Polygenic Risk Scores Advance Parkinson’s Disease Prediction in South Africa
Parkinson’s disease (PD) is a complex neurodegenerative disorder influenced by both genetic predisposition and environmental factors. Recent research has focused on utilizing polygenic risk scores (PRS) to improve risk prediction, particularly in populations beyond those of European ancestry, where most genetic studies have been conducted. A new study, conducted in a South African cohort, provides the first evaluation of PRS performance for PD in a highly admixed population, highlighting the importance of inclusivity in genomic research.
Understanding Polygenic Risk Scores
Polygenic risk scores are calculated by summing the effects of many genetic variants across the genome, each associated with a small increase or decrease in disease risk. PRSice-2 is a tool used to conduct this analysis, leveraging summary statistics from existing genome-wide association studies (GWAS). The goal is to identify individuals at higher risk of developing a disease, allowing for earlier intervention and potentially more effective preventative strategies.
The South African Study: Methodology and Findings
Researchers analyzed genotyping data from 661 South African individuals with Parkinson’s disease and 737 controls. They utilized summary statistics from two previously published PD association studies as base datasets for PRS calculation. The dataset was divided into training (70%) and validation (30%) cohorts to assess the predictive accuracy of the scores. Various parameters, including clumping window sizes, linkage disequilibrium thresholds and p-value thresholds, were tested to optimize risk prediction.
The study found modest predictive performance for the PRS, with an area under the curve (AUC) ranging from 0.5847 to 0.6183. Age at recruitment emerged as the strongest individual predictor of PD risk, although sex contributed the least. These findings underscore the complex interplay between genetics and environmental factors in PD development.
The Importance of Ancestry in Genetic Risk Prediction
A key takeaway from this research is the impact of ancestry on PRS performance. The South African population is highly admixed, meaning it has a diverse genetic background. The study highlights how ancestry composition and study design can affect risk estimation in diverse populations. Using PRS developed primarily from European ancestry populations may not accurately reflect risk in individuals with different genetic backgrounds.
Implications for Precision Medicine
This work lays a foundation for refining genomic prediction in admixed populations and contributes to ongoing efforts to ensure that advances in precision medicine are globally relevant. By including underrepresented populations in genetic research, scientists can develop more accurate and equitable risk prediction models. This is crucial for ensuring that the benefits of precision medicine are accessible to all, regardless of their ancestry.
Conflict of Interest
One author, I.F.M., has received honorarium from the Parkinson’s Foundation PD GENEration Steering Committee and Aligning Science Across Parkinson’s Global Parkinson Genetic Program (ASAP-GP2).
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