Background: Understanding the evolutionary forces related to climate changes that have been shaped genetic variation within species has long been a fundamental pursuit in biology. In this study, we generated whole-genome sequence (WGS) data from 65 cross-bred and 45 Mongolian cattle. Together with 62 whole-genome sequences from world-wide cattle populations, we estimated the genetic diversity and population genetic structure of cattle populations. In addition, we performed comparative population genomics analyses to explore the genetic basis underlying variation in the adaptation to cold climate and immune response in cross-bred cattle located in the cold region of China. To elucidate genomic signatures that underlie adaptation to cold climate, we performed three statistical measurements, fixation index (FST), log₂ nucleotide diversity (θπ ratio) and cross population composite likelihood ratio (XP-CLR), and further investigated the results to identify genomic regions under selection for cold adaptation and immune response-related traits. Results: By generating WGS data, we investigated the population genetic structure and phylogenetic relationship of studied cattle populations. The results revealed clustering of cattle groups in agreement with their geographic distribution. We detected noticeable genetic diversity between indigenous cattle ecotypes and commercial populations. Analysis of population structure demonstrated evidence of shared genetic ancestry between studied cross-bred population and both Red-Angus and Mongolian breeds. Among all studied cattle populations, the highest and lowest levels of linkage disequilibrium (LD) per Kb were detected in Holstein and Rashoki populations (ranged from ~ 0.54 to 0.73, respectively). Our search for potential genomic regions under selection in cross-bred cattle revealed several candidate genes related with immune response and cold shock protein on multiple chromosomes. We identified some adaptive introgression genes with greater than expected contributions from Mongolian ancestry into Molgolian x Red Angus composites such as TRPM8, NMUR1, PRKAA2, SMTNL2 and OXR1 that are involved in energy metabolism and metabolic homeostasis. In addition, we detected some candidate genes probably associated with immune response-related traits. Conclusion: The study identified candidate genes involved in responses to cold adaptation and immune response in cross-bred cattle, including new genes or gene pathways putatively involved in these adaptations. The identification of these genes may clarify the molecular basis underlying adaptation to extreme environmental climate and as such they might be used in cattle breeding programs to select more efficient breeds for cold climate regions.

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

The investigation of carcass traits to produce meat with high efficiency has been in focus on Japanese Black cattle since 1972. To implement a successful breeding program in carcass production, a comprehensive understanding of genetic characteristics and relationships between the traits is of paramount importance. In this study, genomic heritability and genomic correlation between carcass traits, including carcass weight (CW), rib eye area (REA), rib thickness (RT), subcutaneous fat thickness (SFT), yield rate (YI), and beef marbling score (BMS) were estimated using the genomic data of 9,850 Japanese Black cattle (4,142 heifers and 5,708 steers). In addition, we investigated the effect of genetic relatedness degree on the estimation of genetic parameters of carcass traits in sub-populations created based on different GRM-cutoff values. Genome-based restricted maximum likelihood (GREML) analysis was applied to estimate genetic parameters. Using all animal data, the heritability values for carcass traits were estimated as moderate to relatively high magnitude, ranging from 0.338 to 0.509 with standard errors, ranging from 0.014 to 0.015. The genetic correlations were obtained low and negative between SFT and REA [−0.198 (0.034)] and between SFT and BMS [−0.096 (0.033)] traits, and high and negative between SFT and YI [−0.634 (0.022)]. REA trait was genetically highly correlated with YI and BMS [0.811 (0.012) and 0.625 (0.022), respectively]. In sub-populations created based on the genetic-relatedness ceiling, the heritability estimates ranged from 0.212 (0.131) to 0.647 (0.066). At the genetic-relatedness ceiling of 0.15, the correlation values between most traits with low genomic correlation were overestimated while the correlations between the traits with relatively moderate to high correlations, ranging from 0.380 to 0.811, were underestimated. The values were steady at the ceilings of 0.30–0.95 (sample size of 5,443–9,850) for most of the highly correlated traits. The results demonstrated that there is considerable genetic variation and also favorable genomic correlations between carcass traits. Therefore, the genetic improvement for the traits can be simultaneously attained through genomic selection. In addition, we observed that depending on the degree of relationship between individuals and sample size, the genomic heritability and correlation estimates for carcass traits may be different.

