Accuracy of genomic predictions in Bos indicus (Nellore) cattle

Autor(es):Mostrar menos -

Neves, Haroldo H. R. ^[1] ; Carvalheiro, Roberto ^{[1, 2]} ; Perez O'Brien, Ana M. ^[3] ; Utsunomiya, Yuri T. ^[1] ; do Carmo, Adriana S. ^[1] ; Schenkel, Flavio S. ^[4] ; Soelkner, Johann ^[3] ; McEwan, John C. ^[5] ; Van Tassell, Curtis P. ^[6] ; Cole, John B. ^[7] ; da Silva, Marcos V. G. B. ^[8] ; Queiroz, Sandra A. ^[1] ; Sonstegard, Tad S. ^[6] ; Garcia, Jose Fernando ^[9]

Afiliação do(s) autor(es)

^[1] Univ Estadual Paulista, UNESP, Fac Ciencias Agr & Vet, BR-14884900 Sao Paulo - Brazil

^[2] GenSys Consultores Assoc SC Ltda, BR-90680000 Porto Alegre, RS - Brazil

^[3] Univ Nat Resources & Life Sci, Div Livestock Sci, Dept Sustainable Agr Syst BOKU, A-1180 Vienna - Austria

^[4] Univ Guelph, Ctr Genet Improvement Livestock, Guelph, ON N1G 2W1 - Canada

^[5] AgResearch, Ctr Reprod & Genom, Invermay, Mosgiel - New Zealand

^[6] ARS, USDA, Bovine Funct Genom Lab, Beltsville, MD 20705 - USA

^[7] ARS, Anim Improvement Programs Lab, USDA, Beltsville, MD 20705 - USA

^[8] Embrapa DairyCattle, Bioinformat & Anim Genom Lab, Juiz De Fora, MG - Brazil

^[9] Univ Estadual Paulista, UNESP, Fac Med Vet Aracatuba, BR-16050680 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: GENETICS SELECTION EVOLUTION; v. 46, FEB 27 2014.
• Citações Web of Science: 39

Resumo

Background: 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. (AU)

• Processo FAPESP: 10/52030-2 - Estudos de associação genômica das características reprodutivas de touros zebuínos (Bos indicus) utilizando SNP chip de alta densidade
• Beneficiário: José Fernando Garcia
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 10/06185-4 - Estratégias para aumento de eficiência da seleção genômica em programas de melhoramento genético animal
• Beneficiário: Haroldo Henrique de Rezende Neves
• Linha de fomento: Bolsas no Brasil - Doutorado
• Processo FAPESP: 11/16643-2 - Estudo de associação genômica para a detecção de regiões cromossômicas relacionadas ao perímetro escrotal de bovinos Nelore
• Beneficiário: Yuri Tani Utsunomiya
• Linha de fomento: Bolsas no Brasil - Mestrado