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.