Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that identify a common causal effect. There is a general lack of awareness that this effect homogeneity assumption can be violated when there are multiple causal pathways involved, even if all the instrumental variables are valid. In this article, we introduce a latent mixture model MR-PATH that groups instruments that yield similar causal effect estimates together. We develop a Monte-Carlo EM algorithm to fit this mixture model, derive approximate confidence intervals for uncertainty quantification, and adopt a modified Bayesian Information Criterion (BIC) for model selection. We verify the efficacy of the Monte-Carlo EM algorithm, confidence intervals, and model selection criterion using numerical simulations. We identify potential mechanistic heterogeneity when applying our method to estimate the effect of high-density lipoprotein cholesterol on coronary heart disease and the effect of adiposity on type II diabetes.
翻译:在流行病学和遗传学中,门德尔兰随机化(MR)是一种流行的方法,它利用遗传变异作为因果关系推断的工具变量。现有的MR方法通常假定大多数遗传变异物都是有效的工具变量,能够确定共同因果关系。人们普遍缺乏认识,即使所有工具变量都是有效的,如果涉及多重因果途径,这种效果会违反同质性假设。在本篇文章中,我们引入一种潜在的混合物模型MR-PATH,将产生类似因果估计结果的工具组合在一起。我们开发了一种蒙特-卡洛的EM算法,以适应这一混合物模型,得出不确定性量化的大致信任间隔,并采用了经修改的贝耶斯信息标准(BIC)进行模型选择。我们用数字模拟来核查蒙特-卡洛的EM算法、信任间隔和模型选择标准的有效性。我们在使用我们的方法来估计高密度脂蛋白质素对冠状心脏病的影响和二型糖尿病的影响时,我们发现潜在的机械性异性异性。