Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities

Area of Science:

• Genomics
• Pharmacogenomics
• Statistical Genetics

Background:

• Polygenic risk scores (PRSs) are increasingly used for disease prediction.
• Applying PRSs to pharmacogenomics (PGx) offers potential for patient stratification and drug response prediction.
• Unique challenges exist in PGx PRS, including base cohort selection, small sample sizes, and complex modeling.

Purpose of the Study:

• To systematically review PRS applications and method development in PGx GWAS.
• To propose novel strategies for PRS construction and bias reduction in PGx.
• To address challenges in applying PRSs to PGx GWAS.

Main Methods:

• Systematic review of PRS in PGx GWAS literature.
• Development of a novel PRS application strategy using combined PGx and disease GWAS data.
• Introduction of a new Bayesian method (PRS-PGx-Bayesx) to mitigate prediction bias.

Main Results:

• The systematic review identified key trends, challenges, and gaps in PGx PRS.
• Simulations demonstrated the advantages of the proposed PRS strategy and Bayesian method.
• The novel methods showed improved performance over existing PRS approaches in PGx settings.

Conclusions:

• Current PRS methods require adaptation for effective PGx applications.
• Leveraging diverse GWAS data and advanced statistical modeling can overcome PGx PRS challenges.
• The proposed methods offer solutions for more accurate and equitable PGx PRS.