Carene Anne Alene Ndong Sima

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.

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, a polygenic risk score (PRS) analysis was conducted using PRSice-2. Summary statistics were used from two PD association studies as base datasets. The target dataset was split into training (70%; n=979) and validation (30%; n=419) cohorts. Various clumping window sizes, linkage disequilibrium thresholds, and p -value thresholds were tested to determine the optimal combination for risk prediction. Additionally, we investigated the variance explained by different combinations of covariates. Finally, top contributing variants were identified and cross-referenced with inferred local ancestry to assess ancestry-specific effects. Overall, modest predictive performance was observed (AUC: 0.6307-0.6311). Age at recruitment was the strongest individual predictor, while sex contributed the least. Local ancestry analysis indicated that the top contributing variants were not ancestry-specific risk variants. 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.

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.

Globally, tuberculosis (TB) is the second most lethal infectious disease. However, in sub‐Saharan Africa, TB has the largest disease burden, with drug‐resistant TB increasingly becoming a concern. The social and economic impact of TB should not be overlooked, especially in areas where healthcare systems are overburdened, and resources need to be allocated judiciously. The aim of pharmacogenetics (PGx) is to improve therapeutic response and to minimize adverse drug reactions by selecting the most optimal drug and dosage for the individual patient. Implementation of PGx into routine clinical care has been slow, especially in resource‐limited settings, because of perceived high costs relative to uncertain benefit. Given the impact of TB on the disease and disability burden in these regions, a better understanding and optimization of TB treatment in understudied African populations is vital. The first weeks of treatment are the most crucial for treatment success, and a point‐of‐care pre‐emptive PGx test could start patients on the most bactericidal and least toxic drug combination. This may potentially reduce the number of patients returning to clinical care and streamline the use of limited resources across the healthcare system. This review explores the status of TB PGx in Africa, the utility of existing TB PGx testing panels, and the economic feasibility in developing a clinically valuable, cost‐effective, pre‐emptive PGx test to guide optimized, new dosing regimens specifically for African population groups. TB is a disease of poverty, but investment in PGx research in African populations could ensure improved treatments and long‐term cost savings.