Methodological opportunities in genomic data analysis to advance health equity.

Journal: Nature reviews. Genetics
Published Date: May 15, 2025

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.

Authors

  • Brieuc Lehmann
    Department of Statistical Science, University College London, London, UK. b.lehmann@ucl.ac.uk.
  • Leandra Bräuninger
    Department of Statistical Science, University College London, London, UK.
  • Yoonsu Cho
    Genomics England, London, UK.
  • Fabian Falck
    The Alan Turing Institute, London, UK.
  • Smera Jayadeva
    The Alan Turing Institute, London, UK.
  • Michael Katell
    The Alan Turing Institute, London, UK.
  • Thuy Nguyen
  • Antonella Perini
    The Alan Turing Institute, London, UK.
  • Sam Tallman
    Genomics England, London, UK.
  • Maxine Mackintosh
    Genomics England, London, UK.
  • Matt Silver
    Genomics England, London, UK.
  • Karoline Kuchenbäcker
    Genomics England, London, UK.
  • David Leslie
    Alan Turing Institute, London, UK dleslie@turing.ac.uk.
  • Nilanjan Chatterjee
    Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
  • Chris Holmes
    Department of Statistics, University of Oxford, Oxford, UK.

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