Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in maize

Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in 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 Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in maize Yu-Ru Chen, Ursula Frei, Thomas Lübberstedt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3823246/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 Key message Employing multi-trait and de novo GWAS in a ridge regression BLUP model increases the predictive ability of haploid induction rate of haploid inducers in maize. Ridge regression BLUP (rrBLUP) is a widely used model for genomic selection. Different genomic prediction (GP) models have their own niches depending on the genetic architecture of traits and computational complexity. Haploid inducers have unique trait performances, relevant for doubled haploid (DH) technology in maize (Zea mays L.). We herein compared the performance of single-trait (ST) and multi-trait (MT) GP models (rrBLUP, BayesB, Random Forest, and xGBoost) and employed multi-trait and de novo GWAS in the ridge regression BLUP model for four traits of interest (Days to flowering, DTF; haploid induction rate, HIR; plant height, PHT; primary branch length, PBL) of the multifamily DH inducers (DHIs), and next tested the GP models in multi-parent advanced generation inter-cross (MAGIC) DHIs. The average predictive abilities (PA) of different GP methods across traits were 0.44 to 0.65 in multifamily DHIs. ST/MT de novo GWAS rrBLUP methods increased PA of HIR when using five-fold cross-validation. In addition, MT GP models improved PA by 13% on average across traits relative to ST GP models in MAGIC DHIs. These results provide empirical evidence that employing multi-trait and de novo GWAS in rrBLUP model in genomic selection could benefit the genetic improvement of haploid inducers. genomic selection haploid inducer multi-trait ridge regression BLUP de novo GWAS predictive ability Full Text Supplementary Files ESM1.pdf ESM2.pdf 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-3823246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266763979,"identity":"ef26de4c-abfd-4d90-a1b2-5287ae3265d6","order_by":0,"name":"Yu-Ru Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYLACxgYGBn5mBN+AOC2SzUDGAYhqIrUYHCBWi8GN3GcSP3fY5BkfZ378+UPNHzkG9uZtEvi1pJtJ9p5JKzY7zGYmceCYgTEDz7EyAlrS2KQZ2w4nbjvMYMZwgM0gsUEix4wYLf8TNzezf/5w4J9BfYP8G6K0HEjcwMxjIHGwzSCBQYIHvxbJM8+YLXvbkhNnHOYpkzjbZ2zYxpNWbIFPC9/xNMYbP9vsEvv7j2/+UPFNTp6f/fDGG/i0KBxAF2HDpxwE5BsIqRgFo2AUjIJRAABbHUkNJA/plQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0229-3890","institution":"Iowa State University","correspondingAuthor":true,"prefix":"","firstName":"Yu-Ru","middleName":"","lastName":"Chen","suffix":""},{"id":266763980,"identity":"4b4fa905-7424-4999-83e5-60ef59f0401f","order_by":1,"name":"Ursula Frei","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Ursula","middleName":"","lastName":"Frei","suffix":""},{"id":266763981,"identity":"2a3a765b-4e98-45a7-a377-8bd9248dbcfd","order_by":2,"name":"Thomas Lübberstedt","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Lübberstedt","suffix":""}],"badges":[],"createdAt":"2023-12-30 05:50:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3823246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3823246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74652406,"identity":"84e0a0f4-0349-4ad3-8f76-49ee319cf5a3","added_by":"auto","created_at":"2025-01-24 10:56:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1227361,"visible":true,"origin":"","legend":"","description":"","filename":"TAGGPmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823246/v1_covered_beaa55e2-add8-4cd2-ac6f-38741c507395.pdf"},{"id":49685258,"identity":"e0fa1e14-ee6f-4c57-bedc-eacc0cca06cc","added_by":"auto","created_at":"2024-01-16 12:46:39","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":124255,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823246/v1/137d35a47472245e4279ff09.pdf"},{"id":49685259,"identity":"dfe3d975-0556-40ff-9515-4158eae9158e","added_by":"auto","created_at":"2024-01-16 12:46:39","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":138002,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823246/v1/8319fc6e20a638ab102be3c7.pdf"}],"financialInterests":"","formattedTitle":"Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in maize","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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, haploid inducer, multi-trait, ridge regression BLUP, de novo GWAS, predictive ability","lastPublishedDoi":"10.21203/rs.3.rs-3823246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3823246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eKey message\u003c/em\u003e Employing multi-trait and \u003cem\u003ede novo\u003c/em\u003e GWAS in a ridge regression BLUP model increases the predictive ability of haploid induction rate of haploid inducers in maize. Ridge regression BLUP (rrBLUP) is a widely used model for genomic selection. Different genomic prediction (GP) models have their own niches depending on the genetic architecture of traits and computational complexity. Haploid inducers have unique trait performances, relevant for doubled haploid (DH) technology in maize \u003cem\u003e(Zea mays\u003c/em\u003e L.). We herein compared the performance of single-trait (ST) and multi-trait (MT) GP models (rrBLUP, BayesB, Random Forest, and xGBoost) and employed multi-trait and \u003cem\u003ede novo\u003c/em\u003e GWAS in the ridge regression BLUP model for four traits of interest (Days to flowering, DTF; haploid induction rate, HIR; plant height, PHT; primary branch length, PBL) of the multifamily DH inducers (DHIs), and next tested the GP models in multi-parent advanced generation inter-cross (MAGIC) DHIs. The average predictive abilities (PA) of different GP methods across traits were 0.44 to 0.65 in multifamily DHIs. ST/MT \u003cem\u003ede novo\u003c/em\u003e GWAS rrBLUP methods increased PA of HIR when using five-fold cross-validation. In addition, MT GP models improved PA by 13% on average across traits relative to ST GP models in MAGIC DHIs. These results provide empirical evidence that employing multi-trait and \u003cem\u003ede novo\u003c/em\u003e GWAS in rrBLUP model in genomic selection could benefit the genetic improvement of haploid inducers.\u003c/p\u003e","manuscriptTitle":"Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in maize","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 12:46:34","doi":"10.21203/rs.3.rs-3823246/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":"4208e46e-ec09-456d-96de-87137f45915c","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T10:48:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 12:46:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3823246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3823246","identity":"rs-3823246","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}