Animal Molecular Breeding 2024, Vol.14, No.5, 307-317 http://animalscipublisher.com/index.php/amb 314 ML with GWAS can also facilitate the development of more sophisticated models that account for epistatic interactions and environmental factors, ultimately leading to more precise and robust genomic selection strategies (Taherkhani et al., 2022; Sahana et al., 2023). 9 Future Perspectives and Research Directions 9.1 Emerging technologies in GWAS The field of genome-wide association studies (GWAS) is rapidly evolving with the advent of new technologies and methodologies. One significant advancement is the use of high-throughput SNP genotyping technologies, such as the Illumina BovineSNP50 BeadChip, which has broadened the scope for identifying genes associated with milk production traits in dairy cattle (Jiang et al., 2010). Additionally, the integration of whole-genome sequencing data in GWAS has enhanced the precision and power of detecting quantitative trait loci (QTL) (Berg et al., 2016). Meta-analysis methods, which combine data from multiple independent studies, have also proven effective in increasing the accuracy of QTL detection and understanding the biological mechanisms underlying milk production traits (Bakhshalizadeh et al., 2021). Furthermore, the development of single-step genomic best linear unbiased prediction (ssGBLUP) methods has improved the accuracy of genomic predictions, even in populations with limited genotyped animals (Buaban et al., 2021). 9.2 Prospects of precision breeding in dairy cattle Precision breeding, which leverages genomic information to select superior candidates, holds great promise for the future of dairy cattle breeding. The use of genomic breeding values (GEBVs) has already shown significant improvements in dairy cattle productivity by enabling more accurate selection of animals with desirable traits (Gutierrez-Reinoso et al., 2021). Multibreed GWAS approaches have demonstrated the potential to refine QTL mapping and identify causal variants with greater precision, especially when QTL are segregating across breeds (Raven et al., 2014). This approach can be particularly useful in identifying candidate genes and refining QTL intervals, as seen in studies involving Holstein and Jersey cattle (Jiang et al., 2010; Pryce et al., 2010). The integration of genomic selection with precision management practices on modern dairy farms can further optimize breeding programs and enhance food production efficiency (Gutierrez-Reinoso et al., 2021). 9.3 Ethical and regulatory considerations in genetic research As the field of genetic research in dairy cattle advances, it is crucial to address the ethical and regulatory considerations associated with these technologies. One major concern is the potential for inbreeding depression due to the closed bloodlines in several milk breeds, which can negatively impact reproductive performance and overall genetic diversity (Gutierrez-Reinoso et al., 2021). To mitigate these effects, it is essential to implement strategies that promote genetic diversity and avoid excessive inbreeding. Additionally, the use of genome editing technologies, such as CRISPR, raises ethical questions regarding animal welfare and the long-term impacts on the genetic makeup of dairy cattle. Regulatory frameworks must be established to ensure that genetic research and breeding practices adhere to ethical standards and promote the well-being of the animals involved. Furthermore, transparent communication with stakeholders, including farmers, consumers, and policymakers, is necessary to build trust and ensure the responsible use of genetic technologies in dairy cattle breeding. 10 Concluding Remarks Genome-wide association studies (GWAS) have transformed our understanding of the genetic basis of milk production traits in dairy cattle. Extensive research has uncovered numerous genomic regions and candidate genes linked to key traits such as milk yield, fat percentage, protein percentage, and somatic cell score (SCS). For instance, GWAS in Thai dairy cattle identified 210 candidate genes across 19 chromosomes associated with milk production traits and 21 genes on 3 chromosomes related to SCS. Similarly, a longitudinal GWAS in Chinese Holstein cattle pinpointed multiple quantitative trait loci (QTLs) and identified 28 candidate genes, including prominent genes such as DGAT1 and novel ones like CCSER1 and CUX2. Meta-analyses have further validated QTLs across diverse populations, enhancing the precision and reliability of genetic studies. In addition, research on Brazilian Holstein and Danish Jersey cattle has reinforced the significance of well-known genes like MGST1, ABCG2, andDGAT1, while also uncovering new genes involved in crucial biological pathways.
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