Reproducible Gene-Level Association Studies Workflow for Multi-Omic Analysis

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This repository contains an entire end-to-end workflow to reproduce gene-level association studies described in:

Zhou, D., Jiang, Y., Zhong, X. et al. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet 52 (2020). https://doi.org/10.1038/s41588-020-0706-2

The workflow is described in and executed by Snakemake , a management system for scalable reproducible analysis pipelines.

Requirements

Python 3.9
conda - https://docs.conda.io/en/latest/
R>=4.0 including tidyverse and bookdown packages (the latter for creating summary.pdf )

Running workflow

Clone this repository to reproduce associations and generate summary.pdf . Associations results are generated by SPrediXcan.py and saved under results/{ukbb_id}-{model}_{tissue}.csv .

$ git clone https://github.com/manzt/zhou-et-al-natgen-2020 && cd zhou-et-al-natgen-2020
$ conda env create --file environment.yml
$ snakemake --cores all # downloads all data, runs associations, and generates `summary.pdf`

Running the complete workflow will download and organize all the input data necessary to reproduce the gene-level association results described in Table S7,

GWAS summary statistics from UKBB
Pretrained Transcriptome Prediction Model databases (JTI, PrediXcan, UTMOST)
Covariance matrices of SNps within each gene model

The data are organized in the following directory structure, and associations are generated using SPrediXcan.py from MetaXcan .

data/
├── covariances/
├── GWAS/
├── weights/
└── supplementary_tables.xlsx

Individual associations

Running all the associations can take some time. If you are interested in running individual associations, you may run SPrediXcan.py for individual GWAS summary statistics / model-tissue -specific weights explicitly by matching the following pattern with a snakmake wildcard results/{ukbb_id}-{model}_{tissue}.csv .

For example,

$ snakemake --cores all results/30740_irnt-UTMOST_Muscle_Skeletal.csv

will only perform the steps necessary to produce the gene-level association output for Glucose ( 37040_irnt ) using the pretrained UTMOST weights for Muscle Skeletal tissue.

Code Snippets

shell:
  """
  cd notebooks
  Rscript -e "bookdown::render_book('index.Rmd', 'bookdown::pdf_book')"
  cd .. 
  mv notebooks/_book/_main.pdf {output}
  rmdir notebooks/_book
  """

SnakeMake From line 32 of main/Snakefile

shell:
  """
  wget https://github.com/hakyimlab/MetaXcan/zipball/{} -O tmp.zip
  unzip tmp.zip
  rm tmp.zip
  """.format(METAXCAN_HASH)

SnakeMake From line 44 of main/Snakefile

shell: "wget https://zenodo.org/record/3842289/files/{wildcards.sample}.db -O {output}"

SnakeMake From line 53 of main/Snakefile

shell: "wget https://zenodo.org/record/3842289/files/{wildcards.sample}.txt.gz -O {output}"

SnakeMake From line 57 of main/Snakefile

shell: "paste <(cut -f 2- {input}) <(wget -qO - {params} | gunzip) > {output}"

SnakeMake From line 63 of main/Snakefile

shell: "wget https://broad-ukb-sumstats-us-east-1.s3.amazonaws.com/round2/annotations/variants.tsv.bgz -O {output}"

SnakeMake From line 67 of main/Snakefile

shell: "wget https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-020-0706-2/MediaObjects/41588_2020_706_MOESM3_ESM.xlsx -O {output}"

SnakeMake From line 72 of main/Snakefile

shell:
  """
  gunzip -c {input} | head -n 1 | cut -f -6 > {output} || true
  sort <(gunzip -c {input} | cut -f -6 | sed 1d) >> {output} || true
  """

SnakeMake From line 77 of main/Snakefile

shell:
  """
  python {input.SPrediXcan} --model_db_path {input.model_db} \
    --covariance {input.covariance} \
    --gwas_file {input.gwas_file} \
    --snp_column rsid \
    --effect_allele_column alt \
    --non_effect_allele_column ref \
    --chromosome_column chr \
    --position_column pos \
    --beta_column beta \
    --se_column se \
    --pvalue_column pval \
    --freq_column minor_AF \
    --output_file {output} \
  """