We performed a genome-wide association study and fine mapping using two methods (single marker regression: frequentist approach and Bayesian C (BayesC): fitting selected single nucleotide polymorphisms (SNPs) in a Bayesian framework) through three high-density SNP chip platforms to analyze milk production phenotypes in Korean Holstein cattle (n = 2780). We identified four significant SNPs for each phenotype in the single marker regression model: AX-311625843 and AX-115099068 on Bos taurus autosome (BTA) 14 for milk yield (MY) and adjusted 305-d fat yield (FY), respectively, AX-428357234 on BTA 18 for adjusted 305-d protein yield (PY), and AX-185120896 on BTA 5 for somatic cell score (SCS). Using the BayesC model, we discovered significant 1-Mb window regions that harbored over 0.5% of the additive genetic variance effects for four milk production phenotypes. The concordant significant SNPs and 1-Mb window regions were characterized into quantitative trait loci (QTL). Among the QTL regions, we focused on a well-known gene (diacylglycerol O-acyltransferase 1 (DGAT1)) and newly identified genes (phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) for MY and FY, and observed that DGAT1 is involved in glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism, and PDE4B is involved in cAMP signaling. Our findings suggest that the candidate genes in QTL are strongly related to physiological mechanisms related to the fat production and consequent total MY in Korean Holstein cattle.

Objective: The objective of this study was to investigate the genetic components of daily milk yield and to re-rank bulls in South Korea by estimated breeding value (EBV) under heat stress using the temperature-humidity index (THI). Methods: This study was conducted using 125,312 monthly test-day records, collected from January 2000 to February 2017 for 19,889 Holstein cows from 647 farms in South Korea. Milk production data were collected from two agencies, the Dairy Cattle Genetic Improvement Center and the Korea Animal Improvement Association, and meteorological data were obtained from 41 regional weather stations using the Automated Surface Observing System (ASOS) installed throughout South Korea. A random regression model using the THI was applied to estimate genetic parameters of heat tolerance based on the test-day records. The model included herd-year-season, calving age, and days-in-milk as fixed effects, as well as heat tolerance as an additive genetic effect, permanent environmental effect, and direct additive and permanent environmental effect. Results: Below the THI threshold (<= 72; no heat stress), the variance in heat tolerance was zero. However, the heat tolerance variance began to increase as THI exceeded the threshold. The covariance between the genetic additive effect and the heat tolerance effect was -0.33. Heritability estimates of milk yield ranged from 0.111 to 0.176 (average: 0.128). Heritability decreased slightly as THI increased, and began to increase at a THI of 79. The predicted bull EBV ranking varied with THI. Conclusion: We conclude that genetic evaluation using the THI function could be useful for selecting bulls for heat tolerance in South Korea.

The objectives of this study were to compare identified informative regions through two genome-wide association study (GWAS) approaches and determine the accuracy and bias of the direct genomic value (DGV) for milk production traits in Korean Holstein cattle, using two genomic prediction approaches: single-step genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B. Records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected from 265,271 first parity cows. After quality control, 50,765 single-nucleotide polymorphic genotypes were available for analysis. In GWAS for ss-GBLUP (ssGWAS) and Bayes-B (BayesGWAS), the proportion of genetic variance for each 1-Mb genomic window was calculated and used to identify informative genomic regions. Accuracy of the DGV was estimated by a five-fold cross-validation with random clustering. As a measure of accuracy for DGV, we also assessed the correlation between DGV and deregressed-estimated breeding value (DEBV). The bias of DGV for each method was obtained by determining regression coefficients. A total of nine and five significant windows (1 Mb) were identified for MY305 using ssGWAS and BayesGWAS, respectively. Using ssGWAS and BayesGWAS, we also detected multiple significant regions for FY305 (12 and 7) and PY305 (14 and 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32 to 0.34), FY305 (0.37 to 0.39), and PY305 (0.35 to 0.36) traits, respectively. The mean biases of DGVs determined using the single-step and Bayesian methods were 1.50±0.21 and 1.18±0.26 for MY305, 1.75±0.33 and 1.14±0.20 for FY305, and 1.59±0.20 and 1.14±0.15 for PY305, respectively. From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of ss-GBLUP in Korean Holstein populations.

The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

Longevity is closely related to the survival rate of dairy cattle and refers to the period during which the cow has economic value, from first calving to culling. The purpose of this study was to analyze the culling patterns and survival rates of Korean Holstein cows and evaluate genetic characteristics related to parity and longevity of each lactation by using the test day milk yield collected in South Korea. The performance data of the dairy cattle were collected from 2004 to 2019 by the Nonghyup Dairy Cattle Improvement Center. The collected 1,702,304 records were used as pedigree data through the Korea Animal Improvement Association. The lactation period was divided into early-lactation (0–90 days: L1.1, L2.1, and L3.1), mid-lactation (91–299 days: L1.2, L2.2, and L3.2), and late-lactation (300 days-next parity: L1.3, L2.3, and L3.3). The heritability of longevity for the first, second, and third parity was 0.020, 0.028, and 0.039, respectively. In all parities, the heritability in late-lactation was higher than that in early- and mid-lactation. Most genetic correlations for survival in the first parity were higher than those in the second and third parities. The results of this study may serve as a basis for developing a more accurate model for evaluating longevity traits in South Korea.

Objective: In Korea, dairy cattle breeding programs have historically prioritized productive, conformation traits, leading to positive improvements, yet reproductive traits have lagged in development. This study was conducted to develop the breeding program of key reproductive traits in the Korean dairy cattle population.Methods: Utilizing data from 7,596 farms and over seven million observations, we conducted quality control to rectify manual entry errors and selected traits in line with international genetic evaluation standards. Traits analyzed included heifer conception rate (HCR), interval from calving to first insemination (CF), cow conception rate (CCR), interval from first to last insemination (FL), and days open (DO). Genetic parameters were estimated using a single trait animal model for HCR and a multiple lactation animal model for CF, CCR, FL, and DO, considering contemporary group of herd-insemination year, insemination month, and monthly age as fixed effects.Results: Results showed low heritability estimates, ranging from 0.007 to 0.035 across different traits and lactations. Theoretical reliability appears to be low on average due to the influence of heritability, but it showed sufficiently high reliability in some sires (over 0.8). In terms of genetic and phenotypic trends, capacity for reproductive traits declined for a long time until around 2014. In recent individuals, improved trend can be found.Conclusion: This study addressed the critical need for enhancing reproductive efficiency to complement the existing breeding goals, thereby supporting sustained economic viability in the dairy industry. The results underscore the need for improved data quality and methodological adjustments for reproduction records to enhance the genetic evaluation of dairy cattle in Korea.