Animal Molecular Breeding 2024, Vol.14, No.1, 54-61 http://animalscipublisher.com/index.php/amb 56 2 Principles and Applications of Genome-Wide Association Analysis Genome-wide association study (GWAS), as an important method in the field of genetics research, plays a key role in revealing the connection between genes and traits. Its principle is to find the correlation between genotype and phenotype through the analysis of large-scale genotype data and phenotypic data, thereby exploring the genetic basis of trait formation (Cano-Gamez and Trynka, 2020). In the field of animal genetics, genome-wide association analysis is widely used to analyze the genetic basis of various economic traits, providing an important means for genetic improvement of livestock and poultry. 2.1 Principles of genome-wide association analysis Genome-wide association analysis is based on the concept of common variations, namely Single Nucleotide Polymorphisms (SNPs). In the study of mutton production traits, these SNP sites may be related to genetic variations in muscle tissue growth rate, fat content, disease resistance, etc. By comparing SNP genotype data and phenotypic data of large-scale individuals, the correlation between certain SNP sites and specific traits can be found (Xu and Taylor, 2009). This method does not rely on pre-set hypothetical genes or pathways, so it can comprehensively and efficiently discover the association between genes and traits. 2.2 Application of genome-wide association analysis in animal genetics In the genetic improvement of sheep, genome-wide association analysis is widely used to analyze the genetic basis of various economic traits. For example, by analyzing sheep genome data, key genes or gene regions related to production traits such as meat quality, growth rate, and coat color can be discovered. These findings not only help to understand the molecular mechanisms of trait formation, but also provide candidate markers for selective breeding, accelerating the process of genetic improvement of sheep. 2.3 The value of genome-wide association analysis in genetic improvement of livestock and poultry In the genetic improvement of livestock and poultry, genome-wide association analysis provides important technical support for precision breeding. By identifying genetic markers related to meat quality, growth rate, disease resistance, etc., breeders can more accurately select excellent individuals for breeding, thus speeding up the breeding process and improving the efficiency of genetic improvement. Genome-wide association analysis can also reveal new relationships between genetic variants and traits, providing breeders with more options. These research results are of great significance for optimizing the production traits of livestock and poultry, improving meat quality, and enhancing the disease resistance of livestock and poultry. As an efficient genetic analysis method, genome-wide association analysis has important value in the genetic improvement of livestock and poultry. By revealing the correlation between genes and traits, this method provides a scientific basis for precision breeding and promotes the optimization and improvement of livestock and poultry production traits. With the continuous advancement of technology and in-depth research, it is believed that genome-wide association analysis will play an increasingly important role in the field of livestock and poultry genetic improvement and make greater contributions to the sustainable development of the livestock and poultry industry. 3 Genome-Wide Association Analysis Method for Optimization of Mutton Production Traits 3.1 Sample collection and data processing Before conducting genome-wide association analysis, it is first necessary to collect a sufficient number and representative samples and effectively process the data (Asif et al., 2021). The selection of samples should take into account factors such as sheep breed, region, environment, etc. to ensure the reliability and representativeness of the results. For example, when selecting samples, you can consider collecting samples from sheep in different regions, different growth stages, and different breeds to fully reflect the diversity of mutton production traits. At the same time, preliminary processing is performed on the collected sample data, including data cleaning, removal of outliers, etc., to reduce noise and interference in the data and improve the accuracy and credibility of subsequent analysis.
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