s-LDSC example analysis using neuron ATAC-seq data

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Last updated: 2020-07-08

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Knit directory: analysis_pipelines/

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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, 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))

log2 Enrichment

ggplot_log2_enrichment(result_sLDSC, title = "")

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