Abstract

BackgroundAlthough 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.MethodsMolecular 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.ResultsWith 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.ConclusionsEven 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.

Highlights

  • 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

  • Research in dairy cattle has shown that when the subpopulations diverged, genomic predictors from a multi-breed training population did not have higher accuracy than predictors from single-breed training sets, except when evaluation occurred in a breed that was not represented in the training set, in which case adding multiple breeds increased the accuracy of predictors, compared to using a singlebreed training set [13]

  • Heritability estimates were moderate to high and within the range of the estimates reported in the literature and summarized by Koots et al [21], some of the estimates of direct-maternal genetic correlations are greater than expected based on literature [22]

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Summary

Introduction

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. The efficacy of within-breed trained genomic predictors looks promising [3,4,5,6,7,8], it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. Our objectives were to derive and evaluate genomic predictors using genotypes from the Illumina (San Diego, CA) BovineSNP50 platform for growth traits (weaning and yearling weights) in single-breed and multi-breed training data sets and evaluate them on field data

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