ArticlePloS one2017
Genomic prediction with parallel computing for slaughter traits in Chinese Simmental beef cattle using high-density genotypes.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection.BMC plant biology · 2026Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations.Frontiers in genetics · 2021Article
- Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle.PloS one · 2019Article
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14 authors.
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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.
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