Background: Recently, the single-step genomic best linear unbiased prediction (ssGBLUP) method, which incorporates not only genomic information but also phenotypic information of pedigree, is under study. In this study, we performed a ssGBLUP analysis on a commercial Hanwoo population using phenotypic, genotypic, and pedigree data. Methods: The test population comprised Hanwoo 1,740 heads raised in four regions of Korea, while the reference population used Hanwoo 18,499 heads raised across the country and two-generation pedigree data. Analysis was performed using genotype data generated by the Hanwoo 50 K SNP beadchip. Results: The mean Genome estimated breeding values (GEBVs) estimated using the ssGBLUP methods for carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS) were 7.348, 1.515, -0.355, and 0.040, respectively, while the accuracy of each trait was 0.749, 0.733, 0.769, and 0.768, respectively. When the correlation analysis between the GEBVs as a result of this study and the actual slaughter performance was confirmed, CWT, EMA, BFT, and MS were reported to be 0.519, 0.435, 0.444, and 0.543, respectively. Conclusions: Our results suggest that the ssGBLUP method enables a more accurate evaluation because it conducts a genetic evaluation of an individual using not only genotype information but also phenotypic information of the pedigree. Individual evaluation using the ssGBLUP method is considered effective for enhancing the genetic ability of farms and enabling accurate and rapid improvements. It is considered that if more pedigree information of reference population is collected for analysis, genetic ability can be evaluated more accurately.

Data on primal cuts were collected from 1,829 steers of Hanwoo progeny testing programs, between 2010 and 2015 for the ssGWAS. SNP data were analyzed by using Illumina Bovine 50K Beadchip. The SNP data that matches with phenotype data was 674 animals. As a first step, the genomic estimated breeding value(GEBV) of the loin and rib cuts were estimated, which was used in the estimation of SNP marker effects and their variances related to the traits. Then, the estimated variance explained by each marker was expressed as a proportion to the total genetic variance. Finally, the SNP loci and their significance to any possible QTL were examined. Among the 20 best SNP loci explaining a larger proportion of SNP variance to the total genetic variance for tender loin yield, the region between 12,812,193 ~ 12,922,313bp on BTA 10 harbored a cluster of SNPs that explained about 7.32 to 7.34% of the total genetic variance. For strip loin yield, a peak for higher effects for multiple SNPs was found in BTA24, between 38,158,543 and 38,347,278bp distances, which explained about 8.36 to 8.56% of the observed variance for this trait. For loin yield had relatively smaller effects in terms of the total genetic variance. Therefore, loin yield might be affected by a few loci with moderate effects and many other loci with smaller effects across the genome.