How to Interpret a Genome-wide Association Study

TLDR

Genome‑wide association studies use high‑throughput genotyping to test hundreds of thousands of SNPs for association with traits, offering a powerful discovery tool for genetic influences on disease while also facing limitations such as false positives, selection bias, and genotyping errors. The article aims to explain the design, interpretation, application, and limitations of GWA studies for clinicians and scientists. It reviews the typical GWA study workflow, from genotyping large cohorts to statistical association testing and replication of loci. Since 2005, GWA studies have identified and replicated about 100 loci for 40 common diseases and traits, many in unexpected genes or gene‑free regions.

Abstract

Genome-wide association (GWA) studies use high-throughput genotyping technologies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) and relate them to clinical conditions and measurable traits. Since 2005, nearly 100 loci for as many as 40 common diseases and traits have been identified and replicated in GWA studies, many in genes not previously suspected of having a role in the disease under study, and some in genomic regions containing no known genes. GWA studies are an important advance in discovering genetic variants influencing disease but also have important limitations, including their potential for false-positive and false-negative results and for biases related to selection of study participants and genotyping errors. Although these studies are clearly many steps removed from actual clinical use, and specific applications of GWA findings in prevention and treatment are actively being pursued, at present these studies mainly represent a valuable discovery tool for examining genomic function and clarifying pathophysiologic mechanisms. This article describes the design, interpretation, application, and limitations of GWA studies for clinicians and scientists for whom this evolving science may have great relevance.