Objective: This study aims to provide a comprehensive assessment of PRS performance across ancestries and highlight the limitations of a “one-size-fits-all” approach to genetic risk prediction.

Background: 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.

Method: 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.

Results: Model 2 performed comparably across ancestries to implementations of Model 1 generated using European summary statistics while outperforming Model 1 generated from non-European summary statistics. This holds true even when Model 1 is applied to participants of the same ancestry that was used for its ancestry-specific summary statistics. We determined the influence of each variant on the performance of each instance of Model 1, identifying both ancestry-specific variants as well as those which had strong predictive power across all ancestries.

Conclusion: This analysis highlights the heterogeneity of PD risk factors and underscores the bias introduced by predominantly European-derived genetic data.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/insights-into-ancestral-diversity-in-parkinsons-disease-risk-a-comparative-assessment-of-polygenic-risk-scores/