The open targets post-GWAS analysis pipeline

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...

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Published in:Bioinformatics 2020-05, Vol.36 (9), p.2936-2937
Main Authors: Peat, Gareth, Jones, William, Nuhn, Michael, Marugán, José Carlos, Newell, William, Dunham, Ian, Zerbino, Daniel
Format: Article
Language:English
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Summary: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.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btaa020