Comparison of molecular breeding values based on within- and across-breed training in beef cattle

Authors

Stephen D. Kachman, University of Nebraska-LincolnFollow
Matthew L. Spangler, University of Nebraska - LincolnFollow
Gary L. Bennett, USDA-ARSFollow
Kathryn J. Hanford, University of NebraskaFollow
Larry A. Kuehn, USDA-ARSFollow
Warren M. Snelling, USDA-ARSFollow
Mark Thallman, USMARCFollow
Mahdi Saatchi, Iowa State UniversityFollow
Dorian J. Garrick, Iowa State UniversityFollow
Robert D. Schnabel, University of Missouri
Jeremy F. Taylor, University of Missouri
E John Pollak, USDA-ARSFollow

ORCID IDs

Stephen D. Kachman

Document Type

Article

Date of this Version

2013

Citation

Kachman et al. Genetics Selection Evolution 2013, 45:30

Comments

Copyright 2013 Kachman et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.