Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle.

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.

Case report: Investigation of genetic mutations in a case of schistosomus reflexus in a Holstein dairy cattle fetus in Korea.

Schistosomus reflexus (SR) is one of the most common congenital anomalies found in cases of cattle dystocia; this disorder occurs mostly in cattle. Congenital anomalies such as SR are caused by various genetic and environmental factors, but no specific cause has been elucidated for SR. This study reports a case of SR in a Holstein dairy cattle fetus with congenital anomalies in Korea. Grossly, a distinct spine curvature was observed between the thoracic and lumbar vertebrae, accompanied by a consequential malformation from the sacrum to the occipital bone. Furthermore, the thoracic and abdominal organs were exposed. In computed tomography (CT) images, mild and severe kyphoscoliosis was observed in T1~11 and L1~6, respectively. Additionally, vertebral dysplasia was observed in S1~5 and Cd 1~5. To pinpoint the causal genes and mutations, we leveraged a custom 50K Hanwoo SNP-Chip and the Online Mendelian Inheritance in Animals (OMIA) database. As a result, we identified a nonsense mutation in apoptotic protease activating factor 1 (APAF1) within HH1 that was associated with a decrease in conception rate and an increase in abortion in Holstein dairy cattle. The genotype of the SR case was A/A, and most of the 1,142 normal Holstein dairy cattle tested as a control group had the genotype G/G. In addition, the A/A genotype did not exist in the control group. Based on the pathological, genetic, and radiological findings, the congenital abnormalities observed were diagnosed as SR.

Genome-Wide Association Studies for Body Conformation Traits in Korean Holstein Population.

The objective of this study was to identify quantitative trait loci (QTL) and nearby candidate genes that influence body conformation traits. Phenotypic data for 24 body conformation traits were collected from a population of 2329 Korean Holstein cattle, and all animals were genotyped using the 50 K Illumina bovine SNP chip. A total of 24 genome-wide significant SNPs associated with 24 body conformation traits were identified by genome-wide association analysis. The selection of the most promising candidate genes was based on gene ontology (GO) terms and the previously identified functions that influence various body conformation traits as determined in our study. These genes include KCNA1, RYBP, PTH1R, TMIE, and GNAI3 for body traits; ANGPT1 for rump traits; MALRD1, INHBA, and HOXA13 for feet and leg traits; and CDK1, RHOBTB1, and SLC17A1 for udder traits, respectively. These findings contribute to our understanding of the genetic basis of body conformation traits in this population and pave the way for future breeding strategies aimed at enhancing desirable traits in dairy cattle.

Case Study: Improving the Quality of Dairy Cow Reconstruction with a Deep Learning-Based Framework.

Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers. With applications in animal husbandry, we can analyze the characteristics of the body parts of a dairy cow to improve its fertility and milk production efficiency. However, in the depth image generation from stereo data, previous solutions using traditional stereo matching algorithms have several drawbacks, such as poor-quality depth images and missing information in overexposed regions. Additionally, the use of one camera to reconstruct a comprehensive 3D point cloud of the dairy cow has several challenges. One of these issues is point cloud misalignment when combining two adjacent point clouds with the small overlapping area between them. In addition, another drawback is the difficulty of point cloud generation from objects which have little motion. Therefore, we proposed an integrated system using two cameras to overcome the above disadvantages. Specifically, our framework includes two main parts: data recording part applies state-of-the-art convolutional neural networks to improve the depth image quality, and dairy cow 3D reconstruction part utilizes the simultaneous localization and calibration framework in order to reduce drift and provide a better-quality reconstruction. The experimental results showed that our approach improved the quality of the generated point cloud to some extent. This work provides the input data for dairy cow characteristics analysis with a deep learning approach.

The Estimation of Genetic Parameters for Longevity According to Lactation Period Using a Multiple Trait Animal Model in Korean Holstein Cows.

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.

Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle.

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.

The effectiveness of genomic selection for milk production traits of Holstein dairy cattle.

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 (LSB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) 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.

Comparison of genome-wide association and genomic prediction methods for milk production traits in Korean Holstein cattle.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Estimation of the genetic milk yield parameters of Holstein cattle under heat stress in South Korea.

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.

Data on transcriptome profiling of circulating microRNAs in dairy cattle.

MicroRNA (miRNA) are found in numerous biofluids including blood and are considered a new class of biomarkers. The data presented here are related to the research article entitled "Profiling and identification of pregnancy-associated circulating microRNAs in dairy cattle" (Markkandan et al. 2018). In the cited article, we sequenced the circulating microRNAs of the three healthy dairy cows of normal and 30 days of pregnancy (DOP) using Illumina RNA-Seq. Differentially expressed genes (DEG) analysis between normal and pregnant samples showed perturbations in miRNA expression. Herein, we made a comparison of DEGs at normal and 60 DOP libraries. The analysis results showed that 147 known miRNAs were differently expressed at 60 DOP groups when compared to the normal group. In addition, stage specific miRNAs were also predicted.

Profiling and identification of pregnancy-associated circulating microRNAs in dairy cattle.

Holstein is one among the dairy cattle which provide higher milk yields than most other cattle breeds. Lack of high-accuracy, reliable methods for early detection of cattle pregnancy reduces overall productivity and constitutes a high economic burden to the dairy industry. The circulating microRNAs (miRNAs) in exosomes could provide information and serve as potential biomarkers for livestock health and disease. However, the complexity of miRNA in response to cattle early pregnancy remains unknown. Hence, we collected blood samples of three healthy dairy cows of normal and 30 days of pregnancy, in order to further characterize the miRNA transcriptome profile. A high-throughput RNA-Seq approach detected 794 known and 2154 novel circulating miRNAs in six libraries. A total of 29 miRNAs in the 30 days of pregnancy group showed significant differences compared to the normal group. Further, bta-miR-450b, bta-miR-146b, bta-miR-26b and bta-miR-27b were up-regulated which shown to be involved in preeclampsia, immune response and mammary gland development. GO enrichment analysis showed these target genes were involved in the metabolic process, signal transducer activity, and membrane etc., while KEGG analysis showed that these genes were enriched in membrane trafficking, chromosome and associated proteins, exosome and G protein-coupled receptors pathways. These results provide an experimental basis to reveal the potential role of miRNAs as biomarkers in early diagnosis of pregnancy and other molecular functions.

Hanwoo cattle: origin, domestication, breeding strategies and genomic selection.

Hanwoo (Korean cattle) is the native, taurine type of cattle breed of Korea and its history as a draft animal dates back to 5000 Years. In earlier times Hanwoo was used extensively for farming, transportation. Over the period of time, Hanwoo has changed to be meat type cattle. Full-scale production of Hanwoo as meat-type cattle has occurred since 1960s with the rapid growth of the Korean economy. Hanwoo is one of the most economically important species in Korea as it is a significant source of nutrition to the Korean people. Hanwoo beef is the most cherished food of Korea. One of the main goals of researchers is to increase the meat quality, quantity and taste of the beef. In this review we describe the origin, domestication of Hanwoo cattle and breeding program initiated from 1980's. Moreover the advent of technological advancement had provided us a platform to perform genome wide selection on economic traits and its implementation into traditional breeding programs.