An effector index to predict target genes at GWAS loci

Vincenzo Forgetta, Lai Jiang, Nicholas A. Vulpescu, Megan S. Hogan, Siyuan Chen, John A. Morris, Stepan Grinek, Christian Benner, Dong Keun Jang, Quy Hoang, Noel Burtt, Jason A. Flannick, Mark I. McCarthy, Eric Fauman, Celia M.T. Greenwood, Matthew T. Maurano, J. Brent Richards

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.

Original languageEnglish
Pages (from-to)1431-1447
Number of pages17
JournalHuman Genetics
Volume141
Issue number8
DOIs
StatePublished - Aug 2022
Externally publishedYes

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