Predicting Maize Hybrid Performance with Machine Learning and a Locus-Specific Degree of Dominance Transformation

Predicting Maize Hybrid Performance with Machine Learning and a Locus-Specific Degree of Dominance Transformation | 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 Predicting Maize Hybrid Performance with Machine Learning and a Locus-Specific Degree of Dominance Transformation Bright Enogieru Osatohanmwen, Indalécio Cunha Vieira Júnior, Mahmood Gholami, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6002495/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The genetic architecture of a trait plays a vital role in the predictive ability of genomic models. While classical models dominate plant breeding, ML methods are gaining traction for their superior handling of non-linear effects. This study assessed ML and classical models incorporating a novel locus-specific weighted dominance effect transformation matrix for genomic prediction in hybrid maize. Five models were compared: (1) XGBoost combined with locus-specific weighted dominance, (2) XGBoost only, (3) GBLUP model with additive, (4) GBLUP model including additive and dominance, (5) GBLUP combined with locus-specific weighted dominance. The models were evaluated using two simulated maize populations (polygenic scenario and oligogenic scenario) and a real maize hybrid population. Aiming to encompass a range of diverse genetic architectures, the assessment of the real maize population focused on their performance across several traits, including grain yield, test weight, ear height, plant height, Pollen DAP, Silk DAP, and grain moisture. The results show that the transformation with dominance effects did not improve ML methods, and while ML models performed better than the GBLUP model with additive effects only and the GBLUP model including additive and dominance effects, the GBLUP Transformed model outperformed all others, including ML models across the three levels of heritability in the polygenic scenarios, demonstrating the superiority of genomic prediction with transformed GBLUP for polygenic traits. This cannot be said for oligogenic scenarios, as ML models outperformed all other models in oligogenic scenarios. Also, we observed that the performance of genomic models generally decreases as dominance increases. Our findings contribute to optimizing breeding strategies and advancing genomic prediction methodologies. Machine learning dominance effects maize yield genomic prediction gradient boosting genetic architecture Full Text Supplementary Files SupplementaryDataDominancePaper.xlsx Cite Share Download PDF Status: Posted 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-6002495","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436536911,"identity":"775a2371-fd8a-4e1c-ad50-0eef997ab21e","order_by":0,"name":"Bright Enogieru Osatohanmwen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAyidAKVtgJix8QBhLQlwLWkgLQ0kaTkMJvFqMWdgPvzh5w+bPP7Z3WkSH3ect1vbfhhoS41NNC4tlg1saZI9CWnFEnfObpOceeZ28rYziUAtx9JyG3A57ACPGQNPwuHEhhu5227ztt1ONjsA1MLYcBifFuOPf4Ba5oO0/G07l2x2/iFBLQbSIFs2gLQwth2wM7tBwBbLZrY0aZm0tMSNN3K3/+xtS04wuwG0JQGPX8zZmw9/fGNjkzjvRu5mg59tdvZm59MfPvhQY4NTCwMzGj8RrDIBl3JswJ4UxaNgFIyCUTAyAABpRWlKodyj+QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0006-0921-3143","institution":"University of Göttingen: Georg-August-Universitat Gottingen","correspondingAuthor":true,"prefix":"","firstName":"Bright","middleName":"Enogieru","lastName":"Osatohanmwen","suffix":""},{"id":436536912,"identity":"077e9ec6-465a-4df7-83f3-3a11e22f0f4d","order_by":1,"name":"Indalécio Cunha Vieira Júnior","email":"","orcid":"","institution":"KWS: KWS SAAT SE \u0026 Co KGaA","correspondingAuthor":false,"prefix":"","firstName":"Indalécio","middleName":"Cunha Vieira","lastName":"Júnior","suffix":""},{"id":436536913,"identity":"91247c15-11e0-4d8b-a729-136568bfa820","order_by":2,"name":"Mahmood Gholami","email":"","orcid":"","institution":"KWS: KWS SAAT SE \u0026 Co KGaA","correspondingAuthor":false,"prefix":"","firstName":"Mahmood","middleName":"","lastName":"Gholami","suffix":""},{"id":436536914,"identity":"684844bf-f4d9-4ea7-a619-939a276ba933","order_by":3,"name":"Cathy C. 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While classical models dominate plant breeding, ML methods are gaining traction for their superior handling of non-linear effects. This study assessed ML and classical models incorporating a novel locus-specific weighted dominance effect transformation matrix for genomic prediction in hybrid maize. Five models were compared: (1) XGBoost combined with locus-specific weighted dominance, (2) XGBoost only, (3) GBLUP model with additive, (4) GBLUP model including additive and dominance, (5) GBLUP combined with locus-specific weighted dominance. The models were evaluated using two simulated maize populations (polygenic scenario and oligogenic scenario) and a real maize hybrid population. Aiming to encompass a range of diverse genetic architectures, the assessment of the real maize population focused on their performance across several traits, including grain yield, test weight, ear height, plant height, Pollen DAP, Silk DAP, and grain moisture. The results show that the transformation with dominance effects did not improve ML methods, and while ML models performed better than the GBLUP model with additive effects only and the GBLUP model including additive and dominance effects, the GBLUP Transformed model outperformed all others, including ML models across the three levels of heritability in the polygenic scenarios, demonstrating the superiority of genomic prediction with transformed GBLUP for polygenic traits. This cannot be said for oligogenic scenarios, as ML models outperformed all other models in oligogenic scenarios. Also, we observed that the performance of genomic models generally decreases as dominance increases. 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