Gareth Peat1,2, William Jones1,3, Michael Nuhn1,2, José Carlos Marugán1,2, William Newell2,4, Ian Dunham1,2, Daniel Zerbino1,2. 1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK. 2. Open Targets, EBI South Building, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK. 3. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK. 4. GSK, Medicines Research Center, Stevenage SG1 2NY, UK.
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.
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.
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