The open targets post-GWAS analysis pipeline

Bioinformatics. 2020 May 1;36(9):2936-2937. doi: 10.1093/bioinformatics/btaa020.

Abstract

Motivation: Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data.

Results: We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource.

Availability and implementation: The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Genome-Wide Association Study*
  • Phenotype
  • Polymorphism, Single Nucleotide*
  • Quantitative Trait Loci / genetics
  • Software