Development and implementation of genomic selection model for economic traits in Korean native cattle (Hanwoo)

This study investigates the genomic prediction accuracy for four carcass traits (Backfat thickness, Eye muscle area, Carcass weight and Marbling score) and one reproduction trait (Calving Interval) using customized genotyping platform (NHanwoo1) in Korean native cattle. We use 65,351 genotyped animals after the imputation with various genotyping platforms to perform genomic prediction accuracy. A 5-fold cross-validation method using K-Means clustering was employed to estimate genomic prediction accuracy. The K-Means clustering method was utilized using 142,827 pedigree data associated with the 65,351 genotypes ultimately used in the genomic analysis. Genomic prediction accuracies based on BayesC with fixed Pi value of 0.99 were estimated with the results of carcass weight at 0.722, eye muscle area at 0.711, backfat thickness at 0.594, marbling score at 0.642, and calving interval at 0.626. Based on these results of high genomic prediction accuracy, it is suggested that blending the Estimated Breeding Values (EBV) derived from BLUP methods with the MBVs derived from SNP marker effects can produce an integrated genomic estimated breeding value (GEBV) with high reliability for five economic traits in Hanwoo.