A resource of “bottom-line” variant associations for 1,281 complex traits by integrating data across published genome-wide association studies
Year: 2026
Type: preprint
Abstract: Through an analysis of 2,602 genome-wide association studies (GWAS) across 830 human traits, we find that most (56% of) well-studied traits have at least two published GWAS, and many (29%) have at least five. We show that the lack of an established approach for adjudicating variant association estimates across multiple published studies can lead to uncertainty and invalid inferences: using all associations ever published for a trait increases true positives (by 12%) but also false positives (by 55%) relative to using associations from the largest published GWAS for the trait. We employ a “bottom-line” procedure for meta-analyzing published GWAS while inferring and accounting for sample overlap, which identifies a more accurate and comprehensive list of associations relative to existing approaches. Five commonly used bioinformatic methods for post-GWAS analyses produce reliable results when applied to the bottom-line associations. We present these results for 1,281 human complex traits, including 1,839 single-ancestry and 576 trans-ancestry analyses, for browsing or download via the NHGRI Association to Function Knowledge Portal. This resource of “consensus” GWAS results is intended to increase replicability, reuse, and interpretation of GWAS and downstream analyses.
Source: Research Square
Authors: Trang Nguyen, Furkan Büyükgöl, Patrick Smadbeck, Jeffrey Massung, Maria Costanzo, Monica Ruiz, Peter Dornbos, Satoshi Yoshiji, Ryan Koesterer, Thanh Long Nguyen, Dongkeun Jang, Quy Hoang, Yue Ji, Aoife McMahon, Sebanti Sengupta, Xianyong Yin, Brady Ryan, Ryan Welch, Jorien Treur, Connie Bezzina, Gonçalo R. Abecasis, Michael Boehnke, Noël Burtt, Jason Flannick
Institutions: Broad Institute, Helmholtz Munich, Ludwig-Maximilians-Universität München, Boston Children's Hospital, Harvard University, Massachusetts Institute of Technology, McGill University, European Bioinformatics Institute, University of Michigan, Nanjing Medical University, Amsterdam University Medical Centers, Amsterdam UMC Location University of Amsterdam
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: In vivo and in vitro rare coding variant analyses to identify modulations of the adipocyte differentiation pathway that affect T2D risk, University of Michigan Training Program in Genomic Science, The next iteration of the AMP-T2D Knowledge Portal, The Association to Function Knowledge Portal: a genomic data resource for translating GWAS associations to biological effects, Identifying Genes for Type 2 Diabetes: FUSION
OpenAlex ID: W7125417694