Prediction of human missense variant effects from functional evidence

Prediction of human missense variant effects from functional evidence | 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 Prediction of human missense variant effects from functional evidence Tayfun Ozcelik, Barış Kayaalp, Kerem Çil, Clément Conil, Aurélie Cobat, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7536763/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 Prediction of missense variant effects remains the critical bottleneck in disease gene identification and clinical interpretation. Current predictors rely on clinical outcomes or population patterns, rather than direct measures of functional impact, leading to limited generalizability and data circularity. We present FuncVEP, the first family of variant effect predictors trained exclusively on balanced and diverse functional data, providing a direct representation of functional effect. FuncVEP generalizes across contexts, outperforming 47 existing predictors on both clinical and functional benchmarks, improving the accuracy from 82% to 93% and reducing uncertain classifications from 11% to 2%. To illustrate its utility in gene discovery, we applied FuncVEP to 490 inborn errors of immunity genes in the UK Biobank and Mount Sinai Million Health Discoveries Program, identifying 50 novel gene–phenotype associations. FuncVEP provides a robust, scalable solution for variant interpretation, advancing both diagnostic precision and gene discovery. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Computational biology and bioinformatics/Sequence annotation Biological sciences/Computational biology and bioinformatics/Software Biological sciences/Genetics/Population genetics Variant Effect Predictor Missense Variants Inborn Errors of Immunity PheWAS Genetic Epidemiology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information SupplementaryTables.xlsx Supplementary Tables ExtendedDataFig.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7536763","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523516207,"identity":"a0e97d40-0826-4ac8-bcdf-e351251681aa","order_by":0,"name":"Tayfun 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