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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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    1. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    2. Saredo Said & Raha Pazoki & Ville Karhunen & Urmo Võsa & Symen Ligthart & Barbara Bodinier & Fotios Koskeridis & Paul Welsh & Behrooz Z. Alizadeh & Daniel I. Chasman & Naveed Sattar & Marc Chadeau-Hya, 2022. "Genetic analysis of over half a million people characterises C-reactive protein loci," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Konrad J. Karczewski & Laurent C. Francioli & Grace Tiao & Beryl B. Cummings & Jessica Alföldi & Qingbo Wang & Ryan L. Collins & Kristen M. Laricchia & Andrea Ganna & Daniel P. Birnbaum & Laura D. Gau, 2020. "The mutational constraint spectrum quantified from variation in 141,456 humans," Nature, Nature, vol. 581(7809), pages 434-443, May.
    4. Genevieve L. Wojcik & Mariaelisa Graff & Katherine K. Nishimura & Ran Tao & Jeffrey Haessler & Christopher R. Gignoux & Heather M. Highland & Yesha M. Patel & Elena P. Sorokin & Christy L. Avery & Gil, 2019. "Genetic analyses of diverse populations improves discovery for complex traits," Nature, Nature, vol. 570(7762), pages 514-518, June.
    5. Davide Marnetto & Katri Pärna & Kristi Läll & Ludovica Molinaro & Francesco Montinaro & Toomas Haller & Mait Metspalu & Reedik Mägi & Krista Fischer & Luca Pagani, 2020. "Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    6. Daniel Taliun & Daniel N. Harris & Michael D. Kessler & Jedidiah Carlson & Zachary A. Szpiech & Raul Torres & Sarah A. Gagliano Taliun & André Corvelo & Stephanie M. Gogarten & Hyun Min Kang & Achille, 2021. "Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program," Nature, Nature, vol. 590(7845), pages 290-299, February.
    7. 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.
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