Advancing Precision Medicine through Polygenic Risk Scores: From Statistical Innovation to Clinical Implementation
DOI:
https://doi.org/10.71321/ptbkr794Keywords:
Polygenic risk scores, Genome-wide association studies, Precision medicine, Clinical implementationAbstract
The polygenic risk scores (PRS) have emerged as a transformative approach for quantifying inherited predisposition to complex diseases, leveraging the unprecedented expansion of genome-wide association studies (GWAS) and advances in statistical genetics. By aggregating the marginal effects of millions of common variants, PRS provide a single metric of genetic liability that can achieve predictive performance comparable to traditional clinical risk factors. Current methodologies are undergoing a paradigm shift, moving beyond simple linear additive models to incorporate complex linkage disequilibrium (LD) structures, multi-ancestry frameworks, and functional genomic landscapes. In particular, the integration of regulatory annotations, including expression quantitative trait loci (eQTL), chromatin accessibility, and cell-type-specific enhancers, has enhanced both the biological interpretability and predictive robustness of these scores.
This review synthesizes the rapid methodological evolution of PRS, encompassing Bayesian shrinkage frameworks, machine learning algorithms, and functionally informed strategies designed to mitigate the persistent Eurocentric biases in current datasets. We critically evaluate the evidence supporting the integration of PRS into clinical workflows, focusing on cardiovascular diseases, oncology, and neuropsychiatric disorders, where genetic stratification can enhance preventive interventions and diagnostic precision. Despite this progress, we identify significant challenges to widespread adoption, including the reduced portability of scores across diverse populations, the lack of standardized clinical thresholds, and complex ethical considerations related to health equity.
Finally, we propose a multidisciplinary roadmap for the future of PRS, emphasizing the necessity of global biobank diversity, dynamic risk modeling that incorporates temporal and environmental factors, and the seamless integration of genomic insights into electronic health records. Collectively, these advancements are essential for transitioning PRS from a powerful research tool into an equitable and actionable component of the precision medicine toolkit.
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