With the completion of the HapMap project1 and the development of technology that allows the examination of ≥1 million genetic polymorphisms at once, genetic association studies are becoming more comprehensive. This article first provides a brief overview of the rationale for genetic association studies; it then discusses the primary features differentiating genetic from standard association studies and emphasizes these differences with an example. Finally, this article reviews methods for addressing 2 of the main pitfalls of genetic association studies: population stratification and multiple testing. The principal focus of this primer is population-based association studies using unrelated individuals. A future article will address family-based linkage and association studies. Traditional epidemiological studies focus on assessing the impact of specific risk factors on disease risk in populations. The goal of a genetic association study is to establish statistical associations between ≥1 genetic polymorphisms and phenotypes or disease states and thus to identify genetic risk factors that can later be studied in a more comprehensive manner using traditional epidemiological methods. Ideally, the statistical analyses brings us to the point where 1 or several genetic variants are identified as the potential functional variants within a gene, so that laboratory scientists can then use experimental methods to determine what functional purpose the variants have and how it might relate to the phenotype. Historically, the term polymorphism has been used to refer to genetic mutations that occur with a frequency ≥1% in the population. This article refers to genomic locations with multiple alleles interchangeably as genetic variants or polymorphisms. Pollex and Hegele2 describe many types of genetic variants found in the human genome and review the current state of knowledge concerning copy number variants and cardiovascular disease. This article focuses on single-nucleotide polymorphisms (SNPs), although much of what is presented is relevant to all types …