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

• Unix-based operating system (Linux or OSx. Sorry Windows folks.)
• Java v1.8 and higher (download here)
• Docker
• AWS CLI

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

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)

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

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

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

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",
   

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.

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

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

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

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

• Jesse Marks ([email protected])