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  • Primer
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Genome-wide association studies

Nature Reviews Methods Primers volume 1, Article number: 59 (2021) Cite this article

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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.

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Fig. 1: Overview of steps for conducting GWAS.
Fig. 2: Manhattan plot and quantile–quantile plot to visualize GWAS results.
Fig. 3: Illustration of functional follow-up of GWAS.
Fig. 4: Overview of the steps necessary for calculating PRSs.

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Acknowledgements

D.P. is supported by Netherlands Organization for Scientific Research (NWO) grant VICI 435-14-005, the NWO Gravitation project BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology (024.004.012) and European Research Council advanced grant ERC-2018-ADG 834057. N.S.M. is supported by National Institutes of Health (NIH) grant U24HL135600. J.d.V. is supported by NIH grant U54HG009790 and Wellcome Trust grant 219600/Z/19/Z. Y.O. is supported by Japan Society for the Promotion of Science (JSPS) KAKENHI grants 19H01021 and 20K21834 and Japan Agency for Medical Research and Development (AMED) grants JP20km0405211, JP20ek0109413, JP20ek0410075, JP20gm4010006 and JP20km0405217. T.L. is supported by NIH grants R01GM122924, R01HL142028, 1R01AG057422, 1UM1HG008901 and R01MH106842. H.C.M. is supported by a Wellcome Trust core grant to the Sanger Institute (098051).

Author information

Authors and Affiliations

  1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Emil Uffelmann & Danielle Posthuma

  2. Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Qin Qin Huang & Hilary C. Martin

  3. Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

    Nchangwi Syntia Munung & Jantina de Vries

  4. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan

    Yukinori Okada

  5. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan

    Yukinori Okada

  6. Analytic & Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Alicia R. Martin

  7. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Alicia R. Martin

  8. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Alicia R. Martin

  9. New York Genome Center, New York, NY, USA

    Tuuli Lappalainen

  10. Department of Systems Biology, Columbia University, New York, NY, USA

    Tuuli Lappalainen

  11. Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, Netherlands

    Danielle Posthuma

  12. Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden

    Tuuli Lappalainen

Authors
  1. Emil Uffelmann
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  2. Qin Qin Huang
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  3. Nchangwi Syntia Munung
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  4. Jantina de Vries
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  5. Yukinori Okada
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  6. Alicia R. Martin
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  7. Hilary C. Martin
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  8. Tuuli Lappalainen
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  9. Danielle Posthuma
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Contributions

Introduction (D.P. and E.U.); Experimentation (A.R.M. and H.C.M.); Results (D.P., Y.O. and T.L.); Applications (A.R.M.); Reproducibility and data deposition (D.P., E.U., J.d.V. and N.S.M.); Limitations and optimizations (D.P., E.U., J.d.V. and Q.Q.H.); Outlook (D.P., E.U., Q.Q.H., Y.O. and T.L.); Overview of the Primer (D.P.).

Corresponding author

Correspondence to Danielle Posthuma.

Ethics declarations

Competing interests

T.L. is an adviser to Goldfinch Bio, Variant Bio and GSK, and has equity in Variant Bio. The other authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Methods Primers thanks T. Edwards, J. Hostetler, D. Paltoo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Polygenic risk scores

(PRSs). Scores that provide an indication of an individual’s genetic liability to a trait or disease, calculated using an individual’s genome, weighted by effect sizes obtained from genome-wide association studies (GWAS).

Linkage disequilibrium

The non-independent association of two alleles in a population.

Collider bias

A bias that occurs when two variables (A and B) both influence a third variable (C), and the third variable is used to condition on. This can induce spurious correlations between variables A and B.

Hardy–Weinberg equilibrium

If the frequency of observed genotypes of a variant in a population can be derived from the observed allele frequencies, the genetic variant is said to be in Hardy–Weinberg equilibrium. A test for Hardy–Weinberg equilibrium is often used in quality control of genome-wide association studies (GWAS) to filter out variants with possible genotype calling errors.

Phasing

The process of estimating whether genotyped alleles derive from the maternal or paternal allele.

Population stratification

The presence of multiple genetically distinct subpopulations that differ in their mean phenotypic values. When not accounted for, this can lead to spurious genetic associations.

Random effect term

Random effects are effects that have so many levels (including more than the number of observations) that they are not individually estimable. By assigning these effects as random it is assumed that they are drawn from a population with a known variance and covariance structure. The effect for an individual can be predicted given the data and the distributional assumptions.

Logit link function

A function for converting a linear combination of covariate values into probabilities.

Bonferroni testing threshold

A correction for multiple testing that is typically applied by dividing the significance threshold by the number of independent tests that are carried out.

Winner’s curse

The phenomenon that the effect sizes of newly discovered alleles tend to be overestimated.

Odds ratio

An effect size estimate of a risk factor that quantifies the increased odds of having the disease per risk allele count in genome-wide association studies (GWAS) or one standard deviation increase of the polygenic risk score (PRS).

Summary statistics

The primary outcome of genome-wide association studies (GWAS), including a list of all tested single-nucleotide polymorphisms (SNPs) and effect sizes. The minimum required information is SNP IDs, SNP locations and genomic build, alleles, strand, effect size and standard error, P value, test statistic, minor allele frequency and sample size.

Rare variant burden testing

A statistical technique in which the number of rare alleles per gene is used to determine genetic association with a trait.

Transmission disequilibrium test

A family-based genetic association test in which alleles transmitted to affected offspring are contrasted with alleles not transmitted.

Gene flow

The transfer of genetic material between populations.

Genetic bottlenecks

Reductions in effective population size, for example, due to a migration followed by geographical isolation, or due to cultural endogamy, which leads to a reduction in diversity.

Conditional association analysis

A genetic association analysis that includes fixed effects of genetic variants.

Prior probability distribution

A term used in Bayesian statistics to describe the probability distribution of an unknown quantity based on beliefs an investigator has about the model parameters.

Posterior probability distribution

A term used in Bayesian statistics to describe the probability distribution of an unknown quantity based on observed data.

Expression quantitative trait loci

(eQTLs). Dosage effects of genetic variants on gene expression profiles, including expression levels and mRNA splicing patterns.

Pseudo-R 2

A statistical measure that indicates how well a model fits the data for binary traits and that can be used to compare models.

Liability scale

The assumed underlying normal distribution of dichotomous traits.

Net reclassification index

A metric that measures how much a new model improves in terms of reclassification. It is calculated as the proportion of individuals who are correctly reclassified minus the proportion of individuals who are incorrectly reclassified.

Identity by descent

The property of two identical segments of DNA having been inherited from a common ancestor without recombination.

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Uffelmann, E., Huang, Q.Q., Munung, N.S. et al. Genome-wide association studies. Nat Rev Methods Primers 1, 59 (2021). https://doi.org/10.1038/s43586-021-00056-9

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