Genome-wide association studies

TLDR

Genome‑wide association studies scan hundreds of thousands of genetic variants across many genomes to identify those statistically linked to specific traits or diseases, yielding numerous robust associations that inform biology, heritability, genetic correlations, risk prediction, drug development, and causal inference. This Primer introduces GWAS by explaining their statistical foundations, describing how they are conducted, reviewing state‑of‑the‑art approaches, and outlining limitations, challenges, and current and future applications. The Primer details best practices for conducting GWAS, techniques for deriving functional inferences, data‑sharing guidelines, and ethical considerations for GWAS populations and data. Uffelmann et al.

Abstract

Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. This methodology has generated a myriad of robust associations for a range of traits and diseases, and the number of associated variants is expected to grow steadily as GWAS sample sizes increase. GWAS results have a range of applications, such as gaining insight into a phenotype's underlying biology, estimating its heritability, calculating genetic correlations, making clinical risk predictions, informing drug development programmes and inferring potential causal relationships between risk factors and health outcomes. In this Primer, we provide the reader with an introduction to GWAS, explaining their statistical basis and how they are conducted, describe state-of-the art approaches and discuss limitations and challenges, concluding with an overview of the current and future applications for GWAS results. Uffelmann et al. describe the key considerations and best practices for conducting genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture. The Primer also provides information on the best practices for data sharing and discusses important ethical considerations when considering GWAS populations and data.