The DOI matches its Crossref record; 293 of the 293 references it cites were found in a registry or an index.
Genome-wide association studies
Open accessAbstract
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.
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“Misinterpretation or oversights can lead to inaccurate biological conclusions, highlighting the importance of precision in WGA algorithms [80].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [80]
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“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 .”
From Multimodal biomedical AI (2022) · cites this paper as [12]
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Cited by
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Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
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Metadata
- Authors
- Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D.
- Affiliations
- Amsterdam Neuroscience (NL)
- Wellcome Sanger Institute (GB)
- University of Cape Town (ZA)
- Immunology Frontier Research Center, The University of Osaka (JP)
- Broad Institute (US)
- Science for Life Laboratory (SE)
- ORCID
- Venue
- Nature Reviews Methods Primers
- Published
- 2021-08-26
- Volume
- 1
- Issue
- 1
- ISSN
- 2662-8449 (ISSN-L)
- OpenAlex
- W3195285310
- Cited by
- 1,849
- References
- 288
- Citations by year
- 2026: 292 · 2025: 512 · 2024: 546 · 2023: 304 · 2022: 163 · 2021: 22
- FWCI
- 38.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Reviews Methods Primers (publishedVersion, public-domain)
- VU Research Portal (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Japan Agency for Medical Research and Development (JP20ek0109413, JP20km0405217, JP20gm4010006, JP20ek0410075, JP20gm4010006, JP20ek0410075, JP20km0405211)
- Wellcome Trust (219600/Z/19/Z, 098051)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VICI 435-14-005, 024.004.012, 435-14-005)
- National Institutes of Health
- Japan Society for the Promotion of Science (19H01021 and 20K21834, 834057, 20K21834, 19H01021)
- National Heart, Lung, and Blood Institute (R01HL142028, U24HL135600)
- National Institute of Mental Health (R01MH106842)
- National Human Genome Research Institute (UM1HG008901, U54HG009790)
- National Institute of General Medical Sciences (R01GM122924)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genome-wide association study, genetic variants, disease risk, heritability, genetic correlation, clinical risk prediction, drug development, causal inference, data sharing, research ethics
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