Abstract
The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity.
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Acknowledgements
The authors gratefully acknowledge the speakers and attendees of the joint Data Science for Health Equity workshops on ‘Challenges to statistical approaches for fairness in genomics’ and ‘Challenges to statistical approaches for health equity’ held in January 2022. They also thank C. Harbron, G. McVean, S. Walker, D. Deen, A. Shalek and H. Martin for comments on an earlier version of this manuscript. F.F. acknowledges the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme (218529/Z/19/Z), and the Enrichment Scheme of The Alan Turing Institute under an Engineering & Physical Sciences Research Council grant (EP/N510129/1). K.K. was supported by the European Research Council under the European Union Horizon 2020 research and innovation programme (948561). N.C. was supported by US National Institutes of Health grants (R01HG013137, R01HG010480, U01HG011719).
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B.L., L.B. and F.F. researched the literature. B.L. and L.B. wrote the article. All authors contributed substantially to discussions of the content, and reviewed and/or edited the manuscript.
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This manuscript was informed by a project commissioned by the Diverse Data (DD) initiative at Genomics England (GEL) in December 2022 to explore the use of statistical and machine learning methods to improve fairness and equity in genomics. K.K. is the Scientific Lead for DD. S.T., T.N. and Y.C. are Genomic Data Scientists at GEL. M.S. was the Lead Genomic Data Scientist for DD, and M.M. was the Programme Lead for DD. B.L. and L.B. were paid consultants to GEL for the project. M.M. is Director of One HealthTech, which provides the secretariat for the Data Science for Health Equity community, which B.L. is also the co-founder of. B.L. and L.B. have acted as consultants for Google DeepMind in relation to other research in this field; however, Google DeepMind was not involved in this project or this publication. F.F. is an employee and shareholder of Microsoft Corporation.
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Lehmann, B., Bräuninger, L., Cho, Y. et al. Methodological opportunities in genomic data analysis to advance health equity. Nat Rev Genet 26, 635–649 (2025). https://doi.org/10.1038/s41576-025-00839-w
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DOI: https://doi.org/10.1038/s41576-025-00839-w
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