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Abstract
Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included [ver mas...]
dc.contributor.authorRezende, Fernanda M.
dc.contributor.authorNani, Juan Pablo
dc.contributor.authorPeñagaricano, Francisco
dc.date.accessioned2019-05-20T12:00:50Z
dc.date.available2019-05-20T12:00:50Z
dc.date.issued2019-04
dc.identifier.issn0022-0302
dc.identifier.otherhttps://doi.org/10.3168/jds.2018-15810
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0022030219301079
dc.identifier.urihttp://hdl.handle.net/20.500.12123/5151
dc.description.abstractService sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevieres_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.sourceJournal of Dairy Science 102 (4) : 3230-3240 (April 2019)es_AR
dc.subjectGanado de Lechees_AR
dc.subjectDairy Cattleeng
dc.subjectRazas (animales)es_AR
dc.subjectBreeds (animals)eng
dc.subjectGenéticaes_AR
dc.subjectGeneticseng
dc.subjectGenómicaes_AR
dc.subjectGenomicseng
dc.subjectFertilidades_AR
dc.subjectFertilityeng
dc.subjectToroes_AR
dc.subjectBullseng
dc.subject.otherRaza Jerseyes_AR
dc.subject.otherEstados Unidoses_AR
dc.titleGenomic prediction of bull fertility in US Jersey dairy cattlees_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenEEA Rafaelaes_AR
dc.description.filFil: Rezende, Fernanda M. University of Florida. Department of Animal Sciences; Estados Unidos. Universidade Federal de Uberlândia. Faculdade de Medicina Veterinária; Brasiles_AR
dc.description.filFil: Nani, Juan Pablo. University of Florida. Department of Animal Sciences; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentinaes_AR
dc.description.filFil: Peñagaricano, Francisco. University of Florida. Department of Animal Sciences; Estados Unidos. University of Florida. University of Florida Genetics Institute; Estados Unidoses_AR
dc.subtypecientifico


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