Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab019.
Mendelian randomization (MR) is a powerful instrumental variable (IV) method for estimating the causal effect of an exposure on an outcome of interest even in the presence of unmeasured confounding by using genetic variants as IVs. However, the correlated and idiosyncratic pleiotropy phenomena in the human genome will lead to biased estimation of causal effects if they are not properly accounted for. In this article, we develop a novel MR approach named MRCIP to account for correlated and idiosyncratic pleiotropy simultaneously. We first propose a random-effect model to explicitly model the correlated pleiotropy and then propose a novel weighting scheme to handle the presence of idiosyncratic pleiotropy. The model parameters are estimated by maximizing a weighted likelihood function with our proposed PRW-EM algorithm. Moreover, we can also estimate the degree of the correlated pleiotropy and perform a likelihood ratio test for its presence. Extensive simulation studies show that the proposed MRCIP has improved performance over competing methods. We also illustrate the usefulness of MRCIP on two real datasets. The R package for MRCIP is publicly available at https://github.com/siqixu/MRCIP.
孟德尔随机化（MR）是一种强大的工具变量（IV）方法，即使在存在未测量混杂的情况下，也可以使用遗传变异作为 IV 来估计暴露对感兴趣结局的因果效应。然而，如果不适当考虑人类基因组中相关和特异的多效性现象，可能会导致因果效应的估计存在偏差。在本文中，我们开发了一种名为 MRCIP 的新 MR 方法，以同时考虑相关和特异的多效性。我们首先提出了一个随机效应模型来明确建模相关的多效性，然后提出了一种新的加权方案来处理特异的多效性。模型参数通过最大化加权似然函数，使用我们提出的 PRW-EM 算法进行估计。此外，我们还可以估计相关多效性的程度，并进行存在性的似然比检验。广泛的模拟研究表明，所提出的 MRCIP 比竞争方法具有更好的性能。我们还在两个真实数据集上说明了 MRCIP 的有用性。MRCIP 的 R 包可在 https://github.com/siqixu/MRCIP 上公开获取。
