Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

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MetadadosDescriçãoIdioma
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.contributorUniversidade Federal de Goiás (UFG)-
Autor(es): dc.contributorNational Association of Breeders and Researchers-
Autor(es): dc.creatorLopes, F. B. [UNESP]-
Autor(es): dc.creatorBaldi, F. [UNESP]-
Autor(es): dc.creatorPassafaro, T. L.-
Autor(es): dc.creatorBrunes, L. C.-
Autor(es): dc.creatorCosta, M. F.O.-
Autor(es): dc.creatorEifert, E. C.-
Autor(es): dc.creatorNarciso, M. G.-
Autor(es): dc.creatorRosa, G. J.M.-
Autor(es): dc.creatorLobo, R. B.-
Autor(es): dc.creatorMagnabosco, C. U.-
Data de aceite: dc.date.accessioned2022-02-22T00:45:29Z-
Data de disponibilização: dc.date.available2022-02-22T00:45:29Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.animal.2020.100006-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/205851-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/205851-
Descrição: dc.descriptionSeveral methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm2), back fat thickness (BF, mm), rump fat (RF, mm) and Warner–Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Animal Science São Paulo State University - Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane-
Descrição: dc.descriptionEmbrapa Cerrados, BR-020, 18, Sobradinho-
Descrição: dc.descriptionDepartment of Animal Sciences University of Wisconsin-Madison-
Descrição: dc.descriptionDepartment of Animal Science Federal University of Goiás-
Descrição: dc.descriptionEmbrapa Rice and Beans, GO-462, km 12-
Descrição: dc.descriptionDepartment of Biostatistics and Medical Informatics University of Wisconsin-Madison-
Descrição: dc.descriptionNational Association of Breeders and Researchers-
Descrição: dc.descriptionDepartment of Animal Science São Paulo State University - Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane-
Descrição: dc.descriptionFAPESP: #2017/03221- 5479-
Descrição: dc.descriptionFAPESP: 2017/03221-9-
Idioma: dc.languageen-
Relação: dc.relationAnimal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBeef cattle-
Palavras-chave: dc.subjectGenomic prediction-
Palavras-chave: dc.subjectMultiple-trait-
Palavras-chave: dc.subjectWarner–Bratzler shear force-
Título: dc.titleGenome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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