Genome-wide association studies
By E. Uffelmann, Q. Huang, N. S. Munung, J. de Vries, Y. Okada, Alicia R. Martin, H. Martin, Tuuli Lappalainen, Danielle Posthuma. Nature Reviews Methods Primers, 2021
1,011 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/548a6e38d774d58d56358e9f0c2f7739de6ae16b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
In Molecular Biology
Citations in the corpus
- Multimodal biomedical AI (2022, background): “Beyond oncology, there has been a remarkable increase in the last 15 years in the availability and sharing of genetic data, which enabled genome-wide association studies 12 and characterization of the genetic architecture of complex human conditions and traits 13 .”