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RTIInternational / ld-regression-pipeline

WDL workflow for running LD-regression of GWAS summary statistics against one or more phenotypes on interest

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LD-Regression Workflow

Documentation for running ld-score regression between GWAS summary statistics from a reference phenotype and set of phenotypes of interest.

Pre-requisites

  1. Install Cromwell if you haven't already

  2. Configure the AWS CLI for use with CODE AWS group.

    • Configure with the secret key associated with the CODE AWS account

  3. Clone local copy of ld-regression-pipeline to download this repository to your local machine

    git clone https://github.com/RTIInternational/ld-regression-pipeline.git

Voila! You're ready to get started running the pipeline.

More detailed running instructions for WDL/Cromwell workflows are found in the tutorial for the metaxcan pipeline here

Workflow overview

LD-Hub and the associated LDSC software package provides a suite of tools designed to perform LD-score regression between GWAS outputs of two phenotypes.

This document describes an automated WDL workflow designed to perform LD regression of a reference phenotype against one or more phenotypes using the following high level steps:

  1. Transform GWAS summary statistics into summary statistics format
  2. Reformat summary statistics using munge_sumstats.py
  3. Perform LD-regression between the reference and all traits of interest using ldsc.py
  4. Combine results and plot in a single graph (below)
LD regression output

Workflow inputs

The following are required for the main phenotype and each phenotype you want to compare using ld-regression:

  1. Phenotype name
  2. Plot label for how you want the phenotype to be labeled on the output graph
  3. path to a GWAS summary statistics output file
  4. 1-based column index for each of the following in the GWAS input file:
    • id col
    • chr col
    • pos col
    • effect allele col
    • ref allele col
    • pvalue col
    • Effect column (e.g. beta, z-score, odds ratio)
    • Either the number of samples in the cohort or the sample size column index in the GWAS input
  5. Effect column type in the GWAS file (BETA, Z, OR, LOG_ODD)
  6. Plot group for grouping phenotypes into larger trait groups on final output plot

Detailed Workflow Steps

  1. Transform GWAS in summary statistics
    • Split by chr if necessary
      • Put columns in standardized order and give standard colnames (e.g. SNP, A1, A2, BETA, etc.)
      • Convert to 1000g ids
      • Replace 1000g ids with only rsIDs
  2. Reformat summary statistics using munge_sumstats.py
    • For each chr:
      • Call munge_sumstats.py
      • Remove empty variants for each chromosome
    • Re-combine into single munged sumstats file
  3. Perform LD-regression between the reference and all traits of interest using ldsc.py
    • For each phenotype you want to regress against reference phenotype
      • untar ld reference panel files
      • Call LDSC.py with --rg option to get logfile with regression statistics
  4. Combine results and plot in a single graph
    • Parse statistics from log files generated by LDSC
    • Combine into single table and add plot information (e.g. plot_label, plot_group)
    • Make plot of rg values returned for each phenotype with 95% CI error bars

Creating your input file

This repo provides a custom docker tool designed to help generate the json-input file needed to run the pipeline. To generate your analysis-ready input you need to:

  1. Fill out your workflow template file
  2. Create a pheno info excel spreadsheet containing info about phenotypes you want to compare
  3. Use generate_ld_regression_input_json.py tool to create your final input file

Fill out your workflow template file

Start with the json_input/full_ld_regression_wf_template.json and fill in the following values unique to your analysis:

  1. Give your analysis a name

     "full_ld_regression_wf.analysis_name": "ftnd_test"

  2. Set the name of your ref trait.

     "full_ld_regression_wf.ref_trait_name" : "ftnd",

  3. Specify paths to GWAS summary files (22 in total; 1 per chr) for your main reference trait you wish to compare to other phenotypes

     "full_ld_regression_wf.ref_sumstats_files": 
 [
 "s3://rti-nd/META/1df/boneyard/20181108/results/ea/20181108_ftnd_meta_analysis_wave3.eur.chr1.exclude_singletons.1df.gz",
 "s3://rti-nd/META/1df/boneyard/20181108/results/ea/20181108_ftnd_meta_analysis_wave3.eur.chr2.exclude_singletons.1df.gz",
 ...
 "s3://rti-nd/META/1df/boneyard/20181108/results/ea/20181108_ftnd_meta_analysis_wave3.eur.chr22.exclude_singletons.1df.gz",
 ]

  4. Inspect your GWAS summary files to find the correct indices for each required column index (1-based so the first column has index 1)

     "full_ld_regression_wf.ref_id_col":             1,
 "full_ld_regression_wf.ref_chr_col":            2,
 "full_ld_regression_wf.ref_pos_col":            3,
 "full_ld_regression_wf.ref_effect_allele_col":  4,
 "full_ld_regression_wf.ref_ref_allele_col":     5,
 "full_ld_regression_wf.ref_beta_col":           6,
 "full_ld_regression_wf.ref_pvalue_col":         8,

