International Journal of Molecular Zoology 2024, Vol.14, No.1, 44-53 http://animalscipublisher.com/index.php/ijmz 46 1.3 Case study on the genetic mechanism of behavioral traits in livestock Through genome-wide association analysis (GWAS) and other molecular genetic methods, researchers have identified genes and genetic markers associated with important behavioral traits in multiple livestock species. Here are several representative cases: Stress response: In pigs, multiple genes related to stress response have been discovered, such as the NR3C1 gene, which is involved in regulating the response to cortisol and affecting the stress sensitivity of pigs (Niu et al., 2023). Social behavior: In poultry, research has identified genetic markers related to social ranking, which are closely related to animal social interaction and population structure formation. Reproductive behavior: In cattle, multiple genetic loci related to sexual behavior and reproductive efficiency were identified through GWAS analysis, providing molecular genetic evidence for improving cattle reproductive performance. These case studies not only reveal the genetic basis of behavioral traits in livestock, but also provide possible pathways for optimizing these traits through genetic selection and improvement. Understanding and utilizing the genetic mechanisms of behavioral traits can help livestock practitioners develop more scientific management and breeding strategies, thereby improving production efficiency and animal welfare. 2 Principles and Methods of Genome-Wide Association Analysis (GWAS) 2.1 Basic principles and technical processes of GWAS Genome-Wide Association Analysis (GWAS) is a research method used to identify genetic variations associated with specific traits across the entire genome. Since its introduction, GWAS has become an extremely powerful tool in genetic research, especially in revealing the genetic basis of complex traits, including behavioral traits in livestock. The core principle of GWAS is based on genotyping and phenotype observation of a large number of individuals to discover statistical correlations between genotypes and phenotypes. This process involves the following key steps: Sample collection and phenotype data recording: Select a sufficient number of research subjects and record their phenotype data in detail, such as the specific manifestations of behavioral traits. DNA sample extraction and genotyping: DNA is extracted from each individual and high-throughput genotyping technology is used to measure single nucleotide polymorphisms (SNPs) across the entire genome. Association analysis: Using statistical methods to analyze the correlation between genotype data and phenotype data, in order to identify genetic markers significantly associated with the target trait. 2.2 Sample preparation, genotype analysis, and statistical methods Sample preparation: Choosing the appropriate sample set is crucial for the success of GWAS. This usually requires a sufficiently large sample size to ensure sufficient statistical ability to detect even small effects of genetic variation. The sample should represent the research target population as much as possible to avoid selection bias (Deng et al., 2022). Genotype analysis: Currently, high-density SNP chips and next-generation sequencing technology are the main tools for genotype analysis in GWAS. These technologies can efficiently identify millions of SNPs across the entire genome. Statistical methods: GWAS typically uses multiple statistical models to analyze the relationship between genotype and phenotype, including linear regression, logistic regression, etc. The key is to adjust potential confounding variables such as age, gender, and group structure to ensure the accuracy of the analysis results.
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