Genomic relationship-aware sparse multi-environment trial design for optimizing breeding plot allocation

Genomic relationship-aware sparse multi-environment trial design for optimizing breeding plot allocation | 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 Genomic relationship-aware sparse multi-environment trial design for optimizing breeding plot allocation Sikiru Adeniyi Atanda, Garrett Raymon, Hannah Worral This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9636159/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 Phenotyping capacity remains a major constraint on the effective use of genomic selection in plant breeding. Sparse multi-environment trials (METs) can expand candidate evaluation under fixed resources but create a design trade-off: plots assigned to replication, overlap, or checks cannot also be used to observe new genotype-environment combinations. We developed GRACE-MET (Genomic Relationship-Aware Cross-Environment design for Multi-Environment Trials), a pre-phenotyping framework that treats sparse MET layout as a genomic resource-allocation problem. GRACE-MET uses marker-derived genomic relationships, multi-environment GBLUP reliability criteria, and field-layout constraints to rank candidate trial designs before planting. We evaluated GRACE-MET using empirical wheat, maize Genomes to Fields, and pea yield benchmarks, together with 50-repeat AlphaSimR simulations spanning family structure, genotype-by-environment interaction, overlap rate, partial replication, and field-layout error. Across empirical datasets, the best allocation strategy depended on environment transferability and the strength of non-genomic baselines. Low- or no-overlap designs were often competitive in wheat and maize, whereas the low-transfer pea benchmark favored broader local genotype-environment coverage over increased overlap. In known-truth simulations, family- and GRM-informed sparse designs improved missing-cell accuracy over non-genomic incomplete-block allocation in 33 of 36 settings, and family/GRM-informed P-Rep improved over conventional P-Rep in nearly all tested settings. However, P-Rep did not consistently outperform non-replicated sparse baselines because replicated plots displaced unique observations. These results show that sparse MET design is context-dependent. GRACE-MET provides a quantitative framework for comparing coverage, genomic connectedness, local precision, and environment-specific transferability before field resources are committed. genomic selection sparse testing multi-environment trials genotype-by-environment interaction partially replicated design experimental design breeding resource allocation Full Text Additional Declarations No competing interests reported. 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. 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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-9636159","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638849850,"identity":"133a6a0e-af67-4ff9-b03c-c42e721427e5","order_by":0,"name":"Sikiru Adeniyi Atanda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZjaGAzC2BEMFA2MDiMVDjBYesJYzxGhhYINQYC2MbURokW9nSzxc2cYgb89+9uCNn/PuyM6fkcD44G0bbi0Gh9kOHDzbxmDYw5OXbNm77ZnxhhsJzIZz8WlhZm842Ah0Tw9DjpkE77bDiRskEtikefFokW+GaLHv4X9jJvl3zuFEoMPYf+PTwgByGFBLYo9Ejpk0b8PhxIYbCWzM+LQA/ZJwsOGcRHLPjTfG1jLHDhtvOPOwWXLOOTwO6z9m/LGhzMa2vT/H8OabmsOy89uTD354U4bHYRAggcyBRM0oGAWjYBSMAgoAAJ4qUzp8xi3SAAAAAElFTkSuQmCC","orcid":"","institution":"North Dakota State University","correspondingAuthor":true,"prefix":"","firstName":"Sikiru","middleName":"Adeniyi","lastName":"Atanda","suffix":""},{"id":638849851,"identity":"3d80b571-d09a-45c4-b572-7b379f884e10","order_by":1,"name":"Garrett Raymon","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Garrett","middleName":"","lastName":"Raymon","suffix":""},{"id":638849852,"identity":"7f062a83-e530-4ab0-9422-b306bb799c5d","order_by":2,"name":"Hannah Worral","email":"","orcid":"","institution":"North Dakota State University","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Worral","suffix":""}],"badges":[],"createdAt":"2026-05-07 02:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9636159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9636159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109140705,"identity":"5f21a752-d2e5-4a01-9ced-89f67c48c150","added_by":"auto","created_at":"2026-05-13 02:27:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":716349,"visible":true,"origin":"","legend":"","description":"","filename":"GRACEMETAtandaetal562026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9636159/v1_covered_b448b956-fe3d-4e4f-85ec-68cc091cb90d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic relationship-aware sparse multi-environment trial design for optimizing breeding plot allocation","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":"genomic selection, sparse testing, multi-environment trials, genotype-by-environment interaction, partially replicated design, experimental design, breeding resource allocation","lastPublishedDoi":"10.21203/rs.3.rs-9636159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9636159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhenotyping capacity remains a major constraint on the effective use of genomic selection in plant breeding. 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