  5. If your GWAS summary stats file has a sample size column, specify it's index

     "full_ld_regression_wf.ref_num_samples_col": 11

  6. Otherwise, provide the number of samples in the cohort (will be the same for all SNPs)

     "full_ld_regression_wf.ref_num_samples" : 45233

  7. Specify the effect type and the zero value as you would pass it to munge_sumstats.py

     "full_ld_regression_wf.ref_signed_sumstats" : "BETA,0"

  8. Provide paths to the reference ld-reference panel bz2 tarball passed to LDSC.py "full_ld_regression_wf.ref_ld_chr_tarfile": "s3://clustername--files/eur_w_ld_chr.tar.bz2",

  9. Provide path to a merge_allele snplist needed for munge_sumstats.py

     "full_ld_regression_wf.merge_allele_snplist": "s3://clustername--files/w_hm3.snplist",

Create a pheno info excel spreadsheet

Example pheno input file here

The pheno info excel spreadsheet contains information for each phenotype to compare per row.

The format requires the following column names (explanation in parentheses):

  1. trait (Trait Name)
  2. plot_label (Label to be used on plots)
  3. sumstats_path (Path to GWAS summary stats file)
  4. category (Group label for plotting)
  5. sample_size (GWAS sample size)
  6. effect_type (Effect type; accepts Beta, Z, OR, log_odd)
  7. w_ld_chr (Path to LD reference tarball to be used for LDSC.py)
  8. id_col (1-based column index of snp id in GWAS sumstats file)
  9. chr_col (chr column index)
  10. pos_col (position index)
  11. effect_allele_col (effect allele index)
  12. ref_allele_col (ref allele index)
  13. effect_col (effect score index)
  14. pvalue_col (pvalue index)
  15. sample_size_col (sample size column index if present)

Note: All phenotypes must have either sample_size or sample_size_col but do not need to have both.

Use generate_ld_regression_input_json.py to create final input file

Create a folder that contains the input template and your phenotype file

mkdir /path/to/workflow_inputs
cp json_input/full_ld_regression_wf_template.json workflow_inputs
cp phenotype_file.xlsx workflow inputs

Run inside docker image to produce final input file (Note: output gets printed to stdout)

docker run -v "/path/to/workflow_inputs:/data/" rticode/generate_ld_regression_input_json \
    --json-input /data/full_ld_regression_wf_template.json \
    --pheno-file /data/phenotype_file.xlsx > /data/final_wf_inputs.json

Now the analysis-ready input file is ready to run and will be located at /path/to/workflow_inputs/final_wf_inputs.json

Running an analysis

First, make a zip file of the git repo you cloned so Cromwell can handle the local WDL imports:

# Change to directory immediately above metaxcan-pipeline repo
cd /path/to/ld-regression-pipeline
cd ..
# Make zipped copy of repo somewhere
zip --exclude=*var/* --exclude=*.git/* -r /path/to/workflow_inputs/ld-regression-pipeline.zip ld-regression-pipeline

Now run either via command line:

java -Dconfig.file=/path/to/aws.conf -jar cromwell-36.jar run \
    workflow/plot_ld_regression_wf.wdl \
    -i /path/to/workflow_inputs/final_wf_inputs.json \
    -p /path/to/workflow_inputs/ld-regression-pipeline.zip

Or via an API call to a Cromwell server on AWS:

curl -X POST "http://localhost:8000/api/workflows/v1" -H "accept: application/json" \
-F "workflowSource=@/full/path/to/workflow/full_ld_regression_wf.wdl" \
-F "workflowInputs=@"/path/to/workflow_inputs/final_wf_inputs.json \
-F "workflowDependencies"=@/path/to/workflow_inputs/ld-regression-pipeline.zip

Description of subworkflows

There are a number of workflows if you're interested in running only part of the pipeline manually:

  • ldsc_preprocessing_wf.wdl: run the pipeline but stop after munge_sumstats.py
  • munge_phenotype_sumstats_wf.wdl: run a single GWAS sumstats file through the munge_sumstats.py pipeline
  • munge_sumstats_wf.wdl: run a single GWAS sumstats file that has already been split by chr through the munge_sumstats.py pipeline
  • plot_ld_regression_wf.wdl: Merge and plot results from one or more ld-regression analyses
  • single_ld_regression_wf.wdl: Run LD-regression only of the ref trait against a single trait

Contact

For any questions, comments, concerns, or bugs, send me an email or slack and I'll be happy to help.

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WDL workflow for running LD-regression of GWAS summary statistics against one or more phenotypes on interest

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