Improved bivariate analysis of canola survivability against blackleg disease

Improved bivariate analysis of canola survivability against blackleg disease | 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 Improved bivariate analysis of canola survivability against blackleg disease Timothy Thavarajah, James Walter, Julian Taylor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6720944/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Canola ( Brassica napus ) is an important oilseed crop grown extensively worldwide. It is deleteriously affected by the pathogen Leptosphaeria maculans , commonly known as blackleg, causing up to 15% yield loss in Australia annually. The most effective way to manage this disease is by growing resistant varieties. Screening genotypes for blackleg resistance has typically involved deriving percentage survivability against blackleg (from plant counts at emergence and maturity) and conducting a univariate analysis. More comprehensive approaches have involved a bivariate analysis that accounts for the correlation between plant counts. This study highlights shortcomings of the bivariate approach in the literature including independence between genotypes, the restrictive assumption of three-way separable residual models and a constrained selection measure. The research objectives were to (1) incorporate genomic marker information; (2) apply a more flexible residual model; (3) develop a novel selection measure, responsiveness to blackleg disease, from the bivariate regression. To illustrate these advancements, data from disease nurseries within a commercial breeding programme were collected over three years at four locations across Australia. Moderate to strong genetic correlations were found between traits, ranging between 0.49 and 0.91. The incorporation of genomic markers benefitted the maturity count more than emergence count. Furthermore, the more flexible residual model significantly improved model fit in five experiments. Using responsiveness as selection measure produced comparable rankings with the historic survivability measure, with some re-ranking of genotypes which reflects the improved analysis through the bivariate approach. Ultimately, these results demonstrate an improvement over historic analyses, thus encouraging their adoption in canola breeding programmes to accelerate genetic gain for blackleg resistance. canola blackleg disease genomic selection linear mixed models plant breeding Full Text Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Minor revisions 07 Jul, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 27 May, 2025 Editor assigned by journal 22 May, 2025 First submitted to journal 21 May, 2025 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. 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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-6720944","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462406882,"identity":"22b3b4f2-f1ac-41b7-9945-366233b1caa8","order_by":0,"name":"Timothy Thavarajah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABWklEQVRIie2RT0sCQRTA3zKwe2g2r0+U9SvMEkhh6VdZGWgvG9FFiqIEYbsIXYWEvoIhRIcOsyzkRT3PoYOx4MmD0qXoD02hldreg/YHM7w3b368eQxAQsJfhKilVdWGsxyJAArroIOjcn3JoB9KoBT6reiOUjBegQVFtaPsU4EYpWSslKPJdQilTG2ITzdFI3Ven4xH+5hbNbiAcSWEVMOZf5jZZkE3BJq9zafrQ07wrtdON7to+3ToaI1+CCgXlSsMfKWgs8aoIATkTitj+qj56DFiqhLEKu6D/SJOSE560bP5hiUfd8fkVZVysYpnR1SEhElPz5hVLKsuQDRVYgtKqGbp+S6l0tuLsqJDbLmd32jeIlezsKDed6ndHfxUjLPe5eDAL1hGw20FI3HILckjOTra3Lo45feDx0rBsjpzXabU6Czic+cCpl+3zPFXVPz9QkJCQsJ/5h2miXlrSixV9QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8813-4542","institution":"The University of Adelaide AFW: The University of Adelaide School of Agriculture Food \u0026 Wine","correspondingAuthor":true,"prefix":"","firstName":"Timothy","middleName":"","lastName":"Thavarajah","suffix":""},{"id":462406883,"identity":"fa6f16b2-64d0-4fe1-a4c2-ef63ac830a14","order_by":1,"name":"James Walter","email":"","orcid":"","institution":"Australian Grain Technologies","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Walter","suffix":""},{"id":462406884,"identity":"eaae8df9-cf5d-47a6-81cd-8d058a0533df","order_by":2,"name":"Julian Taylor","email":"","orcid":"","institution":"The University of Adelaide AFW: The University of Adelaide School of Agriculture Food \u0026 Wine","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Taylor","suffix":""}],"badges":[],"createdAt":"2025-05-22 04:17:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6720944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6720944/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-04993-x","type":"published","date":"2025-08-22T16:29:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847242,"identity":"33cfea82-9b81-491b-a315-0411aa05f576","added_by":"auto","created_at":"2025-08-25 16:42:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2303155,"visible":true,"origin":"","legend":"","description":"","filename":"TAAGD2500456reviewer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6720944/v1_covered_2c6a149e-4393-4f51-ad4a-b3a0e443d44c.pdf"}],"financialInterests":"","formattedTitle":"Improved bivariate analysis of canola survivability against blackleg disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"canola, blackleg disease, genomic selection, linear mixed models, plant breeding","lastPublishedDoi":"10.21203/rs.3.rs-6720944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6720944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCanola (\u003cstrong\u003eBrassica napus\u003c/strong\u003e) is an important oilseed crop grown extensively worldwide. It is deleteriously affected by the pathogen \u003cstrong\u003eLeptosphaeria maculans \u003c/strong\u003e, commonly known as blackleg, causing up to 15% yield loss in Australia annually. The most effective way to manage this disease is by growing resistant varieties. Screening genotypes for blackleg resistance has typically involved deriving percentage survivability against blackleg (from plant counts at emergence and maturity) and conducting a univariate analysis. More comprehensive approaches have involved a bivariate analysis that accounts for the correlation between plant counts. This study highlights shortcomings of the bivariate approach in the literature including independence between genotypes, the restrictive assumption of three-way separable residual models and a constrained selection measure. 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