Insights into ancestral diversity in Parkinson’s disease risk: a comparative assessment of polygenic risk scores

Insights into ancestral diversity in Parkinson’s disease risk: a comparative assessment of polygenic risk scores

Abstract Risk prediction models play a crucial role in advancing healthcare by enabling early detection and supporting personalized medicine. Nonetheless, polygenic risk scores (PRS) for Parkinson’s disease (PD) have not been extensively studied across diverse populations, contributing to health dis...

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Main Authors: Paula Saffie-Awad, Spencer M. Grant, Mary B. Makarious, Inas Elsayed, Arinola O. Sanyaolu, Peter Wild Crea, Artur F. Schumacher Schuh, Kristin S. Levine, Dan Vitale, Mathew J. Koretsky, Jeffrey Kim, Thiago Peixoto Leal, María Teresa Periñán, Sumit Dey, Alastair J. Noyce, Armando Reyes-Palomares, Noela Rodriguez-Losada, Jia Nee Foo, Wael Mohamed, Karl Heilbron, Lucy Norcliffe-Kaufmann, the 23andMe Research Team, Mie Rizig, Njideka Okubadejo, Mike A. Nalls, Cornelis Blauwendraat, Andrew Singleton, Hampton Leonard, Global Parkinson’s Genetics Program (GP2), Ignacio F. Mata, Sara Bandres-Ciga
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:npj Parkinson's Disease
Online Access:https://doi.org/10.1038/s41531-025-00967-4
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Summary:Abstract Risk prediction models play a crucial role in advancing healthcare by enabling early detection and supporting personalized medicine. Nonetheless, polygenic risk scores (PRS) for Parkinson’s disease (PD) have not been extensively studied across diverse populations, contributing to health disparities. In this study, we constructed 105 PRS using individual-level data from seven ancestries and compared two different models. Model 1 was based on the cumulative effect of 90 known European PD risk variants, weighted by summary statistics from four independent ancestries (European, East Asian, Latino/Admixed American, and African/Admixed). Model 2 leveraged multi-ancestry summary statistics using a p-value thresholding approach to improve prediction across diverse populations. Our findings provide a comprehensive assessment of PRS performance across ancestries and highlight the limitations of a “one-size-fits-all” approach to genetic risk prediction. We observed variability in predictive performance between models, underscoring the need for larger sample sizes and ancestry-specific approaches to enhance accuracy. These results establish a foundation for future research aimed at improving generalizability in genetic risk prediction for PD.
ISSN:2373-8057

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