Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Detalhes bibliográficos
Autor(a) principal: Bignardi, A. B. [UNESP]
Data de Publicação: 2011
Outros Autores: El Faro, L., Torres Junior, R. A. A., Cardoso, V. L., Machado, P. F., Albuquerque, Lucia Galvão de [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.4238/2011.October.31.4
http://hdl.handle.net/11449/4966
Resumo: We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.
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spelling Random regression models using different functions to model test-day milk yield of Brazilian Holstein cowsCovariance functionsParametric functionsTest-daySegmented polynomialsWe analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Jaboticabal, SP, BrazilAgencia Paulista Tecnol Agronegocios, Ribeirao Preto, SP, BrazilEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Gado Corte, Campo Grande, MS, BrazilUniv São Paulo, Escola Super Agr Luiz de Queiroz, Dept Zootecnia, Piracicaba, SP, BrazilUniv Estadual Paulista, Fac Ciencias Agr & Vet, Dept Zootecnia, Jaboticabal, SP, BrazilFunpec-editoraUniversidade Estadual Paulista (Unesp)Agência Paulista de Tecnologia dos Agronegócios (APTA)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Universidade de São Paulo (USP)Bignardi, A. B. [UNESP]El Faro, L.Torres Junior, R. A. A.Cardoso, V. L.Machado, P. F.Albuquerque, Lucia Galvão de [UNESP]2014-05-20T13:19:12Z2014-05-20T13:19:12Z2011-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3565-3575application/pdfhttp://dx.doi.org/10.4238/2011.October.31.4Genetics and Molecular Research. Ribeirao Preto: Funpec-editora, v. 10, n. 4, p. 3565-3575, 2011.1676-5680http://hdl.handle.net/11449/496610.4238/2011.October.31.4WOS:000300617600131WOS000300617600131.pdf5866981114947883Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengGenetics and Molecular Research0,439info:eu-repo/semantics/openAccess2023-11-15T06:14:03Zoai:repositorio.unesp.br:11449/4966Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-11-15T06:14:03Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
title Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
spellingShingle Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
Bignardi, A. B. [UNESP]
Covariance functions
Parametric functions
Test-day
Segmented polynomials
title_short Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
title_full Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
title_fullStr Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
title_full_unstemmed Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
title_sort Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
author Bignardi, A. B. [UNESP]
author_facet Bignardi, A. B. [UNESP]
El Faro, L.
Torres Junior, R. A. A.
Cardoso, V. L.
Machado, P. F.
Albuquerque, Lucia Galvão de [UNESP]
author_role author
author2 El Faro, L.
Torres Junior, R. A. A.
Cardoso, V. L.
Machado, P. F.
Albuquerque, Lucia Galvão de [UNESP]
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Agência Paulista de Tecnologia dos Agronegócios (APTA)
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Bignardi, A. B. [UNESP]
El Faro, L.
Torres Junior, R. A. A.
Cardoso, V. L.
Machado, P. F.
Albuquerque, Lucia Galvão de [UNESP]
dc.subject.por.fl_str_mv Covariance functions
Parametric functions
Test-day
Segmented polynomials
topic Covariance functions
Parametric functions
Test-day
Segmented polynomials
description We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.
publishDate 2011
dc.date.none.fl_str_mv 2011-01-01
2014-05-20T13:19:12Z
2014-05-20T13:19:12Z
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/10.4238/2011.October.31.4
Genetics and Molecular Research. Ribeirao Preto: Funpec-editora, v. 10, n. 4, p. 3565-3575, 2011.
1676-5680
http://hdl.handle.net/11449/4966
10.4238/2011.October.31.4
WOS:000300617600131
WOS000300617600131.pdf
5866981114947883
url http://dx.doi.org/10.4238/2011.October.31.4
http://hdl.handle.net/11449/4966
identifier_str_mv Genetics and Molecular Research. Ribeirao Preto: Funpec-editora, v. 10, n. 4, p. 3565-3575, 2011.
1676-5680
10.4238/2011.October.31.4
WOS:000300617600131
WOS000300617600131.pdf
5866981114947883
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 3565-3575
application/pdf
dc.publisher.none.fl_str_mv Funpec-editora
publisher.none.fl_str_mv Funpec-editora
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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