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Genome-wide association studies

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Uffelmann, Emil, Huang, Qin Qin, Munung, Nchangwi Syntia, de Vries, Jantina, Okada, Yukinori, Martin, Alicia R., Martin, Hilary C., Lappalainen, Tuuli, Posthuma, Danielle·2021·Nature Reviews Methods Primers

DOI 10.1038/s43586-021-00056-9

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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.

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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: Uffelmann, Emil
Huang, Qin Qin
Munung, Nchangwi Syntia
Okada, Yukinori
Martin, Alicia R.
Martin, Hilary C.
Lappalainen, Tuuli
Posthuma, Danielle
Venue: Nature Reviews Methods Primers
Publisher: Springer Science and Business Media LLC
Published: 2021-08-26
Volume: 1
Issue: 1
ISSN: 2662-8449 (ISSN-L)
DOI: 10.1038/s43586-021-00056-9
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: public-domain
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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