International Journal of Molecular Evolution and Biodiversity 2024, Vol.14, No.1, 10-17 http://ecoevopublisher.com/index.php/ijmeb 11 1 Basic Concepts of GWAS Technology 1.1 Definition and principles of GWAS Genome wide association studies (GWAS) are a research method used to search for associations between specific diseases or traits and genetic markers across the entire genome (Uffelmann et al., 2021). The core of GWAS lies in not relying on prior knowledge of candidate genes, but identifying genetic variations that affect specific diseases or traits by detecting statistical associations between thousands of single nucleotide polymorphisms (SNPs) in an individual’s genome and specific phenotypes. The working principle of GWAS is based on the theory of linkage disequilibrium (LD) in population genetics, which means that genetic markers closer to each other in the genome may be co inherited. By comparing the frequency differences of alleles at thousands of SNP loci between diseased individuals and healthy control groups, GWAS can reveal which genetic variations are associated with disease risk. This process requires a large number of samples to ensure the validity and accuracy of statistics. 1.2 Application of GWAS in human genetics Since its introduction, GWAS technology has been widely applied in human genetic research, especially in the field of disease genetics, making significant progress. GWAS has successfully identified thousands of genetic markers associated with various diseases. For example, GWAS has revealed multiple SNPs associated with type 2 diabetes (T2DM). Cirillo et al. (2018) through network analysis, pathway information, and integration of different types of biological information (such as eQTLs and gene environment interactions) (Figure 1), this study revealed the T2D gene and its possible functions at the process level. These findings not only enhance our understanding of the genetic basis of the disease, but also provide clues for the development of new prevention and treatment strategies. Figure 1 SNP-gene-pathway network (Cirillo et al., 2018) Note: The network displays 580 SNPs (green diamonds), 365 genes (circles) and 117 pathway clusters (blue squares). Black symbols indicate genes with ten or more connections to pathway clusters, and triangles indicate genes with a positive DisGeNET score GWAS is also used to study genetic differences in individual responses to specific drugs, which is of great significance for personalized healthcare. For example, in patients in the Middle East and North Africa region (MENA), genetic variations associated with VKORC1 rs9934438 and CYP2C9 rs4086116 loci were discovered through GWAS, which can explain 39% and 27% of the variability in warfarin dosage requirements (Rouby et al., 2021). GWAS conducted in a sample from Brazil found that VKORC1 and CYP2C9 polymorphisms play an important role in warfarin dose variability (Parra et al., 2015). In a GWAS study involving 1053 Swedish subjects,
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