Animal Molecular Breeding 2024, Vol.14, No.4, 252-261 http://animalscipublisher.com/index.php/amb 256 predict an animal's genetic potential, are calculated using pedigree and performance data, and more recently, genomic information (Weigel et al., 2017; Gutierrez-Reinoso et al., 2021; Gutierrez-Reinoso et al., 2021). The integration of genomic selection has enhanced the accuracy of breeding values, enabling the identification of superior animals for milk production and other economically important traits. 5.3 Economic and practical impacts of quantitative genetics in the dairy industry The application of quantitative genetics in dairy cattle breeding has had profound economic and practical impacts. By improving traits such as milk yield, health, and fertility, breeders have increased the productivity and profitability of dairy operations (Miglior et al., 2017; Weigel et al., 2017). Genomic selection has accelerated genetic gains, reduced generation intervals, and allowed for more efficient use of resources (Gutierrez-Reinoso et al., 2021). Additionally, the ability to select for disease resistance and adaptability to environmental stressors has improved animal welfare and reduced veterinary costs (König and May, 2019; Narayana et al., 2022). These advancements contribute to the sustainability of the dairy industry, ensuring it can meet future challenges and consumer demands. In summary, the application of quantitative genetics in dairy cattle breeding has transformed the industry, enhancing productivity, sustainability, and animal welfare through the use of advanced genetic selection techniques. 6 Advances and Emerging Trends in Quantitative Genetics 6.1 Integration of genomics and big data in breeding programs The integration of genomics and big data has revolutionized livestock breeding programs by enhancing the accuracy and efficiency of genetic selection. Genomic selection, which utilizes DNA sequencing and statistical algorithms, allows for the prediction of genomic breeding values, thereby accelerating genetic progress in complex traits such as growth and reproduction (Adebayo et al., 2024). The use of high-density SNP chips enables the selection of genetically superior animals at an early age with high accuracy, facilitating more precise breeding decisions (Singh et al., 2019). Additionally, the combination of genomic data with on-farm sensor data supports precision management, optimizing productivity and sustainability in modern dairy farms (Weigel et al., 2017). 6.2 Role of artificial intelligence and machine learning Artificial intelligence (AI) and machine learning (ML) are playing increasingly significant roles in livestock breeding by improving the analysis and interpretation of complex genetic data. These technologies have been integrated into genomic selection processes, enhancing the prediction accuracy of breeding values and enabling more informed selection decisions (Weigel et al., 2017). Machine learning algorithms, alongside traditional mixed linear models, are now part of the modern breeder's toolkit, offering new insights into genetic inheritance and epigenetic modifications. The application of AI and ML in breeding programs is expected to continue growing, providing innovative solutions to challenges in genetic selection and management (Adebayo et al., 2024). 6.3 CRISPR and gene editing for precision breeding CRISPR and other gene-editing technologies are emerging as powerful tools for precision breeding in livestock. These technologies allow for targeted modifications of specific genes, offering the potential to enhance desirable traits and eliminate undesirable ones with unprecedented precision. Gene editing can be used to knock out or over-express genes, providing deeper insights into gene functions and their impact on animal physiology (Khare and Khare, 2017). The application of CRISPR in livestock breeding holds promise for improving disease resistance, productivity, and overall animal welfare, marking a significant advancement in the field of quantitative genetics (Gutierrez-Reinoso et al., 2021). In summary, the integration of genomics and big data, the application of AI and ML, and the use of CRISPR for gene editing are key emerging trends in quantitative genetics. These advancements are transforming livestock breeding by enhancing genetic selection accuracy, enabling precision management, and offering new possibilities for genetic improvement.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==