Polygenic risk scores and Parkinson’s disease in South Africa advancing ancestry informed disease prediction

Parkinson’s disease (PD) is a complex neurodegenerative disorder with environmental and genetic influences. Using genotyping array data of 661 South African PD cases and 737 controls, we conduct a polygenic risk score (PRS) analysis using PRSice-2. Summary statistics from two PD association studies have been used as base datasets. We split the target dataset into training (70%; n = 979) and validation (30%; n = 419) cohorts. We test various clumping window sizes, linkage disequilibrium thresholds, and p-value thresholds to determine the optimal combination for risk prediction. Additionally, we investigate the variance explained by different combinations of covariates. Overall, we observe modest predictive performance (AUC: 0.5847-0.6183). Age at recruitment emerges as the strongest individual predictor, while sex contributes the least. These findings provide the first evaluation of PRS performance for PD in a highly admixed South African cohort, underscoring the importance of including underrepresented populations in genetic risk prediction. By systematically assessing predictive performance across two base datasets, we highlight how ancestry composition and study design affect risk estimation in diverse populations. 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.Author summary: Parkinson’s disease is a complex brain disorder influenced by both genes and environment. Most of what we know about the genetic contribution to this disease comes from studies in people of European ancestry, leaving major gaps in our understanding of how genetic risk works in other populations. In this study, we examined how well existing genetic risk models, known as polygenic risk scores, can predict Parkinson’s disease in people from South Africa, a population with a rich mix of ancestries. We compared different approaches to building these scores and tested how accurately they could identify individuals with the disease. Although we found that genetic scores alone are modest predictors compared to age, our results highlight important insights about the limits and possibilities of using genetic information in diverse populations. By including underrepresented groups in genetic research, our study takes an important step toward making precision medicine more inclusive and equitable worldwide.