Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements

Abstract

Polygenic risk scores (PRSs) are becoming an important component of precision medicine, supporting earlier disease prediction and preventive strategies. This review summarizes current applications of PRS, future directions, and key considerations for their use in varied health contexts, including low- and middle-income countries (LMICs). Economic issues such as the declining cost of genetic testing, direct-to-consumer services, and reimbursement policies are discussed alongside ethical challenges including ancestry bias, privacy, informed consent, and clinical interpretation. Recent advances in artificial intelligence (AI) and machine learning (ML) are highlighted for their role in improving PRS accuracy, data analysis, and health system integration. Federated learning (FL) is also considered as a method for privacy-preserving data sharing across institutions. Finally, we emphasize the need for equitable global collaboration and capacity building to ensure responsible and accessible implementation of PRS in public health.

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