This chapter provides an overview of Genome-Wide Association Studies (GWAS), a powerful tool for investigating the genetic architecture of human diseases. It covers key concepts such as the structure of common diseases, genetic variation, technologies for capturing genetic information, study designs, and statistical methods for data analysis. The chapter highlights the importance of understanding the biological basis of genetic effects to develop new prevention and treatment strategies. It also discusses the application of GWAS in pharmacogenetics, leading to the development of personalized medicine and genetic testing. The chapter delves into the concepts underlying GWAS study design, including single nucleotide polymorphisms (SNPs), linkage disequilibrium (LD), and the common disease/common variant hypothesis. It explains how LD is used to optimize genetic studies and how indirect associations can occur when the influential SNP is not directly typed. The chapter also covers genotyping technologies, standardized phenotype criteria, and the extraction of phenotypes from electronic medical records. Statistical methods for association testing, including single-locus and multi-locus analyses, are discussed, along with corrections for multiple testing and the importance of replication and meta-analysis. Finally, the chapter addresses data imputation to ensure consistent genotypes across different studies.This chapter provides an overview of Genome-Wide Association Studies (GWAS), a powerful tool for investigating the genetic architecture of human diseases. It covers key concepts such as the structure of common diseases, genetic variation, technologies for capturing genetic information, study designs, and statistical methods for data analysis. The chapter highlights the importance of understanding the biological basis of genetic effects to develop new prevention and treatment strategies. It also discusses the application of GWAS in pharmacogenetics, leading to the development of personalized medicine and genetic testing. The chapter delves into the concepts underlying GWAS study design, including single nucleotide polymorphisms (SNPs), linkage disequilibrium (LD), and the common disease/common variant hypothesis. It explains how LD is used to optimize genetic studies and how indirect associations can occur when the influential SNP is not directly typed. The chapter also covers genotyping technologies, standardized phenotype criteria, and the extraction of phenotypes from electronic medical records. Statistical methods for association testing, including single-locus and multi-locus analyses, are discussed, along with corrections for multiple testing and the importance of replication and meta-analysis. Finally, the chapter addresses data imputation to ensure consistent genotypes across different studies.