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Test the example written in ldsc wiki for “Partitioned-Heritability”

Install LD Score Regression (LDSC) software

Please see the detail instructions: LD Score Regression (LDSC) https://github.com/bulik/ldsc

Download ldsc software

git clone https://github.com/bulik/ldsc.git
cd ldsc

Create a conda environment with LDSC’s dependencies

You might need to update numpy (and other packages) to a newer version (e.g. set numpy==1.16 or newer version)

conda env create --file environment.yml

Activate the conda environment with LDSC’s dependencies

conda activate ldsc

Test installation

If these commands fail with an error, then something as gone wrong during the installation process.

cd ldsc

python ./ldsc.py -h
python ./munge_sumstats.py -h

Download example data

Download the baseline model LD scores

wget https://data.broadinstitute.org/alkesgroup/LDSCORE/1000G_Phase1_baseline_ldscores.tgz
tar -xvzf 1000G_Phase1_baseline_ldscores.tgz

Download regression weights

wget https://data.broadinstitute.org/alkesgroup/LDSCORE/weights_hm3_no_hla.tgz
tar -xvzf weights_hm3_no_hla.tgz

Download allele frequencies (European of Phase 3 of 1000 Genomes)

wget https://data.broadinstitute.org/alkesgroup/LDSCORE/1000G_Phase1_frq.tgz
tar -xvzf 1000G_Phase1_frq.tgz

Download a list of HapMap3 SNPs

wget https://data.broadinstitute.org/alkesgroup/LDSCORE/w_hm3.snplist.bz2
bzip2 -d w_hm3.snplist.bz2

Download GIANT BMI GWAS summary statistics

wget http://portals.broadinstitute.org/collaboration/giant/images/b/b7/GIANT_BMI_Speliotes2010_publicrelease_HapMapCeuFreq.txt.gz
gunzip GIANT_BMI_Speliotes2010_publicrelease_HapMapCeuFreq.txt.gz

Partition heritability

Prepare GWAS summary stats in LDSC .sumstats format

  • Convert GWAS summary stats to the LDSC .sumstats format using munge_sumstats.py
  • ldsc wiki “Summary-Statistics-File-Format”
  • Note: you may need to add an option --chunksize 500000 to munge_sumstats.py command
python munge_sumstats.py \
--sumstats GIANT_BMI_Speliotes2010_publicrelease_HapMapCeuFreq.txt \
--merge-alleles w_hm3.snplist \
--out BMI \
--a1-inc \
--chunksize 500000

Run S-LDSC on BMI GWAS summary statistics using baseline annotations

  • Joint model: you could include multiple annotations file prefixes to run multiple annotations jointly
python ldsc.py \
--h2 BMI.sumstats.gz \
--ref-ld-chr baseline/baseline. \
--w-ld-chr weights_hm3_no_hla/weights. \
--overlap-annot \
--frqfile-chr 1000G_frq/1000G.mac5eur. \
--out BMI_baseline

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