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

Davide Marnetto (), Katri Pärna, Kristi Läll, Ludovica Molinaro, Francesco Montinaro, Toomas Haller, Mait Metspalu, Reedik Mägi, Krista Fischer and Luca Pagani ()
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Davide Marnetto: University of Tartu
Katri Pärna: University of Tartu
Kristi Läll: University of Tartu
Ludovica Molinaro: 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
Luca Pagani: University of Tartu

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-15464-w

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