School of Psychological Science, University of Bristol, Bristol BS8 1TH, UK.
MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK.
Genes (Basel). 2022 Aug 26;13(9):1541. doi: 10.3390/genes13091541.
Mendelian randomisation (MR) is an increasingly popular method for strengthening causal inference in epidemiological studies. -MR in particular uses genetic variants in the gene region of a drug target protein as an instrumental variable to provide quasi-experimental evidence for on-target drug effects. A limitation of this framework is when the genetic variant is correlated to another variant that also effects the outcome of interest (confounding through linkage disequilibrium). Methods for correcting this bias, such as multivariable MR, struggle in a setting because of the high correlation among genetic variants. Here, through simulation experiments and an applied example considering the effect of interleukin 6 receptor signaling on coronary artery disease risk, we present an alternative method for attenuating bias that does not suffer from this problem. As our method uses both MR and the product and difference method for mediation analysis, our proposal inherits all assumptions of these methods. We have additionally developed an R package, TwoStepCisMR, to facilitate the implementation of the method.
孟德尔随机化(MR)是一种越来越受欢迎的方法,可用于加强流行病学研究中的因果推断。 -MR 特别使用药物靶蛋白基因区域中的遗传变异作为工具变量,为靶向药物效应提供准实验证据。该框架的一个局限性是当遗传变异与另一个也影响感兴趣的结果的变异相关(通过连锁不平衡混杂)时。由于遗传变异之间存在高度相关性,因此用于校正这种偏差的方法(如多变量 MR)在这种情况下难以实施。在这里,通过模拟实验和考虑白细胞介素 6 受体信号对冠心病风险的影响的应用实例,我们提出了一种替代方法,可以减轻这种偏差,而不会受到此问题的影响。由于我们的方法同时使用了 MR 和中介分析的乘积和差方法,因此我们的建议继承了这些方法的所有假设。我们还开发了一个 R 包 TwoStepCisMR,以方便该方法的实施。