1. Genetics and Genomics

Variable prediction accuracy of polygenic scores within an ancestry group

  1. Hakhamanesh Mostafavi  Is a corresponding author
  2. Arbel Harpak  Is a corresponding author
  3. Ipsita Agarwal
  4. Dalton Conley
  5. Jonathan K Pritchard
  6. Molly Przeworski  Is a corresponding author
  1. Columbia University, United States
  2. Princeton University, United States
  3. Stanford University, United States
https://doi.org/10.7554/eLife.48376
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  1. Hakhamanesh Mostafavi
  2. Arbel Harpak
  3. Ipsita Agarwal
  4. Dalton Conley
  5. Jonathan K Pritchard
  6. Molly Przeworski
(2020)
Variable prediction accuracy of polygenic scores within an ancestry group
eLife 9:e48376.
https://doi.org/10.7554/eLife.48376
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Abstract

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Data availability

The GWAS summary statistics generated in this study have been uploaded to Dryad.

The following data sets were generated

Article and author information

Author details

  1. Hakhamanesh Mostafavi

    Department of Biological Sciences, Columbia University, New York, United States
    For correspondence
    hsm2137@columbia.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1060-2844

  2. Arbel Harpak

    Department of Biological Sciences, Columbia University, New York, United States
    For correspondence
    ah3586@columbia.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3655-748X

  3. Ipsita Agarwal

    Department of Biological Sciences, Columbia University, New York, United States
    Competing interests
    No competing interests declared.

  4. Dalton Conley

    Department of Sociology, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.

  5. Jonathan K Pritchard

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8828-5236

  6. Molly Przeworski

    Department of Systems Biology, Columbia University, New York, United States
    For correspondence
    mp3284@columbia.edu
    Competing interests
    Molly Przeworski, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5369-9009

Funding

National Institute of General Medical Sciences (GM121372)

  • Molly Przeworski

National Human Genome Research Institute (HG008140)

  • Jonathan K Pritchard

Robert Wood Johnson Foundation (84337817)

  • Dalton Conley

Simons Foundation (633313)

  • Arbel Harpak

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: This study has been conducted using the UK Biobank resource under application Number 11138, as approved by Columbia University Institutional Review Board, protocol AAAS2914.

Copyright

© 2020, Mostafavi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Hakhamanesh Mostafavi
  2. Arbel Harpak
  3. Ipsita Agarwal
  4. Dalton Conley
  5. Jonathan K Pritchard
  6. Molly Przeworski
(2020)
Variable prediction accuracy of polygenic scores within an ancestry group
eLife 9:e48376.
https://doi.org/10.7554/eLife.48376

Share this article

https://doi.org/10.7554/eLife.48376

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