Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture

Genetics Selection Evolution

Research Article

Deuk Hwan Lee¹  Mohammad Hossein Moradi²  Hossein Mehrban³  Masoumeh Naserkheil⁴  Chung IlCho⁵  Noelia Ibáñez-Escriche⁶ 

[1] Department of Animal Life and Environment Science, Hankyong National University, Jungang-ro 327, 456-749, Anseong-si, Gyeonggi-do, Korea;Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, 38156-8-8349, Arāk, Iran;Department of Animal Science, Shahrekord University, P.O. Box 115, 88186-34141, Shahrekord, Iran;Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, P.O. Box 4111, 31587-11167, Karaj, Iran;Hanwoo Improvement Center, National Agricultural Cooperative Federation, Haeun-ro 691, Unsan-myeon, 356-831, Seosan-si, Chungnam-do, Korea;The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, UK;

关键词: Quantitative Trait Locus;    Genomic Selection;    Beef Cattle;    Genomic Prediction;    Carcass Weight;   

DOI  :  10.1186/s12711-016-0283-0

 received in 2015-12-17, accepted in 2016-12-22,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2017

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