An effector index to predict target genes at GWAS loci

• Published in: Human genetics Vol. 141; no. 8; pp. 1431 - 1447
• Main Authors: Forgetta, Vincenzo, Jiang, Lai, Vulpescu, Nicholas A., Hogan, Megan S., Chen, Siyuan, Morris, John A., Grinek, Stepan, Benner, Christian, Jang, Dong-Keun, Hoang, Quy, Burtt, Noel, Flannick, Jason A., McCarthy, Mark I., Fauman, Eric, Greenwood, Celia M. T., Maurano, Matthew T., Richards, J. Brent
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Biomedicine; Chromatin; Conformation; Diabetes mellitus (non-insulin dependent); Disease; Drug development; Gene Function; Gene mapping; Genes; Genetic research; Genome-wide association studies; Genomes; Genomics; Human Genetics; Metabolic Diseases; Molecular Medicine; Original Investigation; Quantitative genetics; Quantitative trait loci; Type 2 diabetes
• ISSN: 0340-6717; 1432-1203
• DOI: 10.1007/s00439-022-02434-z

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.