_list <- gsub("^DN$","iN-DN OCR", annot_list)
  annot_list <- gsub("^GA$","iN-GA OCR", annot_list)
  annot_list <- gsub("^ips$","iPSC OCR", annot_list)
  annot_list <- gsub("^NSC$","NPC OCR", annot_list)
  annot_list <- gsub("^ASoC_glut$","iN-Glut ASoC", annot_list)
  annot_list <- gsub("^ASoC_npc$","NPC ASoC", annot_list)
  return(annot_list)
}

## combine S-LDSC enrichment results across traits
combine_sldsc_traits <- function(trait_name_list, dir_results, baseline){
  registerDoParallel(cores = 10)
  
  result_sLDSC <- foreach(trait = trait_name_list, .combine = rbind)%dopar%{
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "CN_all_peaks.narrowPeak.cleaned.hg19.merged", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.CN <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.CN$Category <- "CN"
    
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "DN_all_peaks.narrowPeak.cleaned.hg19.merged", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.DN <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.DN$Category <- "DN"
    
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "GA_all_peaks.narrowPeak.cleaned.hg19.merged", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.GA <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.GA$Category <- "GA"
    
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "ips_all_peaks.narrowPeak.cleaned.hg19.merged", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.ips <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.ips$Category <- "ips"
    
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "NSC_all_peaks.narrowPeak.cleaned.hg19.merged", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.NSC <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.NSC$Category <- "NSC"
    
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "ASoC_glut_anno_hg19", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.ASoC_glut <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.ASoC_glut$Category <- "ASoC_glut"
    
    sldsc_results <- read.table(paste0(dir_results,"/", trait, "/", baseline, "/", trait,"_", "ASoC_npc_anno_hg19", "_", baseline, ".results"), header = T, stringsAsFactors = F)
    sldsc.ASoC_npc <- sldsc_results[sldsc_results$Category == "L2_1",]
    sldsc.ASoC_npc$Category <- "ASoC_npc"
    
    sldsc.combined <- rbind(sldsc.CN, sldsc.DN, sldsc.GA, sldsc.ips, sldsc.NSC, sldsc.ASoC_glut, sldsc.ASoC_npc)
    sldsc.combined <- cbind(Disease = trait, sldsc.combined)
    sldsc.combined
  }
  return(result_sLDSC)
}

baseline <- "baselineLDv1.1"

dir_results <- "/project2/xinhe/kevinluo/ldsc/results/sLDSC_neuron_ATACseq_examples/"
result_sLDSC <- combine_sldsc_traits(trait_name_list, dir_results, baseline)

result_sLDSC$Category <- change_annot_names(result_sLDSC$Category)

result_sLDSC$Category <- factor(result_sLDSC$Category, levels = rev(annot_list) )
result_sLDSC$Color <- factor(result_sLDSC$Category, levels = annot_list)

Enrichment

DT::datatable(format(result_sLDSC[,1:7], digits = 2), options = list(scrollX = TRUE, keys = TRUE, pageLength = length(annot_list)),rownames = F)

ggplot_enrichment(result_sLDSC, title = "", xlim = c(0,50))

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
0334665	kevinlkx	2020-07-08

log2 Enrichment

ggplot_log2_enrichment(result_sLDSC, title = "")

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
0334665	kevinlkx	2020-07-08

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] doParallel_1.0.14 iterators_1.0.12  foreach_1.5.0     ggplot2_3.3.0    
[5] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6      compiler_3.5.1    pillar_1.4.4      later_1.0.0      
 [5] git2r_0.27.1      tools_3.5.1       digest_0.6.25     jsonlite_1.6     
 [9] evaluate_0.14     lifecycle_0.2.0   tibble_3.0.1      gtable_0.3.0     
[13] pkgconfig_2.0.3   rlang_0.4.6       shiny_1.4.0.2     crosstalk_1.0.0  
[17] yaml_2.2.0        xfun_0.14         fastmap_1.0.1     withr_2.1.2      
[21] stringr_1.4.0     dplyr_0.8.5       knitr_1.28        htmlwidgets_1.5.1
[25] fs_1.3.1          vctrs_0.3.0       DT_0.13           rprojroot_1.3-2  
[29] grid_3.5.1        tidyselect_0.2.5  glue_1.4.1        R6_2.4.1         
[33] rmarkdown_2.1     farver_2.0.3      purrr_0.3.4       magrittr_1.5     
[37] whisker_0.4       codetools_0.2-15  backports_1.1.7   scales_1.1.1     
[41] promises_1.1.0    htmltools_0.4.0   ellipsis_0.3.1    assertthat_0.2.1 
[45] xtable_1.8-4      mime_0.9          colorspace_1.4-1  httpuv_1.5.3.1   
[49] labeling_0.3      stringi_1.4.6     munsell_0.5.0     crayon_1.3.4