Meat quality and carcass traits are the most economically important traits affecting the value and the quality characteristics of the animal at slaughter. In this study, we analyzed the genomes of 295 domestic cattle from 20 global breeds using next-generation sequencing to identify signatures of positive selection associated with meat production and quality traits. The results of population genetic analysis revealed genetic differentiation among worldwide cattle groups. We observed clustering of samples in agreement with their geographic origins and production characteristics. We further observed noticeable genetic variety between cattle groups from different geographical regions. Generally, the lowest genetic diversity was determined for commercial cattle breeds, while the highest genetic diversity was found in African local cattle breeds. Our search for putative selective genomic regions in beef cattle populations revealed several candidate genes such as BMP2, EGR1, MAGEL2, U6, TMEM201, ELK3 and 5S_rRNA that were previously explored to be associated with carcass traits and meat quality in beef cattle. The enriched pathways and candidate genes discovered in this study could supply a basis for future improvement through the use of whole-genome technologies and selective breeding.

Crossbreeding is a cornerstone of modern livestock improvement, combining desirable traits to enhance productivity and environmental resilience. This study conducts the first comprehensive genomic analysis of Red Angus × Chinese Red Steppe (RACS) crossbred cattle, evaluating their genetic architecture, diversity, and selection signatures relative to founder breeds (Red Angus and Chinese Red Steppe) and global populations. A total of 119 cattle, comprising 104 RACS crossbreds and 15 Chinese Red Steppes cattle, were genotyped using the GGP Bovine 100k SNP array. Additionally, the public available genotypic data generated using the BovineSNP50 chip from 550 animals across eight beef breeds (Angus, Hereford, Limousin, Charolais, Mongolian, Shorthorn, Red Angus, and Simmental) and one dairy breed (Holstein) were incorporated into the analysis. We aimed to (1) define the population structure of RACS cattle, (2) quantify their genomic diversity and inbreeding levels, and (3) pinpoint regions under selection linked to adaptive and economic traits. We employed runs of homozygosity (ROH) and population differentiation (Fst) analyses to detect selection signals. The results revealed that the crossbred (RACS), Angus, and Red Angus breeds exhibited similar clustering patterns in principal component analysis (PCA), but the crossbred population showed the highest nucleotide diversity and lowest inbreeding coefficients compared to other breeds. Notably, candidate regions associated with immune response, cold adaptation, and carcass traits were identified within the RACS population. These findings enhance our understanding of the genetic makeup of crossbred beef cattle and highlight their potential for genetic improvement, informing future selection and breeding strategies aimed at optimizing beef production in challenging environments.

Context: Fat colour is one of the most important economic traits in the marketing of beef. There are many factors that affect fat colour, such as breed, age, diet and gender. Fat colour is observed in different ranges of colours, including white, yellow and brown. The main issue with improving fat colour is that consumer preferences of fat colour vary across the globe. Therefore, investigating the metabolic mechanisms of fat colour may provide new biomarkers for phenotyping, so as to develop effective selection strategies to achieve the locally desired fat colour. Aims: This study aimed to perform a comparative metabolic analysis between white and yellow fat from crossbred cattle so as to identify potential biomarkers for the selection of fat colour and to better understand the metabolism of white and yellow fat depots. Methods: Carcass samples of subcutaneous fat were collected from crossbred cattle (Simmental × Mongolian cattle) and scored for fat colour. Liquid chromatography–mass spectrophotometry analysis of extracted metabolites from the subcutaneous fat of six animals with white fat and six animals with yellow fat was performed. Key results: The comparison between metabolites of white and yellow fat colour samples indicated that there were five categories of 235 significant metabolites, which included hydrocarbons, lipids and lipid-like molecules, organic acids and their derivatives, organic oxygen compounds and organoheterocyclic compounds. The principal-component analysis illustrated that yellow and white fat samples can be classified in groups; however, the metabolites of white fat samples showed greater variation than those in the yellow fat. In the white fat, there were 163 metabolites that had a higher relative abundance than in yellow fat and 72 that had a lower relative abundance than in yellow fat. 3-Hydroxyoctanoic acid, anethofuran, 9,10-DiHODE, furanoeremophilane, pregeijerene, N-glycolylneuraminic acid, and glycocholic acid were identified as the metabolites that differed the most in abundance between the white and yellow fat samples. Conclusions: This study has provided insights into the metabolic differences between white and yellow fat depots and identified key metabolites associated with beef fat colour. Implications: This study has provided potential biomarkers that may be useful for selection of beef fat colour in live animals.