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Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI

Author

Listed:
  • Quan Sun

    (University of North Carolina at Chapel Hill)

  • Bryce T. Rowland

    (University of North Carolina at Chapel Hill)

  • Jiawen Chen

    (University of North Carolina at Chapel Hill)

  • Anna V. Mikhaylova

    (University of Washington)

  • Christy Avery

    (University of North Carolina at Chapel Hill)

  • Ulrike Peters

    (Fred Hutchinson Cancer Center)

  • Jessica Lundin

    (Fred Hutchinson Cancer Center)

  • Tara Matise

    (Rutgers University)

  • Steve Buyske

    (Rutgers University)

  • Ran Tao

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

  • Rasika A. Mathias

    (Johns Hopkins University)

  • Alexander P. Reiner

    (University of Washington)

  • Paul L. Auer

    (and Cancer Center, Medical College of Wisconsin)

  • Nancy J. Cox

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

  • Charles Kooperberg

    (Fred Hutchinson Cancer Center)

  • Timothy A. Thornton

    (University of Washington)

  • Laura M. Raffield

    (University of North Carolina at Chapel Hill)

  • Yun Li

    (University of North Carolina at Chapel Hill
    University of North Carolina at Chapel Hill)

Abstract

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Suggested Citation

  • Quan Sun & Bryce T. Rowland & Jiawen Chen & Anna V. Mikhaylova & Christy Avery & Ulrike Peters & Jessica Lundin & Tara Matise & Steve Buyske & Ran Tao & Rasika A. Mathias & Alexander P. Reiner & Paul , 2024. "Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45135-z
    DOI: 10.1038/s41467-024-45135-z
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    References listed on IDEAS

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