Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

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dc.contributor.authorSantos, D. J. A. [UNESP]
dc.contributor.authorPeixoto, M. G. C. D.
dc.contributor.authorAspilcueta Borquis, R. R. [UNESP]
dc.contributor.authorPanetto, J. C. C.
dc.contributor.authorEl Faro, L.
dc.contributor.authorTonhati, H. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.contributor.institutionPesquisador Inst Zootecnia
dc.date.accessioned2015-03-18T15:53:39Z
dc.date.available2015-03-18T15:53:39Z
dc.date.issued2014-09-01
dc.description.abstractGiven the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.en
dc.description.affiliationUNESP, Fac Ciencias Agr & Vet, Dept Zootecnia, BR-14884900 Jaboticabal, SP, Brazil
dc.description.affiliationEmbrapa Gado Leite, BR-36038330 Juiz De Fora, MG, Brazil
dc.description.affiliationPesquisador Inst Zootecnia, Nova Odess, SP, Brazil
dc.description.affiliationUnespUNESP, Fac Ciencias Agr & Vet, Dept Zootecnia, BR-14884900 Jaboticabal, SP, Brazil
dc.format.extent41-50
dc.identifierhttp://dx.doi.org/10.1016/j.livsci.2014.05.023
dc.identifier.citationLivestock Science. Amsterdam: Elsevier Science Bv, v. 167, p. 41-50, 2014.
dc.identifier.doi10.1016/j.livsci.2014.05.023
dc.identifier.issn1871-1413
dc.identifier.lattes7445254960858159
dc.identifier.urihttp://hdl.handle.net/11449/116645
dc.identifier.wosWOS:000341550900006
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofLivestock Science
dc.relation.ispartofjcr1.204
dc.relation.ispartofsjr0,730
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectCovariance functionen
dc.subjectLactation curveen
dc.subjectParametric functionen
dc.subjectLegendre polynomialsen
dc.subjectB-spline functionen
dc.titlePredicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression modelsen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes7445254960858159
unesp.author.orcid0000-0002-1508-4097[5]
unesp.author.orcid0000-0003-4714-3167[6]
unesp.author.orcid0000-0002-9198-9728[4]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabalpt

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