Advancing the clinical translation of polygenic risk scores for complex diseases

Over a century after Mendel established the foundations of inheritance, genomics has revealed that most human traits and common diseases arise from the cumulative effects of thousands of genetic variants. The past decade has seen exponential progress in polygenic risk scores (PRS)—statistical models that aggregate these small effects across the genome to estimate an individual’s genetic predisposition to complex diseases. As biobanks and multi-ancestry datasets expand, PRS are increasingly demonstrating predictive power comparable to, and often exceeding, traditional clinical risk factors. However, translation into clinical and public health practice remains limited by key challenges: ancestry bias, lack of standardization in model reporting, unclear clinical utility thresholds, and limited integration with electronic health and multi-omic data. Bridging these gaps requires a multidisciplinary effort that connects genomics, biostatistics, informatics, and clinical implementation science. This Research Topic aims to consolidate emerging evidence, methods, and frameworks that move PRS from statistical constructs toward actionable tools in precision prevention and care.This Research Topic seeks to bring together leading work advancing the clinical translation of polygenic risk scores, from model development and ancestry calibration to deployment within healthcare systems. We aim to highlight innovations in multi-ancestry modeling, local ancestry inference, cross-omic integration, clinical decision support, and ethical frameworks that ensure equitable, evidence-based use of PRS in diverse populations. The goal is to define practical pathways that connect genomic prediction to measurable improvements in patient outcomes.We welcome articles addressing, but not limited to, the following themes:• Methodological advances in PRS construction, validation, and calibration across ancestries• Local ancestry inference and its role in enhancing PRS portability• Integration of PRS with monogenic testing, biomarkers, and environmental risk models• Multi-omic and transcriptome-informed polygenic risk modeling• Clinical utility studies demonstrating implementation and outcomes in preventive care• PRS-based stratification for lifestyle or pharmacologic interventionsWe invite the submission of original research articles, reviews, perspectives, methodological papers, and case studies. Topic editors: Puya G. Yazdi, Sare Verstockt, Isabelle Cleynen Source: https://www.frontiersin.org/research-topics/77369/advancing-the-clinical-translation-of-polygenic-risk-scores-for-complex-diseases