Multivariate Genomic Best Linear Unbiased Prediction (GBLUP) Can Improve Genomic Prediction in Mungbean

Multivariate Genomic Best Linear Unbiased Prediction (GBLUP) Can Improve Genomic Prediction in Mungbean | 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 Multivariate Genomic Best Linear Unbiased Prediction (GBLUP) Can Improve Genomic Prediction in Mungbean Mark Edward Fabreag, Shanice Van Haeften, Cassandra Pegg, Eric Dinglasan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9153120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Genomic prediction (GP) remains underexplored in mungbean despite its potential to accelerate genetic gain in breeding programs. This study evaluated the implementation of GP in mungbean using multivariate genomic best linear unbiased prediction (GBLUP), including multi-trait, multi-environment and multi-trait multi-environment models to maximize prediction accuracy. Utilizing yield (YLD), days-to-50%-flowering (DTF), and plant height (PHT) data collected from 313 diverse mungbean accessions (including accessions released and widely grown in Australia) tested across four environments and genotyped for 3,310 SNPs, we compared multivariate GBLUP models including single-trait−multi-environment, multi-trait−single-environment, and multi-trait−multi-environment against univariate single-trait−single-environment models. For multi-trait models, YLD was the primary trait, while DTF and PHT were the secondary traits. Secondary traits are correlated traits which are easy and relatively inexpensive to measure early in crop development. Cross-validation schemes implemented assessed whether secondary traits were available at the time of prediction of YLD for the validation sets (no secondary traits, NOSEC vs secondary traits available, SEC) for multi-trait models, cross-environment data availability (no cross-environment data, Scenario 1 vs cross-environment data available, Scenario 2) for multi-environment models and their factorial combinations (NOSEC/SEC × Scenario1/2) for multi-trait multi-environment models could improve prediction accuracy. Single-trait single-environment GBLUP models provided baseline prediction accuracies of 0.33 ± 0.02 to 0.61 ± 0.01 for YLD, 0.39 ± 0.02 to 0.49 ± 0.02 for DTF, and 0.38 ± 0.01 to 0.59 ± 0.01 for PHT. Multivariate approaches substantially improved prediction accuracy, with gains up to + 52.7% for YLD, + 100.22% for DTF and + 83.43% for PHT, over baseline models. The magnitude of improvement depended on trait heritability, genetic correlation between traits and between environments, and availability of information on the validation set. These findings demonstrate that genomic prediction is feasible in mungbean and can be substantially enhanced through multivariate approaches, providing a foundation for implementing genomic selection in mungbean breeding programs. genomic selection genomic best linear unbiased prediction multi-trait multi-environment Vigna radiata Full Text Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx MungbeanGPManuscriptTAGSubmissionFormat170326Supp.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 17 Mar, 2026 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-9153120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622667049,"identity":"5336c744-a567-46d1-843d-792cd5dcbc19","order_by":0,"name":"Mark Edward Fabreag","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"Edward","lastName":"Fabreag","suffix":""},{"id":622667050,"identity":"69eec995-0583-4b2e-97ff-6731014c3454","order_by":1,"name":"Shanice Van Haeften","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Shanice","middleName":"Van","lastName":"Haeften","suffix":""},{"id":622667051,"identity":"df49bcd7-6f00-4a13-9aab-213fdd8e3994","order_by":2,"name":"Cassandra Pegg","email":"","orcid":"","institution":"Commonwealth Scientific and Industrial Research Organisation","correspondingAuthor":false,"prefix":"","firstName":"Cassandra","middleName":"","lastName":"Pegg","suffix":""},{"id":622667052,"identity":"41319f66-5094-41ba-a33d-ec9a70c39d55","order_by":3,"name":"Eric Dinglasan","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Dinglasan","suffix":""},{"id":622667053,"identity":"da4caf12-0c4e-4e46-92bc-b06b22030a38","order_by":4,"name":"Millicent Smith","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Millicent","middleName":"","lastName":"Smith","suffix":""},{"id":622667054,"identity":"a81014b8-2fed-4b12-b4f2-bcf3c3055750","order_by":5,"name":"Valeria Paccapelo","email":"","orcid":"","institution":"Queensland Department of Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Paccapelo","suffix":""},{"id":622667055,"identity":"beed2801-118d-414a-93a0-d143d1ac23c3","order_by":6,"name":"Thomas Noble","email":"","orcid":"","institution":"Queensland Department of Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Noble","suffix":""},{"id":622667056,"identity":"76cd492f-203b-4e91-b1ee-741ad66b0d10","order_by":7,"name":"Merrill Ryan","email":"","orcid":"","institution":"Queensland Department of Primary Industries","correspondingAuthor":false,"prefix":"","firstName":"Merrill","middleName":"","lastName":"Ryan","suffix":""},{"id":622667057,"identity":"81221e94-e2da-45b2-8de5-c318d03403ad","order_by":8,"name":"Lee Hickey","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Lee","middleName":"","lastName":"Hickey","suffix":""},{"id":622667058,"identity":"2c250a1e-4039-4097-8e1d-a7889886ca5e","order_by":9,"name":"Ben J. 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This study evaluated the implementation of GP in mungbean using multivariate genomic best linear unbiased prediction (GBLUP), including multi-trait, multi-environment and multi-trait multi-environment models to maximize prediction accuracy. Utilizing yield (YLD), days-to-50%-flowering (DTF), and plant height (PHT) data collected from 313 diverse mungbean accessions (including accessions released and widely grown in Australia) tested across four environments and genotyped for 3,310 SNPs, we compared multivariate GBLUP models including single-trait\u0026minus;multi-environment, multi-trait\u0026minus;single-environment, and multi-trait\u0026minus;multi-environment against univariate single-trait\u0026minus;single-environment models. For multi-trait models, YLD was the primary trait, while DTF and PHT were the secondary traits. Secondary traits are correlated traits which are easy and relatively inexpensive to measure early in crop development. Cross-validation schemes implemented assessed whether secondary traits were available at the time of prediction of YLD for the validation sets (no secondary traits, NOSEC vs secondary traits available, SEC) for multi-trait models, cross-environment data availability (no cross-environment data, Scenario 1 vs cross-environment data available, Scenario 2) for multi-environment models and their factorial combinations (NOSEC/SEC \u0026times; Scenario1/2) for multi-trait multi-environment models could improve prediction accuracy. Single-trait single-environment GBLUP models provided baseline prediction accuracies of 0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 to 0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 for YLD, 0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 to 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 for DTF, and 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 to 0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 for PHT. Multivariate approaches substantially improved prediction accuracy, with gains up to +\u0026thinsp;52.7% for YLD, +\u0026thinsp;100.22% for DTF and +\u0026thinsp;83.43% for PHT, over baseline models. The magnitude of improvement depended on trait heritability, genetic correlation between traits and between environments, and availability of information on the validation set. These findings demonstrate that genomic prediction is feasible in mungbean and can be substantially enhanced through multivariate approaches, providing a foundation for implementing genomic selection in mungbean breeding programs.\u003c/p\u003e","manuscriptTitle":"Multivariate Genomic Best Linear Unbiased Prediction (GBLUP) Can Improve Genomic Prediction in Mungbean","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 14:09:03","doi":"10.21203/rs.3.rs-9153120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-09T14:57:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T09:51:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T06:06:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2026-03-18T00:48:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"73826560-5aba-4cac-8e5b-7805cf47482c","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T14:09:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 14:09:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9153120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9153120","identity":"rs-9153120","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}