Genome-wide association studies

Uffelmann, Emil; Huang, Qin Qin; Munung, Nchangwi Syntia; de Vries, Jantina; Okada, Yukinori; Martin, Alicia R.; Martin, Hilary C.; Lappalainen, Tuuli; Posthuma, Danielle

doi:10.1038/s43586-021-00056-9

Cited by 1,849Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

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 +1 more·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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Misinterpretation or oversights in WGA can lead to inaccurate biological conclusions.
“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]
The Experimentation section (page unavailable) says biases and errors can enter a GWAS at each step and calls for careful planning and standardized quality control. The Abstract (page unavailable) also warns that limitations can hamper conclusions about the biological meaning of GWAS results. These passages support the general warning, but the paper discusses GWAS, not precision in WGA algorithms. (LLM, conf=0.83)
Experimental — accuracy not yet measured
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 and characterization of the genetic architecture of complex human conditions and traits.
“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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Heath, A. C., Berg, K., Eaves, L. J., Solaas, M. H., Corey, L. A., Sundet, J. M., Magnus, P. M., & Nance, W. E. (1985) · Nature · doi:10.1038/314734a0 · Cited by 345 · see citing works
Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits
Shi, H., Mancuso, N., Spendlove, S. J., & Paşaniuc, B. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.022 · Cited by 345 · see citing works
Minimal phenotyping yields genome-wide association signals of low specificity for major depression
MDD Working Group of the Psychiatric Genomics Consortium, Cai, N., Revez, J. A., Adams, M. J., Andlauer, T. F. M., Breen, G., Byrne, E. M., Clarke, T.-K., Forstner, A. J., Grabe, H. J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0594-5 · Cited by 344 · see citing works
Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome
Tak, Y. G., & Farnham, P. (2015) · Epigenetics & Chromatin · doi:10.1186/s13072-015-0050-4 · Cited by 342 · see citing works
The impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome biology · doi:10.1186/s13059-017-1212-4 · Cited by 342 · see citing works
The GWAS Diversity Monitor tracks diversity by disease in real time
Mills, M. C., & Rahal, C. (2020) · Nature Genetics · doi:10.1038/s41588-020-0580-y · Cited by 342 · see citing works
Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation
Christophersen, I. E., Rienstra, M., Roselli, C., Yin, X., Geelhoed, B., Barnard, J., Lin, H., Arking, D. E., Smith, A. V., Albert, C. M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3843 · Cited by 339 · see citing works
Reduced signal for polygenic adaptation of height in UK Biobank
Berg, J. J., Harpak, A., Sinnott-Armstrong, N., Joergensen, A. M., Mostafavi, H., Field, Y., Boyle, E. A., Zhang, X., Racimo, F., Pritchard, J. K., et al. (2019) · eLife · doi:10.7554/elife.39725 · Cited by 339 · see citing works
Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression
Craig, J. E., Han, X., Qassim, A., Hassall, M., Bailey, J. N. C., Kinzy, T. G., Khawaja, A. P., An, J., Marshall, H. N., Gharahkhani, P., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0556-y · Cited by 339 · see citing works
Evidence of widespread selection on standing variation in Europe at height-associated SNPs
Turchin, M. C., Chiang, C. W., Palmer, C. D., Sankararaman, S., Reich, D., Genetic Investigation of ANthropometric Traits (GIANT) Consortium, & Hirschhorn, J. N. (2012) · Nature Genetics · doi:10.1038/ng.2368 · Cited by 329 · see citing works
Multivariate genome-wide analyses of the well-being spectrum
BIOS consortium, Baselmans, B. M. L., Social Science Genetic Association Consortium, Jansen, R., Ip, H. F., van Dongen, J., Abdellaoui, A., van de Weijer, M. P., Bao, Y., Smart, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0320-8 · Cited by 320 · see citing works
Rights, Interests and Expectations: Indigenous Perspectives on Unrestricted Access to Genomic Data
Hudson, M., Garrison, N. A., Sterling, R., Caron, N. R., Fox, K., Yracheta, J., Anderson, J., Wilcox, P., Arbour, L., Brown, A., et al. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0228-x · Cited by 320 · see citing works
RICOPILI: Rapid Imputation for COnsortias PIpeLIne
