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Abstract
Genomic selection has been widely used for complex quantitative trait in farm animals. Estimations of breeding values for slaughter traits are most important to beef cattle industry, and it is worthwhile to investigate prediction accuracies of genomic selection for these traits. In this study, we assessed genomic predictive abilities for average daily gain weight (ADG), live weight (LW), carcass weight (CW), dressing percentage (DP), lean meat percentage (LMP) and retail meat weight (RMW) using Illumina Bovine 770K SNP Beadchip in Chinese Simmental cattle. To evaluate the abilities of prediction, marker effects were estimated using genomic BLUP (GBLUP) and three parallel Bayesian models, including multiple chains parallel BayesA, BayesB and BayesCπ (PBayesA, PBayesB and PBayesCπ). Training set and validation set were divided by random allocation, and the predictive accuracies were evaluated using 5-fold cross validations. We found the accuracies of genomic predictions ranged from 0.195±0.084 (GBLUP for LMP) to 0.424±0.147 (PBayesB for CW). The average accuracies across traits were 0.327±0.085 (GBLUP), 0.335±0.063 (PBayesA), 0.347±0.093 (PBayesB) and 0.334±0.077 (PBayesCπ), respectively. Notably, parallel Bayesian models were more accurate than GBLUP across six traits. Our study suggested that genomic selections with multiple chains parallel Bayesian models are feasible for slaughter traits in Chinese Simmental cattle. The estimations of direct genomic breeding values using parallel Bayesian methods can offer important insights into improving prediction accuracy at young ages and may also help to identify superior candidates in breeding programs.
Highlights
We further extended the parallel computing in genomic prediction by combining multiple chains parallel MCMC with Bayesian models.The objectives of this study were to 1) estimate prediction abilities of genomic selection for slaughter traits in Chinese Simmental beef cattle with genomic BLUP (GBLUP), parallel Bayesian methods. 2) evaluate the predictive accuracies of these methods. 3) provide valuable insights for application of genomic selection for slaughter traits in Chinese Simmental cattle
For retail meat weight (RMW), we found PBayesCπ, PBayesB and PBayesA were superior to GBLUP, while GBLUP was superior over parallel Bayesian methods for average daily gain weight (ADG)
Our results suggested that GBLUP was suitable for ADG, while PBayesA, PBayesB and PBayesCπ were suitable for other traits in Chinese Simmental cattle population
Summary
Genomic prediction has been widely used to predict breeding values of candidates with genome-wide SNP markers [1], this technology offers great promise to predict genetic merits. Parallel genomic prediction in Chinese Simmental beef cattle. Program (ASTIP- IAS03 and ASTIP-IAS-TS-9), Cattle Breeding Innovative Research Team of Chinese Academy of Agricultural Sciences (cxgcias-03), Beijing Natural Science Foundation (6154032). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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