Approximating prediction error variances and reliabilities in multiple-trait genomic prediction model using Monte Carlo sampling

Approximating prediction error variances and reliabilities in multiple-trait genomic prediction model using Monte Carlo sampling | 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 Research Article Approximating prediction error variances and reliabilities in multiple-trait genomic prediction model using Monte Carlo sampling Antero Heikkilä, Ismo Strandèn, Martin Lidauer, Klaus Nordhausen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7959884/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Genomic prediction models, such as genomic best linear unbiased prediction (GBLUP), use genomic data to improve the accuracy of estimated genetic values. As the number of genotypes and traits increases, the exact calculation of prediction error variances (PEVs) and reliabilities becomes computationally infeasible due to the need to invert the coefficient matrix of the mixed model equations, whose dimension increases directly with the number of individuals and traits. The objective of this study was to evaluate the applicability of the Monte Carlo (MC) sampling method to approximate PEVs and reliabilities in a multiple-trait GBLUP framework relevant to hybrid breeding. The MC method avoids direct matrix inversion by repeatedly sampling genetic values from their assumed distributions to approximate PEVs. We applied the MC method using four previously published formulas to approximate PEVs and reliabilities. All formulas produced consistent estimates of PEVs and reliabilities, with convergence rates depending on the formula, the level of reliability, and the MC sample size. prediction error variance reliability GBLUP Monte Carlo sampling hybrid model Full Text Supplementary Files Supplementarymaterial1.pdf Supplementarymaterial2.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 03 Mar, 2026 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 27 Oct, 2025 First submitted to journal 27 Oct, 2025 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-7959884","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558126188,"identity":"88759c42-33d6-4b6e-80ff-3184ba3488cc","order_by":0,"name":"Antero 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