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

Davide Marnetto, Katri Parna, Kristi Lall, Ludovica Molinaro, Francesco Montinaro, Toomas Haller, Mait Metspalu, Reedik Magi, Krista Fischer, Luca Pagani

    Research output: Contribution to journalArticleAcademicpeer-review

    18 Citations (Scopus)
    30 Downloads (Pure)

    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. Polygenic scores are believed to hold future promise for trait prediction and personalized medicine, but are sensitive to demographic history. Here, Marnetto et al. develop partial polygenic scores supplemented with local ancestry deconvolution which improves prediction accuracy into recently admixed European populations.

    Original languageEnglish
    Article number1628
    Number of pages9
    JournalNature Communications
    Volume11
    Issue number1
    DOIs
    Publication statusPublished - 1-Dec-2020

    Keywords

    • RISK PREDICTION
    • GENETIC RISK
    • ASSOCIATION

    Cite this