Animal Molecular Breeding 2024, Vol.14, No.1, 27-35 http://animalscipublisher.com/index.php/amb 28 the genetic basis for milk production, but also provides the possibility of achieving more efficient and precise breeding goals. The application of GWAS technology in animal breeding, especially in genetic research to improve milk production, has shown enormous potential and value. Through in-depth exploration of GWAS technology and its application in breeding practice, it can not only improve the yield and quality of milk, but also contribute to the sustainable development of agricultural production (Gong et al., 2021). With the advancement of technology and the deepening of research, genetic research will continue to bring revolutionary changes to agricultural production. This study explores the genetic analysis of disease resistance in poultry and its application in poultry health management by integrating the latest advances in genetics, molecular biology, and genomics. Not only does it provide new scientific basis for the prevention and control of poultry diseases, but it also provides new ideas and methods for the sustainable development of the poultry industry. By deeply understanding the genetic resistance of poultry to diseases, we can more effectively address the challenges posed by poultry diseases and contribute to global food safety and public health. 1 The Principle and Application of GWAS Technology 1.1 Principles and Methods of GWAS Technology Genome-wide association study (GWAS) are a powerful genetic research method aimed at identifying genetic variations that affect specific traits, such as disease susceptibility, crop yield, or animal production traits. GWAS analyzes the association between thousands of single nucleotide polymorphisms (SNPs) in individual genomes and specific traits to identify the genetic reasons behind trait differences. The core of this technology is that it does not require prior assumptions about the genetic basis of traits, allowing for unbiased identification of genetic markers related to traits across the entire genome. GWAS research typically involves the following steps: collecting a sufficient number of samples and determining their genome-wide SNPs. For each SNP locus, statistical methods are used to compare the frequency differences among populations with different phenotypes (such as high and low milk production), in order to identify which genetic variations are associated with the traits of interest. Using bioinformatics tools and databases to perform functional annotation and biological interpretation of these associated genetic variations, exploring how they affect traits. 1.2 Application cases in the study of cow milk production GWAS technology has been widely applied in genetic research on cow milk production. A specific example is the large-scale GWAS conducted in Holstein cows in the United States. The research team analyzed 294,079 Holstein cows that were lactating for the first time and successfully identified new additive and dominant effects related to five production traits (including milk yield), three fertility traits, and somatic cell fraction (Li et al., 2023). This study emphasizes the potential of GWAS technology in revealing the genetic basis of complex traits, particularly in economically important traits such as milk yield. Through this method, researchers can identify key gene regions that affect milk production, such as the DGAT1 gene, which is of great significance for understanding the genetic mechanism of milk production and formulating future breeding strategies. Another study used the FarmCPU method to analyze 86,645 SNPs associated with milk production, somatic cell fraction, and body shape traits, ultimately identifying 95 genome-wide significant SNPs associated with milk production traits (Tong et al., 2023). These research results not only enhance the understanding of the genetic background of milk production traits, but also provide important genetic markers for future molecular breeding (Figure 1). These research cases highlight the powerful ability of GWAS technology in identifying genetic variations associated with milk production. Through large-scale samples and advanced statistical methods, researchers are able to reveal the complex genetic network that affects milk production, providing scientific basis for breeding work to improve milk production. Although GWAS technology has shown great potential in identifying related genetic variations, the interpretation and application of its results still need to be cautious, especially when
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