Selective genotyping to implement genomic selection in beef cattle breeding

A review of emerging technologies, nutritional practices, and management strategies to improve intramuscular fat composition in beef cattle

The flavour, tenderness and juiciness of the beef are all impacted by the composition of the intramuscular fat (IMF), which is a key determinant of beef quality. Thus, enhancing the IMF composition of beef cattle has become a major area of research. Consequently, the aim of this paper was to provide insight and synthesis into the emerging technologies, nutritional practices and management strategies to improve IMF composition in beef cattle. This review paper examined the current knowledge of management techniques and nutritional approaches relevant to cattle farming in the beef industry. It includes a thorough investigation of animal handling, weaning age, castration, breed selection, sex determination, environmental factors, grazing methods, slaughter weight and age. Additionally, it rigorously explored dietary energy levels and optimization of fatty acid profiles, as well as the use of feed additives and hormone implant techniques with their associated regulations. The paper also delved into emerging technologies that are shaping future beef production, such as genomic selection methods, genome editing techniques, epigenomic analyses, microbiome manipulation strategies, transcriptomic profiling approaches and metabolomics analyses. In conclusion, a holistic approach combining genomic, nutritional and management strategies is imperative for achieving targeted IMF content and ensuring high-quality beef production.

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

The aim of this study was to assess the impact of incorporating genomic data using the single-step genomic best linear unbiased prediction (ssGBLUP) method compared to the best linear unbiased prediction (BLUP) method on the reliability of breeding values for age at first calving, calving interval, and productive longevity at 78 months in Charolais cattle. The study included 48,590 purebred Charolais individuals classified into four subgroups based on genotyping and performance records. The results showed that considering genotypes significantly improved genomic estimated breeding values (GEBV) reliability across all categories except nongenotyped individuals. For young genotyped individuals, the increase in reliability was up to 27% for both sexes. The highest average reliability was achieved for genotyped proven bulls and cows with performance records, and the inclusion of genomic data further improved the reliability by up to 22% and 21% for cows and bulls, respectively. The gain in reliability was observed mainly during the first three calvings, and then the differences decreased. The imported individuals showed lower estimated breeding values (EBV) and GEBV reliabilities than the domestic population, probably due to the weak genetic connection with the domestic population. However, when the progeny of imported heifers were sired by domestic bulls, the reliability increased by up to 24%. For nongenotyped individuals, only a slight increase in reliability was observed; however, the number of genotyped individuals in the population was still relatively small.

Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle-A Review

Female fertility is the foundation of the cow-calf industry, impacting both efficiency and profitability. Reproductive failure is the primary reason why beef cows are sold in the U.S. and the cause of an estimated annual gross loss of USD 2.8 billion. In this review, we discuss the status of the genomics, transcriptomics, and systems genomics approaches currently applied to female fertility and the tools available to cow-calf producers to maximize genetic progress. We highlight the opportunities and limitations associated with using genomic and transcriptomic approaches to discover genes and regulatory mechanisms related to beef fertility. Considering the complex nature of fertility, significant advances in precision breeding will rely on holistic, multidisciplinary approaches to further advance our ability to understand, predict, and improve reproductive performance. While these technologies have advanced our knowledge, the next step is to translate research findings from bench to on-farm applications.