Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity
Autor(a) principal: | |
---|---|
Data de Publicação: | 2012 |
Outros Autores: | , , , |
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. |
id |
UNSP_ed69eb6a15fc67914e4b350139563eba |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/4501 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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/openAccess2024-06-07T18:44:44Zoai:repositorio.unesp.br:11449/4501Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:25:18.200898Repositório 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 |
|
_version_ |
1808129519272329216 |