Genome-wide association studies

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.

Automated summary

Genome-wide association studies (GWAS) involve testing hundreds of thousands of genetic variants to identify those associated with specific traits or diseases, with the number of associated variants expected to increase as sample sizes grow. The results of GWAS have various applications, including understanding the underlying biology of phenotypes, estimating heritability, predicting clinical risks, guiding drug development programs, and inferring causal relationships between risk factors and health outcomes.