Genome-wide association studies (GWAS) have become a powerful tool for investigating the genetic architecture of human diseases. This chapter reviews key concepts underlying GWAS, including the architecture of common diseases, the structure of common genetic variation, technologies for capturing genetic information, study designs, and statistical methods used for data analysis. It also looks ahead to the future of GWAS beyond its current applications. A central goal of human genetics is to identify genetic risk factors for common and rare diseases. GWAS focuses on measuring and analyzing DNA sequence variations across the human genome to identify genetic risk factors for common diseases. One early success of GWAS was the identification of the Complement Factor H gene as a major risk factor for age-related macular degeneration. GWAS has also been successful in pharmacogenetics, identifying DNA sequence variations associated with drug metabolism and efficacy. For example, GWAS revealed DNA sequence variations in genes that influence warfarin dosing, leading to genetic tests for warfarin dosing in clinical settings. This has given rise to personalized medicine, which aims to tailor healthcare to individual patients based on their genetic background. The modern unit of genetic variation is the single nucleotide polymorphism (SNP). SNPs are common genetic variations that occur with high frequency in the human genome. They can have functional consequences, such as causing amino acid changes, changes to mRNA transcript stability, and changes to transcription factor binding affinity. SNPs are the most abundant form of genetic variation in the human genome. The common disease/common variant (CD/CV) hypothesis suggests that common diseases are influenced by common genetic variants. This hypothesis has been supported by the discovery of susceptibility variants for common diseases with high minor allele frequency. However, common variants typically have small effect sizes and may not have high penetrance. Therefore, multiple common alleles must influence disease susceptibility. The International HapMap Project was designed to identify genetic variation across the genome and characterize correlations among variants. This project used sequencing techniques to discover and catalog SNPs in various populations. The results of this project have been used to understand linkage disequilibrium (LD), which is the degree to which alleles of one SNP are inherited or correlated with alleles of another SNP within a population. LD is a key concept in GWAS, as it allows for the identification of SNPs that are in high LD with the influential SNP. This enables the use of tag SNPs to capture the variation at nearby sites in the genome. LD is exploited to optimize genetic studies, preventing genotyping SNPs that provide redundant information. Genotyping technologies have advanced significantly, allowing for the accurate capture of alleles for a large number of SNPs. These technologies include chip-based microarray technology, which allows for the assaying of one million or more SNPs. Two primary platforms have been used for most GWAS: products from Illumina and Affymetrix. Study design is a critical component of GWAS, as it determines the ability to detect genetic effects.Genome-wide association studies (GWAS) have become a powerful tool for investigating the genetic architecture of human diseases. This chapter reviews key concepts underlying GWAS, including the architecture of common diseases, the structure of common genetic variation, technologies for capturing genetic information, study designs, and statistical methods used for data analysis. It also looks ahead to the future of GWAS beyond its current applications. A central goal of human genetics is to identify genetic risk factors for common and rare diseases. GWAS focuses on measuring and analyzing DNA sequence variations across the human genome to identify genetic risk factors for common diseases. One early success of GWAS was the identification of the Complement Factor H gene as a major risk factor for age-related macular degeneration. GWAS has also been successful in pharmacogenetics, identifying DNA sequence variations associated with drug metabolism and efficacy. For example, GWAS revealed DNA sequence variations in genes that influence warfarin dosing, leading to genetic tests for warfarin dosing in clinical settings. This has given rise to personalized medicine, which aims to tailor healthcare to individual patients based on their genetic background. The modern unit of genetic variation is the single nucleotide polymorphism (SNP). SNPs are common genetic variations that occur with high frequency in the human genome. They can have functional consequences, such as causing amino acid changes, changes to mRNA transcript stability, and changes to transcription factor binding affinity. SNPs are the most abundant form of genetic variation in the human genome. The common disease/common variant (CD/CV) hypothesis suggests that common diseases are influenced by common genetic variants. This hypothesis has been supported by the discovery of susceptibility variants for common diseases with high minor allele frequency. However, common variants typically have small effect sizes and may not have high penetrance. Therefore, multiple common alleles must influence disease susceptibility. The International HapMap Project was designed to identify genetic variation across the genome and characterize correlations among variants. This project used sequencing techniques to discover and catalog SNPs in various populations. The results of this project have been used to understand linkage disequilibrium (LD), which is the degree to which alleles of one SNP are inherited or correlated with alleles of another SNP within a population. LD is a key concept in GWAS, as it allows for the identification of SNPs that are in high LD with the influential SNP. This enables the use of tag SNPs to capture the variation at nearby sites in the genome. LD is exploited to optimize genetic studies, preventing genotyping SNPs that provide redundant information. Genotyping technologies have advanced significantly, allowing for the accurate capture of alleles for a large number of SNPs. These technologies include chip-based microarray technology, which allows for the assaying of one million or more SNPs. Two primary platforms have been used for most GWAS: products from Illumina and Affymetrix. Study design is a critical component of GWAS, as it determines the ability to detect genetic effects.