Lam, M., Awasthi, S., Watson, H. J., Goldstein, J., Panagiotaropoulou, G., Trubetskoy, V., Karlsson, R., Frei, O., Fan, C.-C., De Witte, W., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz633 · Cited by 315 · see citing works
Whole-genome sequencing of 128 camels across Asia reveals origin and migration of domestic Bactrian camels
Ming, L., Yuan, L., Yi, L., Ding, G., Hasi, S., Chen, G., Jambl, T., Hedayat-Evright, N., Batmunkh, M., Бадмаевна, Г. Х., et al. (2020) · Communications Biology · doi:10.1038/s42003-019-0734-6 · Cited by 315 · see citing works
Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis
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A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence
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Exploiting the GTEx resources to decipher the mechanisms at GWAS loci
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Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls
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Anticipate and Communicate: Ethical Management of Incidental and Secondary Findings in the Clinical, Research, and Direct-to-Consumer Contexts (December 2013 Report of the Presidential Commission for the Study of Bioethical Issues)
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Predicting Polygenic Risk of Psychiatric Disorders
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Diverse Genome-wide Association Studies Associate the IL12/IL23 Pathway with Crohn Disease
Wang, K., Zhang, H., Kugathasan, S., Annese, V., Bradfield, J. P., Russell, R. K., Sleiman, P. M. A., Imielinski, M., Glessner, J., Hou, C., et al. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.01.026 · Cited by 291 · see citing works
Comparing Within- and Between-Family Polygenic Score Prediction
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Measuring missing heritability: Inferring the contribution of common variants
Golan, D., Lander, E. S., & Rosset, S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419064111 · Cited by 288 · see citing works
Multiethnic polygenic risk scores improve risk prediction in diverse populations
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Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies
Service, S., DeYoung, J. A., Karayiorgou, M., Roos, J. L., Pretorious, H., Bedoya, G., Ospina, J., Ruiz‐Linares, A., Macedo, A., Palha, J. A., et al. (2006) · Nature Genetics · doi:10.1038/ng1770 · Cited by 273 · see citing works
Extreme Polygenicity of Complex Traits Is Explained by Negative Selection
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Item-level analyses reveal genetic heterogeneity in neuroticism
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Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
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Publication bias: what is it? How do we measure it? How do we avoid it?
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Genetic correlates of social stratification in Great Britain
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The variant call format provides efficient and robust storage of GWAS summary statistics
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Common genetic variants associated with cognitive performance identified using the proxy-phenotype method
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SumHer better estimates the SNP heritability of complex traits from summary statistics
Speed, D., & Balding, D. J. (2019) · Nature Genetics · doi:10.1038/s41588-018-0279-5 · Cited by 254 · see citing works
Genetic analyses identify widespread sex-differential participation bias
Pirastu, N., Cordioli, M., Nandakumar, P., Mignogna, G., Abdellaoui, A., Hollis, B., Kanai, M., Rajagopal, V. M., Parolo, P. D. B., Baya, N., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00846-7 · Cited by 251 · see citing works
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Genetic model testing and statistical power in population‐based association studies of quantitative traits
Lettre, G., Lange, C., & Hirschhorn, J. N. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20217 · Cited by 247 · see citing works
Case–control association mapping by proxy using family history of disease
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Solving the missing heritability problem
Young, A. I. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008222 · Cited by 235 · see citing works
The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability
