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Analysis of polygenic risk score usage and performance in diverse human populations

Author

Listed:
  • L. Duncan

    (Stanford University)

  • H. Shen

    (Stanford University)

  • B. Gelaye

    (Harvard T.H. Chan School of Public Health)

  • J. Meijsen

    (Stanford University)

  • K. Ressler

    (McLean Hospital)

  • M. Feldman

    (Stanford University)

  • R. Peterson

    (Virginia Commonwealth University)

  • B. Domingue

    (Stanford University)

Abstract

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

Suggested Citation

  • L. Duncan & H. Shen & B. Gelaye & J. Meijsen & K. Ressler & M. Feldman & R. Peterson & B. Domingue, 2019. "Analysis of polygenic risk score usage and performance in diverse human populations," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11112-0
    DOI: 10.1038/s41467-019-11112-0
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    14. Jiacheng Miao & Hanmin Guo & Gefei Song & Zijie Zhao & Lin Hou & Qiongshi Lu, 2023. "Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    15. Ricky Lali & Michael Chong & Arghavan Omidi & Pedrum Mohammadi-Shemirani & Ann Le & Edward Cui & Guillaume Paré, 2021. "Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
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    18. H. Serhat Tetikol & Deniz Turgut & Kubra Narci & Gungor Budak & Ozem Kalay & Elif Arslan & Sinem Demirkaya-Budak & Alexey Dolgoborodov & Duygu Kabakci-Zorlu & Vladimir Semenyuk & Amit Jain & Brandi N., 2022. "Pan-African genome demonstrates how population-specific genome graphs improve high-throughput sequencing data analysis," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
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