IFAM: Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information

IFAM: Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information | 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 IFAM: Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information Xiaolei Liu, Tang Zhenshuang, Xiong Xiong, HaoHao Zhang, Yin Dong, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7181414/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 Genomic prediction which makes use of genome-wide genetic markers to predict complex traits had made great achievements during the past decade. With the development of omics techniques, the number of functional genomic annotations increased significantly, and leveraging this information in statistical models can potentially improve prediction performance. However, to effectively utilize the vast variety of functional annotations still faces big challenges. Herein, we developed an adaptive model named ‘IFAM’, which extends the linear mixed model with multiple random effects to accommodate massive types of functional annotations to improve the genomic prediction accuracy for complex traits. The IFAM yielded notable improvements on prediction accuracy across 20 traits from diverse datasets compared with the baseline GBLUP model. Briefly, IFAM achieved an average improvement of 9.43%, 6.25%, and 4.61% at the WTCCC1, UK Biobank, and pig datasets, respectively. Our findings highlight the effectiveness of integrating functional annotations to improve accuracy of genomic predictions. Biological sciences/Genetics/Animal breeding Biological sciences/Genetics/Quantitative trait Genomic prediction Functional annotation Complex traits IFAM Full Text Additional Declarations There is NO Competing Interest. Supplementary Files IFAMSupplementary.pdf Supplementary information for main text 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. 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-7181414","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493904189,"identity":"b9601d5f-6223-4fe8-b578-34464ee8ca27","order_by":0,"name":"Xiaolei 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