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Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals

Author

Listed:
  • Davide Marnetto

    (University of Tartu)

  • Katri Pärna

    (University of Tartu
    University of Tartu
    University Medical Center Groningen)

  • Kristi Läll

    (University of Tartu)

  • Ludovica Molinaro

    (University of Tartu
    University of Tartu)

  • Francesco Montinaro

    (University of Tartu)

  • Toomas Haller

    (University of Tartu)

  • Mait Metspalu

    (University of Tartu)

  • Reedik Mägi

    (University of Tartu)

  • Krista Fischer

    (University of Tartu
    University of Tartu)

  • Luca Pagani

    (University of Tartu
    University of Padova)

Abstract

Polygenic Scores (PSs) describe the genetic component of an individual’s quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15464-w
    DOI: 10.1038/s41467-020-15464-w
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    Cited by:

    1. 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.
    2. Dmitrii Usoltsev & Nikita Kolosov & Oxana Rotar & Alexander Loboda & Maria Boyarinova & Ekaterina Moguchaya & Ekaterina Kolesova & Anastasia Erina & Kristina Tolkunova & Valeriia Rezapova & Ivan Molot, 2024. "Complex trait susceptibilities and population diversity in a sample of 4,145 Russians," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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