title: Effects of number of training generations on genomic prediction for various traits in a layer chicken population.
abstract: BACKGROUND
  Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line.
  
  
  METHODS
  Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated.
  
  
  RESULTS
  On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions.
  
  
  CONCLUSIONS
  The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.
authors.affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50010, USA.
authors.forename: Ziqing
authors.initials: Z
authors.lastname: Weng
authors.affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50010, USA.|Hy-Line International, Dallas Center, IA, 50063, USA.
authors.forename: Anna
authors.initials: A
authors.lastname: Wolc
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.|MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
authors.forename: Xia
authors.initials: X
authors.lastname: Shen
authors.affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50010, USA.
authors.forename: Rohan L
authors.initials: RL
authors.lastname: Fernando
authors.affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50010, USA.
authors.forename: Jack C M
authors.initials: JC
authors.lastname: Dekkers
authors.affiliation: Hy-Line International, Dallas Center, IA, 50063, USA.
authors.forename: Jesus
authors.initials: J
authors.lastname: Arango
authors.affiliation: Hy-Line International, Dallas Center, IA, 50063, USA.
authors.forename: Petek
authors.initials: P
authors.lastname: Settar
authors.affiliation: Hy-Line International, Dallas Center, IA, 50063, USA.
authors.forename: Janet E
authors.initials: JE
authors.lastname: Fulton
authors.affiliation: Hy-Line International, Dallas Center, IA, 50063, USA.
authors.forename: Neil P
authors.initials: NP
authors.lastname: O'Sullivan
authors.affiliation: Department of Animal Science, Iowa State University, Ames, IA, 50010, USA. dorian@iastate.edu.|Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand. dorian@iastate.edu.
authors.forename: Dorian J
authors.initials: DJ
authors.lastname: Garrick
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002645:Chickens / Q000235:genetics*; D004531:Eggs / Q000592:standards; D005260:Female; D016678:Genome; D023281:Genomics / Q000379:methods*; D005838:Genotype; D023421:Models, Animal; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D012641:Selection, Genetic