Abstract
BackgroundHanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS).MethodsAccuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs).ResultsAccuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW.ConclusionsThe predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle.
Highlights
Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents
The results showed a clear superiority of BayesC compared with genomic best linear unbiased prediction (GBLUP) and Bayesian LASSO (BayesL) for carcass weight (CW), whereas for the other traits all methods performed
The prediction accuracy of direct genomic breeding values (DGV) for CW using BayesC was around 7% higher than that obtained with the GBLUP and BayesL methods
Summary
Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS). Estimated breeding values for backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS) are commonly used as selection criteria in attempts to increase meat yield and quality, and subsequently to improve the income generated from steer feedlots and calf sales [7]. The recently developed genomic selection approach is beginning to revolutionize animal breeding It refers to a genetic evaluation method that uses phenotypic data and genotypes of dense single nucleotide polymorphisms (SNPs) to estimate effects of SNPs from a training population and subsequently to predict the genetic values of selection candidates based on their genotypes [8]. Accurate genomic estimated breeding values would lead to greater genetic gain for these traits [15]
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