Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods

Song Zhai, Hong Zhang, Devan V. Mehrotra and Judong Shen ()
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Song Zhai: Merck & Co., Inc.
Hong Zhang: Merck & Co., Inc.
Devan V. Mehrotra: Merck & Co., Inc.
Judong Shen: Merck & Co., Inc.

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches.

Date: 2022
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DOI: 10.1038/s41467-022-32407-9

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