title: Accuracy of genomic predictions in Bos indicus (Nellore) cattle.
abstract: 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.
authors.affiliation: UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo 14884-900, Brazil. haroldozoo@hotmail.com.
authors.forename: Haroldo H R
authors.initials: HH
authors.lastname: Neves
authors.forename: Roberto
authors.initials: R
authors.lastname: Carvalheiro
authors.forename: Ana M Pérez
authors.initials: AM
authors.lastname: O'Brien
authors.forename: Yuri T
authors.initials: YT
authors.lastname: Utsunomiya
authors.forename: Adriana S
authors.initials: AS
authors.lastname: do Carmo
authors.forename: Flávio S
authors.initials: FS
authors.lastname: Schenkel
authors.forename: Johann
authors.initials: J
authors.lastname: Sölkner
authors.forename: John C
authors.initials: JC
authors.lastname: McEwan
authors.forename: Curtis P
authors.initials: CP
authors.lastname: Van Tassell
authors.forename: John B
authors.initials: JB
authors.lastname: Cole
authors.forename: Marcos V G B
authors.initials: MV
authors.lastname: da Silva
authors.forename: Sandra A
authors.initials: SA
authors.lastname: Queiroz
authors.forename: Tad S
authors.initials: TS
authors.lastname: Sonstegard
authors.forename: José Fernando
authors.initials: JF
authors.lastname: Garcia
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002417:Cattle / Q000235:genetics* / Q000502:physiology; D005787:Gene Frequency; D016678:Genome; D023281:Genomics / Q000379:methods; D005838:Genotype; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D012641:Selection, Genetic