Mendelian randomization (MR) is a natural experimental design based on the random transmission of genes from parents to offspring. However, this inferential basis is typically only implicit or used as an informal justification. As parent-offspring data becomes more widely available, we advocate a different approach to MR that is exactly based on this natural randomization, thereby formalizing the analogy between MR and randomized controlled trials. We begin by developing a causal graphical model for MR which represents several biological processes and phenomena, including population structure, gamete formation, fertilization, genetic linkage, and pleiotropy. This causal graph is then used to detect biases in population-based MR studies and identify sufficient confounder adjustment sets to correct these biases. We then propose a randomization test in the within-family MR design using the exogenous randomness in meiosis and fertilization, which is extensively studied in genetics. Besides its transparency and conceptual appeals, our approach also offers some practical advantages, including robustness to misspecified phenotype models, robustness to weak instruments, and elimination of bias arising from population structure, assortative mating, dynastic effects, and horizontal pleiotropy. We conclude with an analysis of a pair of negative and positive controls in the Avon Longitudinal Study of Parents and Children. The accompanying R package can be found at https://github.com/matt-tudball/almostexactmr.
翻译:门捷列夫随机化(MR)是基于从父母遗传到后代的基因随机性质的一种自然实验设计。然而,这种推论基础通常只是隐含的或被用作非正式证明。随着亲子数据的普及,我们建议采用一种不同的MR方法,该方法确切地基于这种自然随机化,从而形式化MR和随机对照试验之间的类比。我们首先开发了一种MR的因果图模型,代表了几种生物过程和现象,包括种群结构、配子形成、受精、遗传连锁和多效性。然后使用这个因果图来检测基于人群的MR研究中的偏差,并确定足够的混杂变量调整集来纠正这些偏差。随后,在家庭内MR设计中使用减数分裂和受精中的外源性随机性提出了一种随机化测试,这在遗传学中得到了广泛的研究。除了它的透明度和概念上的吸引力外,我们的方法还提供了一些实际上的优势,包括对错误指定的表型模型的稳健性,对软弱工具的稳健性,以及消除由种群结构、配对配对、王朝效应和水平多效性产生的偏差。最后,我们通过对父母和儿童的阿冯长期研究中一对阴性和阳性对照的分析来总结。相应的R包可以在https://github.com/matt-tudball/almostexactmr找到。