Developing Polygenic risk scores for precision medicine: a statistical perspective

Music City Center 

Precision medicine aims to personalize treatments by leveraging patients' molecular markers. Polygenic risk scores (PRSs) have emerged as promising tools for improving drug response prediction and patient stratification, thereby accelerating the advancement of precision medicine. This talk will explore three primary strategies for developing PRSs for precision medicine: (1) by utilizing large-scale genome-wide association studies (GWAS) of related diseases, (2) by leveraging independent pharmacogenomics (PGx) studies of related drug response, (3) by jointly modeling both. Each of these strategies presents unique advantages and disadvantages. This talk critically evaluates these strategies, focusing on their ability of capturing prognostic and predictive effects, predicting drug response, and effectively stratifying patients. We further delve into novel PRS methods we have developed for building predictive PRSs with differential treatment effects, including machine learning, Bayesian, and transfer learning-based approaches. Lastly, practical considerations and statistical insights for developing robust PRSs in the context of randomized clinical trials will be also discussed.