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

Detalhes bibliográficos
Autor(a) principal: Lopes, F. B. [UNESP]
Data de Publicação: 2021
Outros Autores: Baldi, F. [UNESP], Passafaro, T. L., Brunes, L. C., Costa, M. F.O., Eifert, E. C., Narciso, M. G., Rosa, G. J.M., Lobo, R. B., Magnabosco, C. U.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.animal.2020.100006
http://hdl.handle.net/11449/205851
Resumo: Several 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.
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spelling Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattleBeef cattleGenomic predictionMultiple-traitWarner–Bratzler shear forceSeveral 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Animal Science São Paulo State University - Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato CastelaneEmbrapa Cerrados, BR-020, 18, SobradinhoDepartment of Animal Sciences University of Wisconsin-MadisonDepartment of Animal Science Federal University of GoiásEmbrapa Rice and Beans, GO-462, km 12Department of Biostatistics and Medical Informatics University of Wisconsin-MadisonNational Association of Breeders and ResearchersDepartment of Animal Science São Paulo State University - Júlio de Mesquita Filho (UNESP), Prof. Paulo Donato CastelaneFAPESP: #2017/03221- 5479FAPESP: 2017/03221-9Universidade Estadual Paulista (Unesp)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)University of Wisconsin-MadisonUniversidade Federal de Goiás (UFG)National Association of Breeders and ResearchersLopes, F. B. [UNESP]Baldi, F. [UNESP]Passafaro, T. L.Brunes, L. C.Costa, M. F.O.Eifert, E. C.Narciso, M. G.Rosa, G. J.M.Lobo, R. B.Magnabosco, C. U.2021-06-25T10:22:19Z2021-06-25T10:22:19Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.animal.2020.100006Animal, v. 15, n. 1, 2021.1751-732X1751-7311http://hdl.handle.net/11449/20585110.1016/j.animal.2020.1000062-s2.0-85100568933Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnimalinfo:eu-repo/semantics/openAccess2021-10-22T18:56:42Zoai:repositorio.unesp.br:11449/205851Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:43:35.306153Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
title Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
spellingShingle Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
Lopes, F. B. [UNESP]
Beef cattle
Genomic prediction
Multiple-trait
Warner–Bratzler shear force
title_short Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
title_full Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
title_fullStr Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
title_full_unstemmed Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
title_sort Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle
author Lopes, F. B. [UNESP]
author_facet Lopes, F. B. [UNESP]
Baldi, F. [UNESP]
Passafaro, T. L.
Brunes, L. C.
Costa, M. F.O.
Eifert, E. C.
Narciso, M. G.
Rosa, G. J.M.
Lobo, R. B.
Magnabosco, C. U.
author_role author
author2 Baldi, F. [UNESP]
Passafaro, T. L.
Brunes, L. C.
Costa, M. F.O.
Eifert, E. C.
Narciso, M. G.
Rosa, G. J.M.
Lobo, R. B.
Magnabosco, C. U.
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
University of Wisconsin-Madison
Universidade Federal de Goiás (UFG)
National Association of Breeders and Researchers
dc.contributor.author.fl_str_mv Lopes, F. B. [UNESP]
Baldi, F. [UNESP]
Passafaro, T. L.
Brunes, L. C.
Costa, M. F.O.
Eifert, E. C.
Narciso, M. G.
Rosa, G. J.M.
Lobo, R. B.
Magnabosco, C. U.
dc.subject.por.fl_str_mv Beef cattle
Genomic prediction
Multiple-trait
Warner–Bratzler shear force
topic Beef cattle
Genomic prediction
Multiple-trait
Warner–Bratzler shear force
description Several 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.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T10:22:19Z
2021-06-25T10:22:19Z
2021-01-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.animal.2020.100006
Animal, v. 15, n. 1, 2021.
1751-732X
1751-7311
http://hdl.handle.net/11449/205851
10.1016/j.animal.2020.100006
2-s2.0-85100568933
url http://dx.doi.org/10.1016/j.animal.2020.100006
http://hdl.handle.net/11449/205851
identifier_str_mv Animal, v. 15, n. 1, 2021.
1751-732X
1751-7311
10.1016/j.animal.2020.100006
2-s2.0-85100568933
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Animal
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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