Singh, T., Walters, J. T. R., Johnstone, M., Curtis, D., Suvisaari, J., Torniainen, M., Rees, E., Iyegbe, C., Blackwood, D., McIntosh, A. M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3903 · Cited by 231 · see citing works
Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model
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Population genetic differentiation of height and body mass index across Europe
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The single-cell eQTLGen consortium
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Instead of “playing the game” it is time to change the rules: Registered Reports at <em>AIMS Neuroscience</em> and beyond
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The Promises and Pitfalls of Genoeconomics
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Multimodal biomedical AI
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 · see citing works
Genome-wide analyses of attention deficit hyperactivity disorder identify 27 risk loci, refine the genetic architecture, and implicate several cognitive domains
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Large-scale plasma proteomics comparisons through genetics and disease associations
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Applications of multi‐omics analysis in human diseases
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The complex genetic architecture of Alzheimer's disease: novel insights and future directions
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Empowering biomedical discovery with AI agents
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Prospective study design and data analysis in UK Biobank
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New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications
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Physiological and pathological roles of lipogenesis
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Multi-omic approaches for host-microbiome data integration
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Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores
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Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals
Sinha, D., Maurya, A. K., Abdi, G., Majeed, M., Agarwal, R., Mukherjee, R., Ganguly, S., Aziz, R., Bhatia, M., Majgaonkar, A., et al. (2023) · Genes · doi:10.3390/genes14071484 · Cited by 180 · see citing works
Genetics and sports performance: the present and future in the identification of talent for sports based on DNA testing
Varillas‐Delgado, D., Coso, J. D., Gutiérrez‐Hellín, J., Aguilar‐Navarro, M., Muñoz, A., Maestro, A., & Morencos, E. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-04945-z · Cited by 146 · see citing works
Leveraging base-pair mammalian constraint to understand genetic variation and human disease
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Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology
Bianconi, I., Aschbacher, R., & Pagani, E. (2023) · Antibiotics · doi:10.3390/antibiotics12111580 · Cited by 130 · see citing works
Common genetic factors among autoimmune diseases
Harroud, A., & Hafler, D. A. (2023) · Science · doi:10.1126/science.adg2992 · Cited by 110 · see citing works
Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence—A Critical Review
Sun, L., Lai, M., Ghouri, F., Nawaz, M. A., Ali, F., Baloch, F. S., Nadeem, M. A., Aasım, M., & Shahid, M. Q. (2024) · Plants · doi:10.3390/plants13192676 · Cited by 129 · see citing works
Advancing pharmaceutical research: A comprehensive review of cutting-edge tools and technologies
Chen, J., Lin, A., & Luo, P. (2024) · Current Pharmaceutical Analysis · doi:10.1016/j.cpan.2024.11.001 · Cited by 112 · see citing works
Microglial efferocytosis: Diving into the Alzheimer’s disease gene pool
Romero-Molina, C., Garretti, F., Andrews, S. J., Marcora, E., & Goate, A. M. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.015 · Cited by 111 · see citing works
Plant genome resequencing and population genomics: Current status and future prospects
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Convergence of coronary artery disease genes onto endothelial cell programs
Schnitzler, G. R., Kang, H. Y., Fang, S., Angom, R. S., Lee-Kim, V. S., Ma, X. R., Zhou, R., Zeng, T., Guo, K., Taylor, M. S., et al. (2024) · Nature · doi:10.1038/s41586-024-07022-x · Cited by 105 · see citing works
Cardiovascular diseases and depression: A meta-analysis and Mendelian randomization analysis
