Locus-Specific Degree of Dominance Transformed XGBoost coupled with Environmental Variables for Genomic Prediction in Hybrid Maize

Locus-Specific Degree of Dominance Transformed XGBoost coupled with Environmental Variables for Genomic Prediction in Hybrid Maize | 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 Locus-Specific Degree of Dominance Transformed XGBoost coupled with Environmental Variables for Genomic Prediction in Hybrid Maize Manavi Shrestha, Henry Munroe, Ahmad Reza Sharifi, Bright Enogieru Osatohanmwen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9022823/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 Genomic prediction accelerates breeding by enabling early selection of superior genotypes. To improve prediction accuracy, we evaluated the integration of locus-specific degrees of dominance with environmental covariates for maize grain yield and plant height. Four models were used: (i) Bayesian Model (Bayes A & Bayesian Ridge Regression) + Environment, (ii) Locus-specific Dominance Transformed Bayesian model + Environment, (iii) XGBoost + Environment, (iv) Locus-specific Dominance Transformed XGBoost + Environment. Model performance was assessed using three cross-validation strategies: Leave One Year Out (LOYO), Rolling Year, and Leave One Environment Out (LOEO). XGBoost outperformed the Bayesian model under LOYO and LOEO cross-validation for both yield and plant height, showing higher predictive correlations and lower Root Mean Squared Error (RMSE). However, under rolling year cross-validation, the Bayesian model demonstrated superior predictive performance compared to XGBoost. Locus-specific dominance transformation improved the Bayesian model’s RMSE by 0.6% and 0.9%, and the correlation improved by 0.9% and 0.85% for yield and plant height in LOEO cross-validation. The dominance transformation did not improve the machine learning model’s accuracy but enhanced stability. However, the transformation did not improve prediction accuracy in LOYO or rolling year cross-validation. The performances of prediction models are impacted by training population size, overlapping hybrids, and the number of training years, mainly machine learning models. Bayesian models may suit small training populations and overlapping hybrids, whereas machine learning models are preferable for large-scale datasets. Locus-specific degree of dominance Environmental Covariates Genomic Prediction Bayesian model Machine Learning XGBoost Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.docx 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-9022823","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602008761,"identity":"c17596f0-1150-4eec-881e-6431c405dd2a","order_by":0,"name":"Manavi Shrestha","email":"data:image/png;base64,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","orcid":"","institution":"University of Göttingen","correspondingAuthor":true,"prefix":"","firstName":"Manavi","middleName":"","lastName":"Shrestha","suffix":""},{"id":602008762,"identity":"b5d6da09-14aa-4cea-8a0e-6d28f27e67f5","order_by":1,"name":"Henry Munroe","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Munroe","suffix":""},{"id":602008763,"identity":"dc4f3c4e-62b5-47c2-a43a-611532144813","order_by":2,"name":"Ahmad Reza Sharifi","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Reza","lastName":"Sharifi","suffix":""},{"id":602008764,"identity":"3c4c5610-89f8-47a2-af91-ed3d28e17f8f","order_by":3,"name":"Bright Enogieru Osatohanmwen","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Bright","middleName":"Enogieru","lastName":"Osatohanmwen","suffix":""}],"badges":[],"createdAt":"2026-03-03 17:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9022823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9022823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104741588,"identity":"5d584385-b7de-4364-a84b-bae5a30194b3","added_by":"auto","created_at":"2026-03-16 16:25:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1791686,"visible":true,"origin":"","legend":"","description":"","filename":"ManaviShresthaManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9022823/v1_covered_2c491d92-c9af-4a13-8281-888aba79b3ae.pdf"},{"id":104297934,"identity":"fa6b4e63-7d98-4da0-93e0-7739e7bdb8e3","added_by":"auto","created_at":"2026-03-10 08:19:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26887,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022823/v1/648df96e563a788d10d702f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Locus-Specific Degree of Dominance Transformed XGBoost coupled with Environmental Variables for Genomic Prediction in Hybrid Maize","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Locus-specific degree of dominance, Environmental Covariates, Genomic Prediction, Bayesian model, Machine Learning, XGBoost","lastPublishedDoi":"10.21203/rs.3.rs-9022823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9022823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenomic prediction accelerates breeding by enabling early selection of superior genotypes. To improve prediction accuracy, we evaluated the integration of locus-specific degrees of dominance with environmental covariates for maize grain yield and plant height. Four models were used: (i) Bayesian Model (Bayes A \u0026amp; Bayesian Ridge Regression) + Environment, (ii) Locus-specific Dominance Transformed Bayesian model\u0026thinsp;+\u0026thinsp;Environment, (iii) XGBoost\u0026thinsp;+\u0026thinsp;Environment, (iv) Locus-specific Dominance Transformed XGBoost\u0026thinsp;+\u0026thinsp;Environment. Model performance was assessed using three cross-validation strategies: Leave One Year Out (LOYO), Rolling Year, and Leave One Environment Out (LOEO). XGBoost outperformed the Bayesian model under LOYO and LOEO cross-validation for both yield and plant height, showing higher predictive correlations and lower Root Mean Squared Error (RMSE). However, under rolling year cross-validation, the Bayesian model demonstrated superior predictive performance compared to XGBoost. Locus-specific dominance transformation improved the Bayesian model\u0026rsquo;s RMSE by 0.6% and 0.9%, and the correlation improved by 0.9% and 0.85% for yield and plant height in LOEO cross-validation. The dominance transformation did not improve the machine learning model\u0026rsquo;s accuracy but enhanced stability. However, the transformation did not improve prediction accuracy in LOYO or rolling year cross-validation. The performances of prediction models are impacted by training population size, overlapping hybrids, and the number of training years, mainly machine learning models. Bayesian models may suit small training populations and overlapping hybrids, whereas machine learning models are preferable for large-scale datasets.\u003c/p\u003e","manuscriptTitle":"Locus-Specific Degree of Dominance Transformed XGBoost coupled with Environmental Variables for Genomic Prediction in Hybrid Maize","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 08:19:46","doi":"10.21203/rs.3.rs-9022823/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f353223-9456-4d4e-8eaf-3156aac5d6fc","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 08:19:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9022823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9022823","identity":"rs-9022823","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}