Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle.

B Zhu,Y Chen,H Gao,L Xu,W Zhang,X Gao,P Guo,L Zhang,J Li,Z Wang

doi:10.1111/age.12853

Abstract

SummaryGenomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesCπ and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesCπ) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesCπ and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesCπ, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.

Highlights

Genomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) for quantitative traits in breeding program of farm animals (Hayes et al 2009; Goddard et al 2010)
The success of genomic selection depends on the accuracies of GEBVs, which are largely affected by the predictive approaches, the size of reference population, trait heritability and the extent of the linkage disequilibrium between SNPs and QTL (Hayes et al 2009; VanRaden et al 2009)
We found that 10 traits showed relatively high heritabilities: fore shank (FS) (0.4), live weight (LW) (0.43), retail meat weight (RMW) (0.43), average daily gain (ADG) (0.47), BI (0.47), inside cap off (ICO) (0.51), ER (0.52), OU (0.6), hind shank (HS) (0.61) and KN (0.62)

Summary

Introduction

Genomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) for quantitative traits in breeding program of farm animals (Hayes et al 2009; Goddard et al 2010). The success of genomic selection depends on the accuracies of GEBVs, which are largely affected by the predictive approaches, the size of reference population, trait heritability and the extent of the linkage disequilibrium between SNPs and QTL (Hayes et al 2009; VanRaden et al 2009). Many studies have assessed the predictive accuracies of GEBVs for economically important traits in different beef cattle populations using the BovineSNP50 BeadChip (Saatchi et al 2011, 2012; Todd et al 2014; Chen et al 2015), and their results show varying degrees of accuracy for GEBVs. For instance, genomic prediction for growth, meat quality and reproduction traits in US Limousin and Simmental beef cattle revealed accuracies of GEBVs ranging from 0.39 to 0.76 and 0.29 to 0.65 respectively(Saatchi et al 2012). Accuracies of GEBVs for US Angus ranged from 0.22 to 0.69

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Conclusion