A review of methods and software for polygenic risk score analysis

Polygenic risk scores (PRSs) are emerging as powerful tools for predicting individual susceptibility to various diseases and traits based on genetic variants. These scores integrate information from multiple genetic markers associated with the trait or disease of interest, offering personalized risk assessment and enhancing disease management strategies. PRS is an active area of research and is being studied in various fields, such as disease prediction. This review explores the advancement of PRS research, focusing on methodological approaches, software tools, and applications across diverse disciplines. A systematic literature review identified 40 relevant articles classified based on PRS methods and software. Key methods for PRS computation, including penalized regression and threshold-based approaches, Bayesian approaches, and machine learning approaches, are discussed, along with notable software and their features. Applications of PRS in disease prevention are highlighted. Challenges and future directions, such as increasing diversity in genetic data, integrating environmental factors, and evaluating clinical implications, are also discussed to guide future research and implementation efforts.