Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Detalhes bibliográficos
Autor(a) principal: Shiotsuki, L.
Data de Publicação: 2012
Outros Autores: Cardoso, F. F., Silva, J. A. Il. V. [UNESP], Rosa, G. J. M., Albuquerque, Lucia Galvão de [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.livsci.2011.11.002
http://hdl.handle.net/11449/4501
Resumo: The objective of this work was to compare a model based on the use of an average numerator relationship matrix (ANRM) and a hierarchical animal model (HIER) to indicate the most appropriate statistical procedure to better estimate the genetic value of Nellore animals that have unknown paternity. The data set contained records of 62,212 Nellore animals. The pedigree file contained a total of 75,088 animals. Two approaches were adopted for the treatment of uncertain paternity. In the model based on the use of the ANRM probabilities were attributed to each of the possible parents of the animals with uncertain paternity. The other method adopted in the present study, i.e., the HIER, considers uncertainty in the assignment of paternity of animals participating in the multiple-sire (MS) system. Within this context, a priori probabilities are assigned to each possible sire of animals with uncertain paternity, which are altered according to information present in the data for the generation of posterior probabilities. Univariate analyses were carried out under Bayesian approach via Markov Chain Monte Carlo (MCMC) methods, implementing a chain of 400,000 rounds where the first 10.000 rounds were discarded (burn-in period). Models were compared by deviance information criteria (DIC) and pseudo Bayes factors (PBF). The model that best fits the data for estimating genetic parameter of animals with uncertain paternity is the Bayesian hierarchical model. Nevertheless, for genetic evaluation, the choice between these models would have no impact on genetic value classification of animals for selection. (C) 2011 Elsevier B.V. All rights reserved.
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spelling Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternityBayesian inferenceBeef cattleMultiple-siresUncertain paternityThe objective of this work was to compare a model based on the use of an average numerator relationship matrix (ANRM) and a hierarchical animal model (HIER) to indicate the most appropriate statistical procedure to better estimate the genetic value of Nellore animals that have unknown paternity. The data set contained records of 62,212 Nellore animals. The pedigree file contained a total of 75,088 animals. Two approaches were adopted for the treatment of uncertain paternity. In the model based on the use of the ANRM probabilities were attributed to each of the possible parents of the animals with uncertain paternity. The other method adopted in the present study, i.e., the HIER, considers uncertainty in the assignment of paternity of animals participating in the multiple-sire (MS) system. Within this context, a priori probabilities are assigned to each possible sire of animals with uncertain paternity, which are altered according to information present in the data for the generation of posterior probabilities. Univariate analyses were carried out under Bayesian approach via Markov Chain Monte Carlo (MCMC) methods, implementing a chain of 400,000 rounds where the first 10.000 rounds were discarded (burn-in period). Models were compared by deviance information criteria (DIC) and pseudo Bayes factors (PBF). The model that best fits the data for estimating genetic parameter of animals with uncertain paternity is the Bayesian hierarchical model. Nevertheless, for genetic evaluation, the choice between these models would have no impact on genetic value classification of animals for selection. (C) 2011 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Brazilian Agr Res Corp Goat & Sheep, BR-62010970 Sobral, CE, BrazilEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA) So Reg Anim Husb, BR-96401970 Bage, RS, BrazilSão Paulo State Univ UNESP, Dept Anim Sci, BR-18610307 Botucatu, SP, BrazilUniv Wisconsin, Dept Dairy Sci, Madison, WI 53706 USANatl Council Sci & Technol Dev CNPq, Brasilia, DF, BrazilSão Paulo State Univ UNESP, Dept Anim Sci, BR-18610307 Botucatu, SP, BrazilFAPESP: 06/58896-6CAPES: 4057/08-2Elsevier B.V.Brazilian Agr Res Corp Goat & SheepEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Universidade Estadual Paulista (Unesp)Univ WisconsinShiotsuki, L.Cardoso, F. F.Silva, J. A. Il. V. [UNESP]Rosa, G. J. M.Albuquerque, Lucia Galvão de [UNESP]2014-05-20T13:18:24Z2014-05-20T13:18:24Z2012-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article89-95application/pdfhttp://dx.doi.org/10.1016/j.livsci.2011.11.002Livestock Science. Amsterdam: Elsevier B.V., v. 144, n. 1-2, p. 89-95, 2012.1871-1413http://hdl.handle.net/11449/450110.1016/j.livsci.2011.11.002WOS:000300807200010WOS000300807200010.pdf5866981114947883Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLivestock Science1.2040,730info:eu-repo/semantics/openAccess2024-01-19T06:32:44Zoai:repositorio.unesp.br:11449/4501Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-19T06:32:44Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
title Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
spellingShingle Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
Shiotsuki, L.
