This course comprehensively introduces the fundamental principles and practical procedures of genome-wide association studies (GWAS). The course begins by explaining basic concepts, including linkage disequilibrium (LD) and its crucial role in the construction of association maps. Subsequently, it elaborates on phenotypic analysis and quality control, distinguishing between qualitative, quantitative, and other types of characteristics, and focusing on methods such as normality tests, outlier removal, and data standardization. In terms of genotyping, the course explains the VCF format, SNP/Indel variation types, and the strategy for genotype interpolation in low-depth sequencing data and its applicability. The course deeply analyzes the influence of population structure (phylogenetic trees, principal component analysis PCA, STRUCTURE) and kinship (kinship matrix) on false positive results. The course systematically compares generalized linear models (GLM), mixed linear models (MLM), and efficient mixed model implementation methods (EMMAX, GEMMA), covering their basic principles and appropriate application scenarios. The result interpretation mainly focuses on Manhattan plots (highlighting significant SNPs) and QQ plots (evaluating false positive control), combined with multiple test correction methods (Bonferroni, FDR). Finally, LD blocks are analyzed using D' and r² indicators to determine candidate intervals around significant single nucleotide polymorphisms (SNPs), followed by gene annotation and combined with transcriptome data to prioritize the screening of functional candidate sites. This course emphasizes the crucial role of rational material selection, accurate phenotype identification, and model applicability, providing a complete framework for the analysis of complex traits.