Prioritization of Deleterious Mutations Informs Genomic Prediction and Increases the Rate of Genetic Gain in Common Bean (Phaseolus vulgaris L.), a Simulation Study

Prioritization of Deleterious Mutations Informs Genomic Prediction and Increases the Rate of Genetic Gain in Common Bean (Phaseolus vulgaris L.), a Simulation Study | 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 Prioritization of Deleterious Mutations Informs Genomic Prediction and Increases the Rate of Genetic Gain in Common Bean (Phaseolus vulgaris L.), a Simulation Study Henry Alexander Cordoba-Novoa, Valerio Hoyos-Villegas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313431/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 The study of mutations is fundamental to understanding evolution, domestication, and genetics. Characterizing mutations has potential to accelerate breeding programs through selection and purging deleterious mutations (DelMut). We investigated how predicting DelMut in breeding populations informs genomic prediction (GP) increasing the rate of genetic gain. DelMut were annotated in three independent common bean populations using a previously developed random forest (RF) model developed for common bean incorporating phylogenetic and protein information. Deleterious scores from the RF model were around 0.25, with the top 1% ( highly DelMut) of variants scoring between 0.78–0.82 among populations. All populations showed variation in the number of highly DelMut per line (max. 13–197) and in genetic load. We assessed the impact of incorporating a priori information on DelMut for variant prioritization and weighting in GP models for yield and flowering time. Stochastic simulations were conducted to evaluate how designing mating schemes based on variable numbers of DelMut per parent can affect genetic gain. Variants with higher predicted scores had significantly different effect distributions compared to random or lower-scored markers. Simulated breeding cycles showed that selecting parents with fewer highly DelMut consistently increases the rate of genetic gain depending and could be superior to phenotypic selection depending on the population. These results highlight the potential of DelMut information for variant prioritization and the optimization of common bean breeding programs. The approaches we developed can be assessed in other species to improve the efficacy of crop improvement. Effects GBLUP genomic selection stochastic simulations GRM weighted models Full Text Supplementary Files SupplementaryMaterial.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 09 Aug, 2025 First submitted to journal 06 Aug, 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. 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-7313431","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500881749,"identity":"1071a7cf-9b61-4e11-b242-19b8106515c8","order_by":0,"name":"Henry Alexander Cordoba-Novoa","email":"","orcid":"","institution":"McGill University","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"Alexander","lastName":"Cordoba-Novoa","suffix":""},{"id":500881750,"identity":"fb45d88c-254f-4218-b021-b944235476c9","order_by":1,"name":"Valerio Hoyos-Villegas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAygtww+hmYnXwiPZQLIWgwPEajFn7zF8+LPNjsf42vFnEgwV1okNhLRY9pwxNuZtS+Yxu51jJsFwJp2wFoMbOWbSDGeYQVrYJBjbDhOnRfLHmXoe49npzyQY/xGpRYKn4jCPgXSCmQRjAzFazhwrNuapOM4jcTvH2CLhWLoxYS3Hmzc+/GFQLcc/O/3hjQ811rIEtTAwcBgg2AmElYMA+wPi1I2CUTAKRsHIBQCxzznp7QgQtQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1080-9148","institution":"Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Valerio","middleName":"","lastName":"Hoyos-Villegas","suffix":""}],"badges":[],"createdAt":"2025-08-07 00:14:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7313431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7313431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89634512,"identity":"0d373e89-92df-4c1c-889d-c839057fb650","added_by":"auto","created_at":"2025-08-22 07:07:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1470721,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptGPandSimulationsv6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313431/v1_covered_201e7a8a-580f-4a0b-b62b-e3b7ad6cb0ae.pdf"},{"id":89629830,"identity":"e1639351-71c0-4075-8130-fbd161549a24","added_by":"auto","created_at":"2025-08-22 06:35:25","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":4073091,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7313431/v1/5df90e1077f5eefa24ff23db.docx"},{"id":89631681,"identity":"234856b4-8d40-4a99-9f54-ef031dd8a022","added_by":"auto","created_at":"2025-08-22 06:51:26","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2969175,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7313431/v1/8b08c739febd9c9d5ff7283a.xlsx"}],"financialInterests":"","formattedTitle":"Prioritization of Deleterious Mutations Informs Genomic Prediction and Increases the Rate of Genetic Gain in Common Bean (Phaseolus vulgaris L.), a Simulation Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Effects, GBLUP, genomic selection, stochastic simulations, GRM, weighted models","lastPublishedDoi":"10.21203/rs.3.rs-7313431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7313431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of mutations is fundamental to understanding evolution, domestication, and genetics. Characterizing mutations has potential to accelerate breeding programs through selection and purging deleterious mutations (DelMut). We investigated how predicting DelMut in breeding populations informs genomic prediction (GP) increasing the rate of genetic gain. DelMut were annotated in three independent common bean populations using a previously developed random forest (RF) model developed for common bean incorporating phylogenetic and protein information. Deleterious scores from the RF model were around 0.25, with the top 1% (\u003cem\u003ehighly\u003c/em\u003e DelMut) of variants scoring between 0.78\u0026ndash;0.82 among populations. All populations showed variation in the number of \u003cem\u003ehighly\u003c/em\u003e DelMut per line (max. 13\u0026ndash;197) and in genetic load. We assessed the impact of incorporating \u003cem\u003ea priori\u003c/em\u003e information on DelMut for variant prioritization and weighting in GP models for yield and flowering time. Stochastic simulations were conducted to evaluate how designing mating schemes based on variable numbers of DelMut per parent can affect genetic gain. Variants with higher predicted scores had significantly different effect distributions compared to random or lower-scored markers. Simulated breeding cycles showed that selecting parents with fewer \u003cem\u003ehighly\u003c/em\u003e DelMut consistently increases the rate of genetic gain depending and could be superior to phenotypic selection depending on the population. These results highlight the potential of DelMut information for variant prioritization and the optimization of common bean breeding programs. 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