Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
Autor(a) principal: | |
---|---|
Data de Publicação: | 2013 |
Outros Autores: | , , , , , |
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. |
id |
UNSP_81d202d7acdc00b3de1e68ab5a01152d |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/76046 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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 |
|
_version_ |
1808129359109685248 |