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Instability of high polygenic risk classification and mitigation by integrative scoring

Author

Listed:
  • Anika Misra

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Buu Truong

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Sarah M. Urbut

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Harvard Medical School)

  • Yang Sui

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Harvard Medical School)

  • Akl C. Fahed

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Harvard Medical School)

  • Jordan W. Smoller

    (Massachusetts General Hospital
    Harvard Medical School
    Massachusetts General Hospital)

  • Aniruddh P. Patel

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Harvard Medical School)

  • Pradeep Natarajan

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Harvard Medical School)

Abstract

Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and commercial laboratories are increasingly deploying PRS reports to patients, but it is unknown how the classification of high polygenic risk changes across individual PRS. Here, we assess the association and classification performance of cataloged PRS for three complex traits. We chronologically order all trait-related publications (Pubn) and identify the single PRS Best(Pubn) for each Pubn that has the strongest association with the target outcome. While each Best(Pubn) demonstrates generally consistent population-level strengths of associations, the classification of individuals in the top 10% of each Best(Pubn) distribution varies widely. Using the PRSmix framework, which integrates information across several PRS to improve prediction, we generate corresponding ChronoAdd(Pubn) scores for each Pubn that combine all polygenic scores from all publications up to and including Pubn. When compared with Best(Pubn), ChronoAdd(Pubn) scores demonstrate more consistent high-risk classification amongst themselves. This integrative scoring approach provides stable and reliable classification of high-risk individuals and is an adaptable framework into which new scores can be incorporated as they are introduced, integrating easily with current PRS implementation strategies.

Suggested Citation

  • Anika Misra & Buu Truong & Sarah M. Urbut & Yang Sui & Akl C. Fahed & Jordan W. Smoller & Aniruddh P. Patel & Pradeep Natarajan, 2025. "Instability of high polygenic risk classification and mitigation by integrative scoring," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56945-0
    DOI: 10.1038/s41467-025-56945-0
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    References listed on IDEAS

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    1. Alexander G. Bick & Joshua S. Weinstock & Satish K. Nandakumar & Charles P. Fulco & Erik L. Bao & Seyedeh M. Zekavat & Mindy D. Szeto & Xiaotian Liao & Matthew J. Leventhal & Joseph Nasser & Kyle Chan, 2020. "Inherited causes of clonal haematopoiesis in 97,691 whole genomes," Nature, Nature, vol. 586(7831), pages 763-768, October.
    2. Loïc Yengo & Sailaja Vedantam & Eirini Marouli & Julia Sidorenko & Eric Bartell & Saori Sakaue & Marielisa Graff & Anders U. Eliasen & Yunxuan Jiang & Sridharan Raghavan & Jenkai Miao & Joshua D. Aria, 2022. "A saturated map of common genetic variants associated with human height," Nature, Nature, vol. 610(7933), pages 704-712, October.
    3. Robert M. Maier & Zhihong Zhu & Sang Hong Lee & Maciej Trzaskowski & Douglas M. Ruderfer & Eli A. Stahl & Stephan Ripke & Naomi R. Wray & Jian Yang & Peter M. Visscher & Matthew R. Robinson, 2018. "Improving genetic prediction by leveraging genetic correlations among human diseases and traits," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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