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/project2/xinhe/kevinluo/ldsc/annot/annot_bed//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)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/.
https://github.com/bulik/ldsc/wiki/Partitioned-Heritability
Convert GWAS summary stats to the .sumstats format using munge_sumstats.py
See this page for details
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
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 rprojroot_1.3-2 digest_0.6.25 later_1.0.0
[5] R6_2.4.1 backports_1.1.7 git2r_0.27.1 magrittr_1.5
[9] evaluate_0.14 stringi_1.4.6 rlang_0.4.6 fs_1.3.1
[13] promises_1.1.0 whisker_0.4 rmarkdown_2.1 tools_3.5.1
[17] stringr_1.4.0 glue_1.4.1 httpuv_1.5.3.1 xfun_0.14
[21] yaml_2.2.0 compiler_3.5.1 htmltools_0.4.0 knitr_1.28