Genomic predictions for growth and feed effeciency traits in duck breeding populations

Genomic predictions for growth and feed effeciency traits in duck breeding populations | 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 predictions for growth and feed effeciency traits in duck breeding populations Wentao Cai, Chengmin Han, Linxi Zhu, Mengdie Wang, Qinglei Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6694626/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Background In the commercial broiler duck industry, optimizing breeding practices is crucial, especially for growth and feed efficiency traits. Although genomic selection (GS) has been successfully applied in livestocks, it is not yet widely used in duck breeding. This study aims to investigate genetic parameters and refine GS strategies for feed efficiency and growth traits in ducks, paving the way for more precise and efficient breeding programs. Results We investigated genetic parameters of 12 growth and feed efficiency traits in a commercial breeding line of 52,610 ducks across 10 generations. We applied genomic predictions in 2779 ducks of latest three generations. Heritability of these traits ranging from 0.16 to 0.51. Genomic prediction using GBLUP demonstrated higher reliability in cross-validation (average reliability: 0.30) than in forward validation (0.13–0.17), with performance gaps influenced by reference population recency and trait complexity, while ssGBLUP consistently outperformed pedigree-based BLUP, particularly for feed efficiency traits. Expanding the reference population with recent generations improved forward validation reliability by 27.7%, highlighting the critical role of updated genetic data in enhancing across-generation predictive accuracy. The newly proposed residual feed intake adjusted for breast muscle volume demonstrated a higher heritability and predictive reliability compared to its predecessor. Pruning variants using linkage disequilibrium thresholds of 0.075 resulted in an increase of 0.05 in the average predictive reliability. Similarly, omitting the Hardy-Weinberg equilibrium threshold generally resulted in higher predictive reliability for most traits. However, for traits such as BMW, BMT, and BMV, we observed enhanced predictive reliability when applying a specific threshold for HWE test pruning. The BayesRC model, when informed by cis-eQTLs or their regulated genes, particularly from adipose and muscle tissues, increased predictive reliability for various traits, highlighting the importance of integrating biological data into genomic prediction frameworks. Conclusions This study offers encouraging evidence for utilizing GS to enhance growth and feed efficiency traits in ducks. It offers valuable insights into optimizing GS for duck breeding, emphasizing the critical roles of model selection, marker density refinement, and the strategic integration of prior biological information. Full Text Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional files Additional file 1, Table S1 File format: XLSX Title: The breeding population of ducks. Additional file 1, Table S2 File format: XLSX Title: Heritability estimates and variance components across 12 traits using three models. Additional file 1, Table S3 File format: XLSX Title: Genetic and phenotype correlations across 12 traits. Additional file 1, Table S4 File format: XLSX Title: Genomic prediction by pruning markers based on various linkage disequilibrium (LD) thesholds. Additional file 1, Table S5 File format: XLSX Title: The predictive reliability of GS for duck growth and feed effciency traits using Bayesian models. Additional file 1, Table S6 File format: XLSX Title: Genomic prediction by pruning markers based on various Hardy-Weinberg equilibrium (HWE) thesholds. Additional file 1, Table S7 File format: XLSX Title: Genomic prediction by incorporating eQTLs prior information of seven duck tissues. Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 17 Jun, 2025 Editor invited by journal 29 May, 2025 Submission checks completed at journal 29 May, 2025 First submitted to journal 29 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-6694626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473335174,"identity":"0b416ed5-5794-4338-a0cb-b44c7436aa20","order_by":0,"name":"Wentao Cai","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Cai","suffix":""},{"id":473335175,"identity":"c12358ed-a2e0-4047-9e5a-ee9e0c863897","order_by":1,"name":"Chengmin Han","email":"","orcid":"","institution":"Chinese Academy of Agricultural 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S4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: XLSX\u003c/p\u003e\n\u003cp\u003eTitle: Genomic prediction by pruning markers based on various linkage disequilibrium (LD) thesholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1, Table S5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: XLSX\u003c/p\u003e\n\u003cp\u003eTitle: The predictive reliability of GS for duck growth and feed effciency traits using Bayesian models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1, Table S6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: XLSX\u003c/p\u003e\n\u003cp\u003eTitle: Genomic prediction by pruning markers based on various Hardy-Weinberg equilibrium (HWE) thesholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1, Table S7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: XLSX\u003c/p\u003e\n\u003cp\u003eTitle: Genomic prediction by incorporating eQTLs prior information of seven duck 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Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6694626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6694626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn the commercial broiler duck industry, optimizing breeding practices is crucial, especially for growth and feed efficiency traits. Although genomic selection (GS) has been successfully applied in livestocks, it is not yet widely used in duck breeding. This study aims to investigate genetic parameters and refine GS strategies for feed efficiency and growth traits in ducks, paving the way for more precise and efficient breeding programs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe investigated genetic parameters of 12 growth and feed efficiency traits in a commercial breeding line of 52,610 ducks across 10 generations. We applied genomic predictions in 2779 ducks of latest three generations. Heritability of these traits ranging from 0.16 to 0.51. Genomic prediction using GBLUP demonstrated higher reliability in cross-validation (average reliability: 0.30) than in forward validation (0.13\u0026ndash;0.17), with performance gaps influenced by reference population recency and trait complexity, while ssGBLUP consistently outperformed pedigree-based BLUP, particularly for feed efficiency traits. Expanding the reference population with recent generations improved forward validation reliability by 27.7%, highlighting the critical role of updated genetic data in enhancing across-generation predictive accuracy. The newly proposed residual feed intake adjusted for breast muscle volume demonstrated a higher heritability and predictive reliability compared to its predecessor. Pruning variants using linkage disequilibrium thresholds of 0.075 resulted in an increase of 0.05 in the average predictive reliability. Similarly, omitting the Hardy-Weinberg equilibrium threshold generally resulted in higher predictive reliability for most traits. However, for traits such as BMW, BMT, and BMV, we observed enhanced predictive reliability when applying a specific threshold for HWE test pruning. The BayesRC model, when informed by cis-eQTLs or their regulated genes, particularly from adipose and muscle tissues, increased predictive reliability for various traits, highlighting the importance of integrating biological data into genomic prediction frameworks.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study offers encouraging evidence for utilizing GS to enhance growth and feed efficiency traits in ducks. It offers valuable insights into optimizing GS for duck breeding, emphasizing the critical roles of model selection, marker density refinement, and the strategic integration of prior biological information.\u003c/p\u003e","manuscriptTitle":"Genomic predictions for growth and feed effeciency traits in duck breeding populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 16:12:23","doi":"10.21203/rs.3.rs-6694626/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-14T13:43:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T15:26:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T12:28:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260699918980960533992596814599043273974","date":"2025-06-19T02:02:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176614327102089945017313065005467258759","date":"2025-06-19T00:58:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-18T01:22:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-17T16:45:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-29T20:01:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-29T08:13:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-05-29T08:12:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecb38e0f-b136-476b-9367-50f85265038e","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-6694626","link":"https://doi.org/10.1186/s12864-025-12087-7","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-10-08 15:57:04","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-06-19 16:12:23","video":"","vorDoi":"10.1186/s12864-025-12087-7","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12087-7","workflowStages":[]},"version":"v1","identity":"rs-6694626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6694626","identity":"rs-6694626","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}