Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L SB ) and that for the production of cows (L SC ) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L DB ) and cows (L DC ) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.a These authors contributed equally to this work.

Title of the manuscript: Genetic characteristics and diversity of Korean Jeju Black cattle by whole genome SNP markers.

ObjectiveA genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms.MethodsThe data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs.ResultsThe accuracies under the GBLUP model for BFT and CWT ranged from 0.33–0.72 and 0.33–0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies.ConclusionThe genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.

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