Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, 07065, USA.
Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA, 19454, USA.
Nat Commun. 2022 Sep 8;13(1):5278. doi: 10.1038/s41467-022-32407-9.
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.
多基因风险评分（PRS）在过去几年中已成功用于预测人类疾病和复杂特征。对于随机临床试验中的药物反应预测，一种常见的做法是将来自疾病全基因组关联研究（GWAS）的 PRS 直接应用于相应的药物基因组学（PGx）环境。在这里，我们表明这种方法依赖于对所选遗传变异的预后和预测效果的严格假设。我们通过同时对预后和预测效果进行建模，提出了从疾病 PRS 到 PGx PRS 方法的转变，并通过开发一系列 PRS-PGx 方法进一步实现了这一转变，包括一种新的贝叶斯回归方法（PRS-PGx-Bayes）。模拟研究表明，PRS-PGx 方法通常优于疾病 PRS 方法，而 PRS-PGx-Bayes 优于所有其他 PRS-PGx 方法。我们进一步将 PRS-PGx 方法应用于来自大型心血管随机临床试验（IMPROVE-IT）的 PGx GWAS 数据，以预测与治疗相关的 LDL 胆固醇降低。结果表明，与疾病 PRS 方法相比，PRS-PGx-Bayes 在预测准确性和捕捉特定于治疗的预测效果方面都有了很大的提高。
