Abstract
The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using customized Hanwoo 50K SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent.
Highlights
The Hanwoo (Korean native cattle) is well known for extensive marbling, tenderness, juiciness, and the characteristic flavor of its beef [1]
The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation because it is easier to implement and less computationally demanding than the Bayesian Alphabet methods
GBLUP usually assumes that all single nucleotide polymorphisms (SNPs) explain the same fraction of genetic variance [6,7], which is unlikely in the case of traits with different architectures and those influenced by major SNP
Summary
The Hanwoo (Korean native cattle) is well known for extensive marbling, tenderness, juiciness, and the characteristic flavor of its beef [1]. With the steadfast advances in high-throughput genotyping and the constant availability of molecular data, commercial application of genomic selection has been rapidly adopted for the improvement of livestock species [2,3]. This genomic selection has not yet been fully implemented in Hanwoo breeding schemes, though it is thought to be advantageous in small populations [4,5]. GBLUP usually assumes that all single nucleotide polymorphisms (SNPs) explain the same fraction of genetic variance [6,7], which is unlikely in the case of traits with different architectures and those influenced by major SNP. Many others explored the use of various weights for GBLUP in a single-trait evaluation, either weighting SNPs individually or assigning a common weight to adjacent SNPs [9,10]
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