Animal Molecular Breeding 2024, Vol.14, No.5, 307-317 http://animalscipublisher.com/index.php/amb 308 different phenotypes to find genetic markers, such as single nucleotide polymorphisms (SNPs), that occur more frequently in individuals with a particular trait. GWAS has been widely applied in both human and animal genetics to uncover the genetic basis of complex traits and diseases (Jiang et al., 2019; Ma, 2020; Wang et al., 2020). 2.2 History and evolution of GWAS in livestock research The application of GWAS in livestock, particularly in dairy cattle, has evolved significantly since its inception. Initially, GWAS in livestock focused on simple linear regression models to identify quantitative trait loci (QTL) associated with economically important traits such as milk production. Over time, more sophisticated statistical models, including mixed models and Bayesian approaches, were developed to account for population structure and relatedness among individuals, which are common in livestock populations (Jiang et al., 2010; Ma, 2020; Bakhshalizadeh et al., 2021). The first GWAS in dairy cattle were conducted in the early 2000s, and since then, the field has seen rapid advancements. The availability of high-density SNP chips, such as the Illumina BovineSNP50 BeadChip, has enabled researchers to perform more comprehensive and accurate genome scans. These technological advancements have led to the identification of numerous QTLs and candidate genes associated with milk production and other important traits (Jiang et al., 2010; Chen et al., 2018; Jiang et al., 2019). 2.3 Methodologies and statistical models used in GWAS Several methodologies and statistical models are employed in GWAS to identify genetic associations with traits of interest. The choice of method depends on the study design, population structure, and the specific traits being investigated. Commonly used methods include: Single-SNP Analysis: This approach tests each SNP individually for association with the trait. It is simple and computationally efficient but may lack power to detect associations in the presence of complex genetic architectures (Pryce et al., 2010; Wang et al., 2020). Haplotype-Based Analysis: Instead of analyzing individual SNPs, this method considers haplotypes, which are combinations of alleles at adjacent loci. Haplotype-based analysis can increase the power to detect associations by capturing the combined effect of multiple SNPs (Pryce et al., 2010; Chen et al., 2018). Mixed Linear Models (MLM): MLMs account for population structure and relatedness among individuals by incorporating random effects. This method is particularly useful in livestock populations where individuals are often related. MLMs have become the standard approach in GWAS for dairy cattle (Jiang et al., 2019; Ma, 2020; Wang et al., 2020). Bayesian Approaches: These methods use prior information and probabilistic models to estimate the effects of SNPs. Bayesian approaches can handle complex genetic architectures and provide more accurate estimates of SNP effects, especially in studies with small sample sizes (Ma, 2020). Meta-Analysis: This method combines data from multiple GWAS to increase the power to detect associations. Meta-analysis can identify QTLs with higher precision and reveal consistent genetic associations across different populations and studies (Bakhshalizadeh et al., 2021; Taherkhani et al., 2022). 3 Milk Production Traits and Their Genetic Basis 3.1 Key traits influencing milk production Milk production in dairy cattle is influenced by several key traits, including milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP), and milk protein percentage (PP). These traits are critical for the economic viability of dairy farming as they directly impact the quantity and quality of milk produced. Studies have shown that these traits are highly heritable and can be significantly influenced by genetic factors (Jiang et al., 2010; Wang et al., 2019; Teng et al., 2023). For instance, milk yield is a primary trait of interest due to its direct correlation with dairy farm profitability, while fat and protein percentages are essential for determining milk quality and processing characteristics (Chen et al., 2018; Bakhshalizadeh et al., 2021; Kim et al., 2021).
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