Animal Molecular Breeding 2024, Vol.14, No.1, 27-35 http://animalscipublisher.com/index.php/amb 29 considering the complexity of genetic diversity and environmental factors. Further research and validation are needed to successfully translate GWAS findings into practical breeding strategies. Figure 1 Application of GWAS technology in identifying genetic variations related to milk production 1.3 Advantages and limitations of GWAS technology The main advantage of GWAS technology is its ability to identify genetic variations related to complex traits across the entire genome without requiring prior knowledge about candidate genes. This is crucial for understanding the genetic basis of complex traits, as these traits are often influenced by multiple genes and interactions between genes and the environment. GWAS can reveal unknown or unexpected biological pathways, providing a new perspective for the study of disease mechanisms and genetic improvement (Lu et al., 2021). GWAS technology also has some limitations. It requires a large sample size to obtain sufficient statistical power, especially when it comes to small effect genetic variations. Many associations in GWAS results may need to be validated through subsequent research, as statistical associations are not directly equivalent to causal relationships. Although GWAS can identify genetic variations associated with traits, the biological functions and mechanisms of action of these variations often require further functional research to elucidate. 2 Transformation from GWAS to Breeding Practice 2.1 How GWAS identifies key genes and variations that affect milk production Genome-wide association study (GWAS) are a powerful scientific approach that identifies genetic variations associated with specific traits such as milk yield by analyzing the entire genome. The core of this method is to find the statistical correlation between specific single nucleotide polymorphisms (SNPs) and traits. Through this approach, GWAS research can reveal which genetic variations have a significant impact on milk yield, providing scientific basis for breeding (Korkuć et al., 2020). Sample collection and genotype analysis: The study began by collecting a large number of individual DNA samples. These samples come from cows with different milk yields. Using high-throughput sequencing technology for genotype analysis of these samples, thousands of SNPs were identified. Statistical analysis: Statistical analysis is the core component of GWAS. Researchers used complex statistical models to compare the frequency distribution of SNPs in cattle herds with different milk yields. If a certain SNP has a significantly higher frequency in the high-yielding milk population than in the low yielding milk population, then this SNP may be related to milk yield. Gene mapping and functional research: After identifying SNPs related to milk production, researchers will further investigate the gene regions in which these SNPs are located and how these genes affect milk production. This may involve research on the expression patterns, regulatory mechanisms, and biological functions of specific genes.
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