Skip to content

hclimente/gwas-tools

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Aug 4, 2021
Sep 26, 2018

gwas-tools

Docker Pulls

This repository contains pipelines for common use-cases when dealing with GWAS datasets, from data preprocessing to biomarker discovery.

Installation

The easiest way to install gwas-tools is cloning the repository, and adding the bin folder to your path:

git clone git@github.com:hclimente/gwas-tools.git
export PATH=$PATH:$PWD/gwas-tools/bin

The pipelines are written in Nextflow, and makes use of multiple tools (see Dependencies). These tools need to be installed independently on a per-pipeline basis. However, those that can be distributed are included in a Docker image, which can be used adding the parameter '-with-docker hclimente/gwas-tools' or '-with-singularity hclimente/gwas-tools'.

Functions

GWAS

Data preprocessing

  • Impute a dataset: impute --bfile test/data/example --strand_info test/data/strand_info.txt --population EUR -with-docker hclimente/gwas-tools
  • Run VEGAS2: vegas2.nf --bfile test/data/example --gencode 31 --genome 37 --buffer 50000 --vegas_params '-top 10' -with-docker hclimente/gwas-tools
  • Map SNPs to GENCODE genes: snp2gene.nf --bim test/data/example.map --genome GRCh38 -with-docker hclimente/gwas-tools

Network-guided GWAS

We adapted and benchmarked multiple algorithms for the detection of SNPs associated to a phenotype. If you use any of the following algorithms, please cite the following article:

Climente-González H, Lonjou C, Lesueur F, GENESIS study group, Stoppa-Lyonnet D, et al. (2021) Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer. PLOS Computational Biology 17(3): e1008819. https://doi.org/10.1371/journal.pcbi.1008819

The available methods are:

  • dmGWAS: dmgwas.nf --vegas scored_genes.top10.txt --tab2 test/data/interactions.tab2 -with-docker hclimente/gwas-tools
  • heinz: heinz.nf --vegas scored_genes.top10.txt --tab2 test/data/interactions.tab2 --fdr 0.5 -with-docker hclimente/gwas-tools
  • HotNet2: hotnet2.nf --scores scored_genes.top10.txt --tab2 test/data/interactions.tab2 --hotnet2_path hotnet2 --lfdr_cutoff 0.125 -with-docker hclimente/gwas-tools
  • LEAN: lean.nf --vegas scored_genes.top10.txt --tab2 test/data/interactions.tab2 -with-docker hclimente/gwas-tools
  • SConES: old_scones.nf --bfile test/data/example.map --network gi --snp2gene test/data/snp2gene.tsv --tab2 test/data/interactions.tab2 -with-docker hclimente/gwas-tools
  • Sigmod: sigmod.nf --sigmod SigMod_v2 --vegas scored_genes.top10.txt --tab2 test/data/interactions.tab2 -with-docker hclimente/gwas-tools

Epistasis detection

Network-guided epistasis detection

We developed a modular method to discover epistasis along the edges of a biological network. This facilitates the interpretability of the results and allows to find interactions that would otherwise be overcome by the multiple test burden. If you use this algorithm, please cite the following article:

Duroux D, Climente-González H, Azencott C-A, Van Steen K (2021) Interpretable network-guided epistasis detection. In press. https://doi.org/10.1101/2020.09.24.310136

Usage:

network_epistasis.nf --bfile ../test/data/example --tab2 ../test/data/interactions.tab2 --snp2gene ../test/data/snp2gene.tsv --nperm 10

Dependencies

Tool Docker'd License
AntEpiSeeker No ?
BEAM No ?
BEDOPS Yes GPLv2
Biofilter No ?
GTOOL No Copyright
HotNet2 No Copyright
IMPUTE No Copyright
liftOver No Copyright
MB-MDR No ?
PLINK 1.90 Yes GPLv3
SMMB No Freeware
VEGAS2v02 Yes GPLv3
R::biglasso Yes GPLv3
R::bigmemory Yes LGPLv3
R::CASMAP Yes GPLv2
R::BioNet Yes GPLv2
R::dmGWASv3 Yes GPLv2
R::igraph Yes GPLv3
R::LEANR Yes GPLv3
R::martini Yes MIT
R::ranger Yes GPLv3
R::SigModv2 No ?
R::SKAT Yes GPLv3
R::snpStats Yes GPLv3

About

🧬 Containerized pipelines for GWAS

Resources

License

Stars

Watchers

Forks