A review of methods and software for polygenic risk score analysis

Area of Science:

• Genetics
• Bioinformatics
• Personalized Medicine

Background:

• Polygenic risk scores (PRSs) are increasingly utilized for predicting individual susceptibility to diseases and traits.
• These scores aggregate data from numerous genetic markers to provide personalized risk assessments.
• PRS research is a dynamic field with significant implications for disease prediction and management.

Purpose of the Study:

• To review advancements in Polygenic risk score (PRS) research.
• To explore methodological approaches, software tools, and applications of PRS.
• To identify challenges and future directions in the field.

Main Methods:

• Systematic literature review of 40 relevant articles.
• Classification of articles based on PRS methods and software.
• Discussion of key PRS computation methods (penalized regression, threshold-based, Bayesian, machine learning).

Main Results:

• Identified and discussed various PRS computation methods and associated software.
• Highlighted applications of PRS in disease prevention strategies.
• Summarized challenges including data diversity and integration of environmental factors.

Conclusions:

• Polygenic risk scores (PRSs) are valuable tools for personalized risk assessment and disease prevention.
• Further research is needed to enhance PRS accuracy, diversity, and clinical utility.
• Integrating environmental factors and addressing ethical considerations are crucial for future PRS implementation.