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Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we identified a set of positive control genes for 12 common diseases and traits that cause a Mendelian form of the disease or are the target of a medicine used for disease treatment. We then identified a simple set of genomic fea-tures enriching GWAS single nucleotide variants for these positive control genes. Using these features, we trained and validated the Effector Index (Ei), a causal gene mapping algorithm us-ing the 12 common diseases and traits. The area under Ei's receiver operator curve to identify positive control genes was 80% and area under the precision recall curve was 29%. Using an enlarged set of independently curated positive control genes for type 2 diabetes which included genes identified by large-scale exome sequencing, these areas increased to 85% and 61%, re-spectively. The best predictors were coding or transcript altering variation, distance to gene and open chromatin-based metrics. We provide the Ei algorithm for its widespread use. Ei provides a simple, understandable tool to prioritize genes at GWAS loci for functional follow-up and drug development. ### Competing Interest Statement JBR reports investigator-initiated grants from Biogen, Eli Lilly and GlaxoSmithKline, for programs unrelated to the research presented here. JBR is an advisor to GlaxoSmithKline. MMcC has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MMcC is an employee of Genentech, and a holder of Roche stock. EF is an employee and shareholder of Pfizer, Inc. CB is an employee of Regeneron Pharmaceuticals, Inc.

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