title: Comparison of molecular breeding values based on within- and across-breed training in beef cattle.
abstract: BACKGROUND
  Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported.
  
  
  METHODS
  Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype.
  
  
  RESULTS
  With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero.
  
  
  CONCLUSIONS
  Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.
authors: Department of Statistics, University of Nebraska, Lincoln, NE 68583, USA. steve.kachman@unl.edu.
authors: Stephen D
authors: SD
authors: Kachman
authors: Matthew L
authors: ML
authors: Spangler
authors: Gary L
authors: GL
authors: Bennett
authors: Kathryn J
authors: KJ
authors: Hanford
authors: Larry A
authors: LA
authors: Kuehn
authors: Warren M
authors: WM
authors: Snelling
authors: R Mark
authors: RM
authors: Thallman
authors: Mahdi
authors: M
authors: Saatchi
authors: Dorian J
authors: DJ
authors: Garrick
authors: Robert D
authors: RD
authors: Schnabel
authors: Jeremy F
authors: JF
authors: Taylor
authors: E John
authors: EJ
authors: Pollak
mesh_terms: D000465:Algorithms; D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D014644:Genetic Variation; D016678:Genome; D023281:Genomics; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable
references: J Anim Sci. 2012 Oct;90(10):3375-84
references: 23038744
references: Science. 2009 Apr 24;324(5926):528-32
references: 19390050
references: J Anim Sci. 2010 Jan;88(1):32-46
references: 19749023
references: J Anim Sci. 2010 Feb;88(2):544-51
references: 19820059
references: J Dairy Sci. 2009 Feb;92(2):433-43
references: 19164653
references: Genetics. 2009 Dec;183(4):1545-53
references: 19822733
references: Genet Sel Evol. 2011 Nov 28;43:40
references: 22122853
references: Genet Sel Evol. 2009 Jan 15;41:12
references: 19284703
references: J Anim Sci. 2012 Dec;90(12):4191-202
references: 22871929
references: J Anim Sci. 2010 Feb;88(2):517-22
references: 19897629
references: J Anim Sci. 2012 Dec;90(12):4177-90
references: 22767091
references: BMC Bioinformatics. 2011 May 23;12:186
references: 21605355
references: Genet Sel Evol. 2012 Dec 07;44:38
references: 23216608
references: J Anim Sci. 2013 Apr;91(4):1538-51
references: 23345550
references: Genet Sel Evol. 2009 Dec 31;41:55
references: 20043827
references: J Dairy Sci. 2011 May;94(5):2625-30
references: 21524555