Bayesian inference
Beef cattle
Multiple-sires
Uncertain paternity
title_short Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
title_full Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
title_fullStr Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
title_full_unstemmed Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
title_sort Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
author Shiotsuki, L.
author_facet Shiotsuki, L.
Cardoso, F. F.
Silva, J. A. Il. V. [UNESP]
Rosa, G. J. M.
Albuquerque, Lucia Galvão de [UNESP]
author_role author
author2 Cardoso, F. F.
Silva, J. A. Il. V. [UNESP]
Rosa, G. J. M.
Albuquerque, Lucia Galvão de [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Brazilian Agr Res Corp Goat & Sheep
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
Universidade Estadual Paulista (Unesp)
Univ Wisconsin
dc.contributor.author.fl_str_mv Shiotsuki, L.
Cardoso, F. F.
Silva, J. A. Il. V. [UNESP]
Rosa, G. J. M.
Albuquerque, Lucia Galvão de [UNESP]
dc.subject.por.fl_str_mv Bayesian inference
Beef cattle
Multiple-sires
Uncertain paternity
topic Bayesian inference
Beef cattle
Multiple-sires
Uncertain paternity
description The objective of this work was to compare a model based on the use of an average numerator relationship matrix (ANRM) and a hierarchical animal model (HIER) to indicate the most appropriate statistical procedure to better estimate the genetic value of Nellore animals that have unknown paternity. The data set contained records of 62,212 Nellore animals. The pedigree file contained a total of 75,088 animals. Two approaches were adopted for the treatment of uncertain paternity. In the model based on the use of the ANRM probabilities were attributed to each of the possible parents of the animals with uncertain paternity. The other method adopted in the present study, i.e., the HIER, considers uncertainty in the assignment of paternity of animals participating in the multiple-sire (MS) system. Within this context, a priori probabilities are assigned to each possible sire of animals with uncertain paternity, which are altered according to information present in the data for the generation of posterior probabilities. Univariate analyses were carried out under Bayesian approach via Markov Chain Monte Carlo (MCMC) methods, implementing a chain of 400,000 rounds where the first 10.000 rounds were discarded (burn-in period). Models were compared by deviance information criteria (DIC) and pseudo Bayes factors (PBF). The model that best fits the data for estimating genetic parameter of animals with uncertain paternity is the Bayesian hierarchical model. Nevertheless, for genetic evaluation, the choice between these models would have no impact on genetic value classification of animals for selection. (C) 2011 Elsevier B.V. All rights reserved.
publishDate 2012
dc.date.none.fl_str_mv 2012-03-01
2014-05-20T13:18:24Z
2014-05-20T13:18:24Z
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.livsci.2011.11.002
Livestock Science. Amsterdam: Elsevier B.V., v. 144, n. 1-2, p. 89-95, 2012.
1871-1413
http://hdl.handle.net/11449/4501
10.1016/j.livsci.2011.11.002
WOS:000300807200010
WOS000300807200010.pdf
5866981114947883
url http://dx.doi.org/10.1016/j.livsci.2011.11.002
http://hdl.handle.net/11449/4501
identifier_str_mv Livestock Science. Amsterdam: Elsevier B.V., v. 144, n. 1-2, p. 89-95, 2012.
1871-1413
10.1016/j.livsci.2011.11.002
WOS:000300807200010
WOS000300807200010.pdf
5866981114947883
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Livestock Science
1.204
0,730
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 89-95
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Web of Science
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)
repository.mail.fl_str_mv
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