Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversity of Wisconsin-Madison-
Autor(es): dc.contributorNational Association of Breeders and Researchers-
Autor(es): dc.creatorLopes, Fernando Brito-
Autor(es): dc.creatorBaldi, Fernando-
Autor(es): dc.creatorBrunes, Ludmilla Costa-
Autor(es): dc.creatorOliveira e Costa, Marcos Fernando-
Autor(es): dc.creatorda Costa Eifert, Eduardo-
Autor(es): dc.creatorRosa, Guilherme Jordão Magalhães-
Autor(es): dc.creatorLobo, Raysildo Barbosa-
Autor(es): dc.creatorMagnabosco, Cláudio Ulhoa-
Data de aceite: dc.date.accessioned2025-08-21T20:03:04Z-
Data de disponibilização: dc.date.available2025-08-21T20:03:04Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/jbg.12740-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246080-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246080-
Descrição: dc.descriptionThis study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.-
Descrição: dc.descriptionSão Paulo State University - Júlio de Mesquita Filho (UNESP) Department of Animal Science Prof. Paulo Donato Castelane-
Descrição: dc.descriptionEmbrapa Cerrados-
Descrição: dc.descriptionEmbrapa Rice and Beans-
Descrição: dc.descriptionDepartment of Animal Sciences University of Wisconsin-Madison-
Descrição: dc.descriptionDepartment of Biostatistics and Medical Informatics University of Wisconsin-Madison-
Descrição: dc.descriptionNational Association of Breeders and Researchers-
Descrição: dc.descriptionSão Paulo State University - Júlio de Mesquita Filho (UNESP) Department of Animal Science Prof. Paulo Donato Castelane-
Formato: dc.format1-12-
Idioma: dc.languageen-
Relação: dc.relationJournal of Animal Breeding and Genetics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBayesian approach-
Palavras-chave: dc.subjectbeef cattle-
Palavras-chave: dc.subjectgenomic prediction-
Palavras-chave: dc.subjectinformative SNPs-
Palavras-chave: dc.subjectWBSF-
Título: dc.titleGenomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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