Korean peninsula weather is rapidly becoming subtropical due to global warming. In summer 2018, South Korea experienced the highest temperatures since the meteorological observations recorded in 1907. Heat stress has a negative effect on Holstein cows, the most popular breed of dairy cattle in South Korea, which is susceptible to heat. To examine physiological changes in dairy cows under heat stress conditions, we analyzed the profiles circulating microRNAs isolated from whole blood samples collected under heat stress and non-heat stress conditions using small RNA sequencing. We compared the expression profiles in lactating cows under heat stress and non-heat stress conditions to understand the regulation of biological processes in heat-stressed cows. Moreover, we measured several heat stress indicators, such as rectal temperature, milk yield, and average daily gain. All these assessments showed that pregnant cows were more susceptible to heat stress than non-pregnant cows. In addition, we found the differential expression of 11 miRNAs (bta-miR-19a, bta-miR-19b, bta-miR-30a-5p, and several from the bta-miR-2284 family) in both pregnant and non-pregnant cows under heat stress conditions. In target gene prediction and gene set enrichment analysis, these miRNAs were found to be associated with the cytoskeleton, cell junction, vasculogenesis, cell proliferation, ATP synthesis, oxidative stress, and immune responses involved in heat response. These miRNAs can be used as potential biomarkers for heat stress.

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time.

Understanding the genetic basis of locally adapted indigenous cattle populations is essential to design appropriate strategies and programs for their genetic improvement and conservation. Here, we report genetic diversity measures, population differentiation, and structure of 218 animals sampled from six indicine cattle populations of Bangladesh. Animals were genotyped with Illumina Bovine SNP50K BeadChip along with genotyped data of 505 individuals included from 19 zebu and taurine breeds worldwide. The principal component analysis (PCA) showed clear geographic separation between taurine and indicine lineages where Bangladeshi indigenous cattle clustered with South Asian zebu populations. However, overlapped clusters in PCA, heterozygosity estimates, and Neighbor-Joining phylogenetic tree analysis revealed weak genetic differentiation among the indigenous cattle populations of Bangladesh. The admixture analysis at K = 5 and 9 suggests distinct genetic structure of the studied populations along with 1 to 4% of taurine ancestry. The effective population size suggested a limited pool of ancestors particularly for Sahiwal and North Bengal Grey cattle. In conclusion, these findings shed insights into the genetic architecture of six indigenous cattle populations of Bangladesh for the first time and suggested as distinct gene pools without potential admixture with zebu or taurine populations.

The Sapsaree is a breed of dog (Canis familiaris) native to Korea, which became perilously close to extinction in the mid-1980s. However, with systematic genetic conservation and restoration efforts, this breed was rescued from extinction and population sizes have been gradually increasing over the past few decades. The aim of this study was to ascertain novel information about the genetic diversity, population structure, and demographic history of the Sapsaree breed using genome-wide single nucleotide polymorphism data. We characterized the genetic profile of the Sapsaree breed by comparison with seven foreign dog breeds with similar morphologies to estimate genetic differentiation within and among these breeds. The results suggest that Sapsarees have higher genetic variance compared with the other breeds analyzed. The majority of the Sapsarees in this study share a discrete genetic pattern, although some individuals were slightly different, possibly as a consequence of the recent restoration process. Concordant results from analyses of linkage disequilibrium, effective population size, genetic diversity, and population structural analyses illustrate a relationship among the Sapsaree and the Tibetan breeds Tibetan terrier and Lhasa Apso, and a small genetic introgression from European breeds. The effective population size of the Sapsaree has contracted dramatically over the past generations, and is currently insufficient to maintain long-term viability of the breed’s genetic diversity. This study provides novel insights regarding the genetic diversity and population structure of the native Korean dog breed Sapsaree. Our results suggest the importance of a strategic and systematic approach to ensure the genetic diversity and the authenticity of the Sapsaree breed.

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor,α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.