Zeng, J., Qiu, Y., Yang, C., Fan, X., Zhou, X., Zhang, C., Zhu, S., Long, Y., Hashimoto, K., Chang, L., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03003-2 · Cited by 108 · see citing works
Immuno-Modulatory Effects of Intervertebral Disc Cells
Bermudez‐Lekerika, P., Crump, K. B., Tseranidou, S., Nüesch, A., Kanelis, E., Alminnawi, A., Baumgartner, L., Muñoz‐Moya, E., Compte, R., Gualdi, F., et al. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.924692 · Cited by 103 · see citing works
Identification of non-coding silencer elements and their regulation of gene expression
Pang, B., van Weerd, J. H., Hamoen, F. L., & Snyder, M. P. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00549-9 · Cited by 97 · see citing works
Computational drug repurposing: approaches, evaluation of in silico resources and case studies
Tanoli, Z., Fernández‐Torras, A., Özcan, U. O., Kushnir, A., Nader, K. M., Gadiya, Y., Fiorenza, L., Ianevski, A., Vähä-Koskela, M. J. V., Miihkinen, M., et al. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01164-x · Cited by 99 · see citing works
Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize
Sahito, J. H., Zhang, H., Gishkori, Z. G. N., Ma, C., Wang, Z., Ding, D., Zhang, X., & Tang, J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031918 · Cited by 98 · see citing works
TEC Monitoring by Using Satellite Positioning Systems
Jakowski, N. (1996) · elib (German Aerospace Center) · Cited by 97 · see citing works
Molecular quantitative trait loci
Aguet, F., Alasoo, K., Li, Y. I., Battle, A., Im, H. K., Montgomery, S. B., & Lappalainen, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00188-6 · Cited by 98 · see citing works
Self-citation
Interpreting omics data with pathway enrichment analysis
Zhao, K., & Rhee, S. Y. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.01.003 · Cited by 95 · see citing works
Graph Neural Networks in Modern AI-Aided Drug Discovery
Zhang, O., Lin, H., Zhang, X., Wang, X., Wu, Z., Ye, Q., Zhao, W., Wang, J., Ying, K., Kang, Y., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00461 · Cited by 103 · see citing works
Deciphering the impact of genomic variation on function
Writing group (ordered by contribution), Engreitz, J., Lawson, H. A., Singh, H., Starita, L. M., Hon, G. C., Carter, H. K., Sahni, N., Reddy, T. E., Lin, X., et al. (2024) · Nature · doi:10.1038/s41586-024-07510-0 · Cited by 91 · see citing works
Uncovering the genetic basis of Parkinson's disease globally: from discoveries to the clinic
Lim, S., Tan, A. H., Ahmad‐Annuar, A., Okubadejo, N. U., Lohmann, K., Morris, H. R., Toh, T. S., Tay, Y. W., Lange, L. M., Bandrés‐Ciga, S., et al. (2024) · The Lancet Neurology · doi:10.1016/s1474-4422(24)00378-8 · Cited by 90 · see citing works
Insights into Dyslexia Genetics Research from the Last Two Decades
Erbeli, F., Rice, M., & Paracchini, S. (2021) · Brain Sciences · doi:10.3390/brainsci12010027 · Cited by 88 · see citing works
Decoding the genetic and epigenetic basis of asthma
Stikker, B., Hendriks, R. W., & Stadhouders, R. (2023) · Allergy · doi:10.1111/all.15666 · Cited by 95 · see citing works
Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders
Singh, S., Sarma, D. K., Verma, V., Nagpal, R., & Kumar, M. (2023) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2023.09.064 · Cited by 94 · see citing works
Genome-wide association studies of cardiovascular disease
Walsh, R., Jurgens, S. J., Erdmann, J., & Bezzina, C. R. (2023) · Physiological Reviews · doi:10.1152/physrev.00024.2022 · Cited by 92 · see citing works
Microbiome epidemiology and association studies in human health
VanEvery, H., Franzosa, E. A., Nguyen, L. H., & Huttenhower, C. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00529-x · Cited by 85 · see citing works
The pathogenesis of gout: molecular insights from genetic, epigenomic and transcriptomic studies
Leask, M., Crișan, T. O., Ji, A., Matsuo, H., Köttgen, A., & Merriman, T. R. (2024) · Nature Reviews Rheumatology · doi:10.1038/s41584-024-01137-1 · Cited by 86 · see citing works
Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits
Zhao, S., Crouse, W. L., Qian, S., Luo, K., Stephens, M., & He, X. (2024) · Nature Genetics · doi:10.1038/s41588-023-01648-9 · Cited by 79 · see citing works
Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements
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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 · 2020: 2 · 2019: 4 · 2014: 1 · 2013: 1
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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