Genetic evaluation using multi-trait and random regression models in Simmental beef cattle

Detalhes bibliográficos
Autor(a) principal: Mota, R. R.
Data de Publicação: 2013
Outros Autores: Marques, L. F A, Lopes, P. S., da Silva, L. P., Neto, F. R A [UNESP], de Resende, M. D V, Torres, R. A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.4238/2013.July.24.2
http://hdl.handle.net/11449/76046
Resumo: The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.
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spelling Genetic evaluation using multi-trait and random regression models in Simmental beef cattle(Co)variance componentsBody weightGrowth trajectoryHeritabilityagealgorithmbeef cattlebody weightbreeding lineenvironmental factorfemalegenetic analysisgenetic associationgrowth curveheritabilitylactationmalemathematical analysismathematical modelmulti trait modelnonhumanpedigreephenotyperandom regression modelvarianceThe Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.Departamento de Zootecnia Universidade Federal de Viçosa, Viçosa, MGDepartamento de Zootecnia Centro de Ciências Agrárias Universidade Federal do Espírito Santo, Alegre, ESDepartamento de Zootecnia Universidade Estadual Paulista 'Júlio de Mesquita Filho', Jaboticabal, SPEmbrapa Florestas, Colombo, PRDepartamento de Engenharia Florestal Universidade Federal de Viçosa, Viçosa, MGDepartamento de Zootecnia Universidade Estadual Paulista 'Júlio de Mesquita Filho', Jaboticabal, SPUniversidade Federal de Viçosa (UFV)Universidade Federal do Espírito Santo (UFES)Universidade Estadual Paulista (Unesp)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Mota, R. R.Marques, L. F ALopes, P. S.da Silva, L. P.Neto, F. R A [UNESP]de Resende, M. D VTorres, R. A.2014-05-27T11:30:02Z2014-05-27T11:30:02Z2013-07-24info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article2465-2480application/pdfhttp://dx.doi.org/10.4238/2013.July.24.2Genetics and Molecular Research, v. 12, n. 3, p. 2465-2480, 2013.1676-5680http://hdl.handle.net/11449/7604610.4238/2013.July.24.2WOS:0003317174000312-s2.0-848808177942-s2.0-84880817794.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengGenetics and Molecular Research0,439info:eu-repo/semantics/openAccess2024-06-07T18:44:01Zoai:repositorio.unesp.br:11449/76046Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:47:33.859673Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
title Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
spellingShingle Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
Mota, R. R.
(Co)variance components
Body weight
Growth trajectory
Heritability
age
algorithm
beef cattle
body weight
breeding line
environmental factor
female
genetic analysis
genetic association
growth curve
heritability
lactation
male
mathematical analysis
mathematical model
multi trait model
nonhuman
pedigree
phenotype
random regression model
variance
title_short Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
title_full Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
title_fullStr Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
title_full_unstemmed Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
title_sort Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
author Mota, R. R.
author_facet Mota, R. R.
Marques, L. F A
Lopes, P. S.
da Silva, L. P.
Neto, F. R A [UNESP]
de Resende, M. D V
Torres, R. A.
author_role author
author2 Marques, L. F A
Lopes, P. S.
da Silva, L. P.
Neto, F. R A [UNESP]
de Resende, M. D V
Torres, R. A.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de Viçosa (UFV)
Universidade Federal do Espírito Santo (UFES)
Universidade Estadual Paulista (Unesp)
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.contributor.author.fl_str_mv Mota, R. R.
Marques, L. F A
Lopes, P. S.
da Silva, L. P.
Neto, F. R A [UNESP]
de Resende, M. D V
Torres, R. A.
dc.subject.por.fl_str_mv (Co)variance components
Body weight
Growth trajectory
Heritability
age
algorithm
beef cattle
body weight
breeding line
environmental factor
female
genetic analysis
genetic association
growth curve
heritability
lactation
male
mathematical analysis
mathematical model
multi trait model
nonhuman
pedigree
phenotype
random regression model
variance
topic (Co)variance components
Body weight
Growth trajectory
Heritability
age
algorithm
beef cattle
body weight
breeding line
environmental factor
female
genetic analysis
genetic association
growth curve
heritability
lactation
male
mathematical analysis
mathematical model
multi trait model
nonhuman
pedigree
phenotype
random regression model
variance
description The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.
publishDate 2013
dc.date.none.fl_str_mv 2013-07-24
2014-05-27T11:30:02Z
2014-05-27T11:30:02Z
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/10.4238/2013.July.24.2
Genetics and Molecular Research, v. 12, n. 3, p. 2465-2480, 2013.
1676-5680
http://hdl.handle.net/11449/76046
10.4238/2013.July.24.2
WOS:000331717400031
2-s2.0-84880817794
2-s2.0-84880817794.pdf
url http://dx.doi.org/10.4238/2013.July.24.2
http://hdl.handle.net/11449/76046
identifier_str_mv Genetics and Molecular Research, v. 12, n. 3, p. 2465-2480, 2013.
1676-5680
10.4238/2013.July.24.2
WOS:000331717400031
2-s2.0-84880817794
2-s2.0-84880817794.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Genetics and Molecular Research
0,439
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 2465-2480
application/pdf
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)
repository.mail.fl_str_mv
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