Insights into ancestral diversity in Parkinson’s disease risk: a comparative assessment of polygenic risk scores
Abstrak
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.
Topik & Kata Kunci
Penulis (31)
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
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1038/s41531-025-00967-4
- Akses
- Open Access ✓