Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , |
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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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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1799964944175726592 |