Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/987574
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dc.contributor.authorNEVES, H. H.pt_BR
dc.contributor.authorCARVALHEIRO, R.pt_BR
dc.contributor.authorO'BRIEN, A. M.pt_BR
dc.contributor.authorUTSUNOMIYA, Y. T.pt_BR
dc.contributor.authorCARMO, A. S. dopt_BR
dc.contributor.authorSCHENKEL, F. S.pt_BR
dc.contributor.authorSÖLKNER, J.pt_BR
dc.contributor.authorMCEWAN, J. C.pt_BR
dc.contributor.authorVAN TASSELL, C. P.pt_BR
dc.contributor.authorCOLE, J. B.pt_BR
dc.contributor.authorSILVA, M. V. G. B.pt_BR
dc.contributor.authorQUEIROZ, S. A.pt_BR
dc.contributor.authorSONSTEGARD, T. S.pt_BR
dc.contributor.authorGARCIA, J. F.pt_BR
dc.date.accessioned2015-01-25T08:43:51Z-
dc.date.available2015-01-25T08:43:51Z-
dc.date.created2014-06-04pt_BR
dc.date.issued2014pt_BR
dc.identifier.citationGenetics Selection Evolution, v. 46, article 17, 2014.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/987574pt_BR
dc.descriptionBackground- Nellore cattle play an important role in beef production in tropical systems and there is great interest in determining if genomic selection can contribute to accelerate genetic improvement of production and fertility in this breed. We present the first results of the implementation of genomic prediction in a Bos indicus (Nellore) population. Methods - Influential bulls were genotyped with the Illumina Bovine HD chip in order to assess genomic predictive ability for weight and carcass traits, gestation length, scrotal circumference and two selection indices. 685 samples and 320 238 single nucleotide polymorphisms (SNPs) were used in the analyses. A forward-prediction scheme was adopted to predict the genomic breeding values (DGV). In the training step, the estimated breeding values (EBV) of bulls were deregressed (dEBV) and used as pseudo-phenotypes to estimate marker effects using four methods: genomic BLUP with or without a residual polygenic effect (GBLUP20 and GBLUP0, respectively), a mixture model (Bayes C) and Bayesian LASSO (BLASSO). Empirical accuracies of the resulting genomic predictions were assessed based on the correlation between DGV and dEBV for the testing group. Results - Accuracies of genomic predictions ranged from 0.17 (navel at weaning) to 0.74 (finishing precocity). Across traits, Bayesian regression models (Bayes C and BLASSO) were more accurate than GBLUP. The average empirical accuracies were 0.39 (GBLUP0), 0.40 (GBLUP20) and 0.44 (Bayes C and BLASSO). Bayes C and BLASSO tended to produce deflated predictions (i.e. slope of the regression of dEBV on DGV greater than 1). Further analyses suggested that higher-than-expected accuracies were observed for traits for which EBV means differed significantly between two breeding subgroups that were identified in a principal component analysis based on genomic relationships. Conclusions -Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions. Recurrent updates of the training population would be required to enable accurate prediction of the genetic merit of young animals. The technical feasibility of applying genomic prediction in a Bos indicus (Nellore) population was demonstrated. Further research is needed to permit cost-effective selection decisions using genomic information.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectNellore cattlept_BR
dc.subjectGenomic selectionpt_BR
dc.titleAccuracy of genomic predictions in Bos indicus (Nellore) cattle.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2015-01-25T08:43:51Zpt_BR
riaa.ainfo.id987574pt_BR
riaa.ainfo.lastupdate2015-01-24pt_BR
dc.identifier.doihttps://doi.org/10.1186/1297-9686-46-17eng
dc.contributor.institutionHaroldo HR Neves; Roberto Carvalheiro; Ana M Pérez O'Brien; Yuri T Utsunomiya; Adriana S. do Carmo; Flávio S Schenkel; Johann Sölkner; John C McEwan; Curtis P Van Tassell; John B Cole; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; Sandra A Queiroz; Tad S Sonstegard; José Fernando Garcia.pt_BR
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