|Thallman, Richard - Mark|
Submitted to: Genetics Selection Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/13/2013
Publication Date: 8/16/2013
Citation: Kachman, S.D., Spangler, M.L., Bennett, G.L., Hanford, K.J., Kuehn, L.A., Snelling, W.M., Thallman, R.M., Saatchi, M., Garrick, D.J., Schnabel, R.D., Taylor, J.F., Pollak, E.J. 2013. Comparison of molecular breeding values based on within- and across-breed training in beef cattle. Genetics Selection Evolution. 45:30. 9 p. DOI: 10.1186/1297-9686-45-30. Interpretive Summary: Prediction of genetic differences among beef bulls and cows based on tens of thousands of genetic markers has recently become feasible. How these predictions should be derived and how widely they can be applied are significant questions. Predictions can be derived (trained) using each breed or crossbred independently and then applied either within the population or across other populations. Alternatively, they can be trained combining information from multiple breeds and crossbreds and used in many populations. These scenarios were tested in some large experimental and industry populations of cattle for weaning and yearling weights. Predictions trained within a breed and used within that breed were partially successful but were unsuccessful in other breeds not used for training. Combining information from multiple breeds to train predictions was partially successful when used within any of these breeds but was not successful in breeds excluded from the training. Both approaches can increase the accuracy of genetic evaluations for breeds that were included in training. By incorporating information from multiple breeds, it is possible to produce a single genetic prediction that increases accuracy for multiple breeds.
Technical 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.