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

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 ra

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

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

  • 引用：Zhai S, Zhang H, Mehrotra DV, Shen J. (2022). Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods. Nature communications. PMID 36075892 · PMC9458667 · DOI 10.1038/s41467-022-32407-9
  • 证据类型：RANDOMIZED_TRIAL
  • 主题：clinical-genomics
  • 被引次数（采集时）：46
  • 原始记录：[Europe PMC](https://europepmc.org/article/MED/36075892)
  • 来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

    • 作者摘要（按来源许可复用）

    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.

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