Selecting variant masks to improve power and replicability of gene-level burden tests

Selecting variant masks to improve power and replicability of gene-level burden tests | 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 Selecting variant masks to improve power and replicability of gene-level burden tests Trang Nguyen, Ryan Koesterer, Sean Jurgens, Peter Dornbos, Satoshi Yoshiji, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6322956/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2026 Read the published version in Nature Genetics → Version 1 posted You are reading this latest preprint version Abstract Rare coding variant association studies typically perform gene-level association tests in which variants are filtered (or “masked”) and aggregated based on functional annotation and allele frequency. As there is little research and no consensus regarding masking strategies to use, we investigated the impact of masking strategies on gene-level burden tests, the most widely used and interpretable type of aggregate association test. A systematic review of 234 studies catalogued 664 masks and masking strategies that rarely repeated across studies. Analyzing 54 traits within 189,947 UK Biobank exomes, we show that the number of significant associations greatly depends on the masking strategy employed (ranging from 58 to 2,523 associations) and, consequently, separate published analyses of this dataset report minimally overlapping associations (<30%). By empirically determining mask combinations that maximize the number of significant associations, we propose masking strategies that detect twice as many significant low-frequency and rare variant associations as the “average” strategies previously employed, with consistent performance across many traits. Our analyses demonstrate the inconsistency of previously used variant masking strategies and provide a simple solution to increase power and replicability in future studies. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics/Genetic association study Biological sciences/Genetics/Population genetics Full Text Additional Declarations Yes there is potential Competing Interest. P.D. is an employee and stockholder of Regeneron Pharmaceuticals. The remaining authors declare no conflicts of interest relevant to this study. Supplementary Files NatGenSupplementaryTables.xlsx Supplementary Tables NatGenSupplementaryFigures.pdf Supplementary Figures Cite Share Download PDF Status: Published Journal Publication published 08 May, 2026 Read the published version in Nature Genetics → 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6322956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441894909,"identity":"ec97006d-9bee-41c6-84be-d68c8c276fb1","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":441894910,"identity":"4475c928-f3d5-4075-b0e3-991bd6bf5afb","order_by":1,"name":"Ryan Koesterer","email":"","orcid":"","institution":"Broad Institute of MIT 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