OmiGA: A Toolkit for Ultra-efficient Molecular Trait Analysis in Complex Populations

OmiGA: A Toolkit for Ultra-efficient Molecular Trait Analysis in Complex Populations | 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 Technical Report OmiGA: A Toolkit for Ultra-efficient Molecular Trait Analysis in Complex Populations Lingzhao Fang, Jinyan Teng, Wenjing Zhang, Wentao Gong, Jiajian Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5885802/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Molecular quantitative trait loci (molQTL) mapping is one of the most popular approaches to systematically characterize functional impacts of genomic variants, leading to advanced understanding of the regulatory mechanisms underpinning complex traits and diseases. However, when applied to high-throughput molecular phenotypes, the existing molQTL mapping tools often implement simple linear models, overlooking complex inter-individual relatedness, leading to false positives and insufficient statistical power. Here, we introduce the Omi cs G enetic A nalysis toolkit (OmiGA), an ultra-efficient linear mixed model (LMM) based toolkit, for molQTL mapping in populations with complex relatedness. Both computational simulations and real data analyses demonstrated that OmiGA outperformed the existing popular tools regarding molQTL discovery power, fine mapping of causal variants, colocalization of molQTL and trait associations, and computational efficiency. In summary, we recommend OmiGA for molQTL mapping in populations with complex relatedness, for example, those in the Farm animal Genotype-Tissue Expression (FarmGTEx) project and family-based molQTL studies in humans. Biological sciences/Genetics/Gene expression Biological sciences/Computational biology and bioinformatics/Software OmiGA FarmGTEx molQTL mapping linear mixed model complex traits Full Text Additional Declarations There is NO Competing Interest. Supplementary Files ExtendedDataFig.1.jpg Extended Data Figure 1 OmiGASupplInformation.docx Supplemwntary figures and tables ExtendedDataFig.2.jpg Extended Data Figure 2 ExtendedDataFig.3.jpg Extended Data Figure 3 ExtendedDataFig.4.jpg Extended Data Figure 4 ExtendedDataFig.5.jpg Extended Data Figure 5 ExtendedDataFig.6.jpg Extended Data Figure 6 ExtendedDataFig.7.jpg Extended Data Figure 7 nrreportingsummary.pdf reporting summary nreditorialpolicychecklist.pdf editorial policy checklist Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Nature Communications → 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-5885802","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Technical Report","associatedPublications":[],"authors":[{"id":412681155,"identity":"e55488b8-82c7-483e-a65c-1c37e1986084","order_by":0,"name":"Lingzhao 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However, when applied to high-throughput molecular phenotypes, the existing molQTL mapping tools often implement simple linear models, overlooking complex inter-individual relatedness, leading to false positives and insufficient statistical power. Here, we introduce the \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eOmi\u003c/span\u003ecs \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eG\u003c/span\u003eenetic \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eA\u003c/span\u003enalysis toolkit (OmiGA), an ultra-efficient linear mixed model (LMM) based toolkit, for molQTL mapping in populations with complex relatedness. Both computational simulations and real data analyses demonstrated that OmiGA outperformed the existing popular tools regarding molQTL discovery power, fine mapping of causal variants, colocalization of molQTL and trait associations, and computational efficiency. 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