Biostatistics and Research Decision Sciences, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Bioinformatics. 2020 May 1;36(10):3162-3168. doi: 10.1093/bioinformatics/btaa125.
It is of substantial interest to discover novel genetic markers that influence drug response in order to develop personalized treatment strategies that maximize therapeutic efficacy and safety. To help enable such discoveries, we focus on testing the association between the cumulative effect of multiple single nucleotide polymorphisms (SNPs) in a particular genomic region and a drug response of interest. However, the currently existing methods are either computational inefficient or not able to control type I error and provide decent power for whole exome or genome analysis in Pharmacogenetics (PGx) studies with small sample sizes.
In this article, we propose the Composite Kernel Association Test (CKAT), a flexible and robust kernel machine-based approach to jointly test the genetic main effect and SNP-treatment interaction effect for SNP-sets in Pharmacogenetics (PGx) assessments embedded within randomized clinical trials. An analytic procedure is developed to accurately calculate the P-value so that computationally extensive procedures (e.g. permutation or perturbation) can be avoided. We evaluate CKAT through extensive simulation studies and application to the gene-level association test of the reduction in Clostridium difficile infection recurrence in patients treated with bezlotoxumab. The results demonstrate that the proposed CKAT controls type I error well for PGx studies, is efficient for whole exome/genome association analysis and provides better power performance than existing methods across multiple scenarios.
The R package CKAT is publicly available on CRAN https://cran.r-project.org/web/packages/CKAT/.
Supplementary data are available at Bioinformatics online.
发现影响药物反应的新遗传标记对于制定个性化治疗策略以最大限度地提高治疗效果和安全性具有重要意义。为了帮助实现这一目标，我们专注于测试特定基因组区域中多个单核苷酸多态性 (SNP) 的累积效应与感兴趣的药物反应之间的关联。然而，目前现有的方法要么计算效率低下，要么无法控制 I 型错误，并且在小型样本量的药物基因组学 (PGx) 研究中，无法为全外显子或全基因组分析提供足够的功效。
在本文中，我们提出了复合核关联测试 (CKAT)，这是一种基于灵活稳健核机器的方法，用于联合测试药物基因组学 (PGx) 评估中 SNP 集的遗传主效应和 SNP-治疗相互作用效应。开发了一种分析程序来准确计算 P 值，从而避免了计算密集的程序（例如置换或微扰）。我们通过广泛的模拟研究和对 bezlotoxumab 治疗的艰难梭菌感染复发减少的基因水平关联测试的应用来评估 CKAT。结果表明，所提出的 CKAT 可以很好地控制 PGx 研究中的 I 型错误，对于全外显子/基因组关联分析效率高，并在多个场景下提供比现有方法更好的功效性能。
R 包 CKAT 可在 CRAN https://cran.r-project.org/web/packages/CKAT/ 上公开获得。
补充数据可在生物信息学在线获得。
