A resource of “bottom-line” variant associations for 1,281 complex traits by integrating data across published genome-wide association studies

A resource of “bottom-line” variant associations for 1,281 complex traits by integrating data across published genome-wide association studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A resource of “bottom-line” variant associations for 1,281 complex traits by integrating data across published genome-wide association studies Trang Nguyen, Furkan Büyükgöl, Patrick Smadbeck, Jeffrey Massung, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8585052/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version 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. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics/Genetic association study Health sciences/Medical research/Genetics research Full Text Additional Declarations Yes there is potential Competing Interest. P. D. and G. A. are employees and stockholders of Regeneron Pharmaceuticals. The remaining authors declare no conflicts of interest relevant to this study. Supplementary Files BLpaperSuppTablesJan2026.xlsx Supplementary Tables BLpaperSuppFiguresJan2026.pdf Supplementary Figures Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8585052","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573659552,"identity":"f7857797-673d-469c-ab48-eec6d6f6675a","order_by":0,"name":"Trang Nguyen","email":"","orcid":"https://orcid.org/0009-0004-9413-5965","institution":"The Broad Institute of MIT and Harvard","correspondingAuthor":false,"prefix":"","firstName":"Trang","middleName":"","lastName":"Nguyen","suffix":""},{"id":573659553,"identity":"ca9c24df-dd0a-4792-893a-816a89de227d","order_by":1,"name":"Furkan Büyükgöl","email":"","orcid":"","institution":"Broad Institute of MIT 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