A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.
A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.
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DOI：
10.1002/sim.7221
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发表时间：
2017-05-20
影响因子：
2
通讯作者：
Thompson J
中科院分区：
文献类型：
--
作者：
Bowden J;Del Greco M F;Minelli C;Davey Smith G;Sheehan N;Thompson J
孟德尔随机化（MR）通过调用工具变量（IV）假设，使用遗传数据来探讨流行病学研究中的因果关系问题。近年来，通过综合从大型独立研究人群中收集的遗传关联的汇总数据估计来尝试MR分析已变得司空见惯。这被称为双样本汇总数据MR。不幸的是，由于可以很容易地纳入汇总数据MR分析的变体数量庞大，因此越来越有可能由于多效性而导致一些不符合IV假设。迫切需要开发能够检测和纠正多效性的方法，以保持MR方法在这种情况下的有效性。在本文中，我们的目的是澄清如何从主流Meta分析中建立Meta回归和随机效应建模的方法来执行这项任务。具体来说，我们重点关注两种对比方法：逆方差加权（IVW）方法，该方法以最简单的形式假设所有遗传变异都是有效的IV，以及MR-Egger回归方法，该方法允许所有变异都违反IV假设，尽管是以一种特定的方式。我们研究了两种流行的随机效应模型在IVW方法下为多效性提供鲁棒性的能力，并提出了量化IVW方法相对于MR-Egger回归的相对拟合优度的统计量。版权所有© 2017作者. JohnWiley & Sons Ltd发布的医学统计数据
Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthesising summary data estimates of genetic association gleaned from large and independent study populations. This is referred to as two‐sample summary data MR. Unfortunately, due to the sheer number of variants that can be easily included into summary data MR analyses, it is increasingly likely that some do not meet the IV assumptions due to pleiotropy. There is a pressing need to develop methods that can both detect and correct for pleiotropy, in order to preserve the validity of the MR approach in this context. In this paper, we aim to clarify how established methods of meta‐regression and random effects modelling from mainstream meta‐analysis are being adapted to perform this task. Specifically, we focus on two contrastin g approaches: the Inverse Variance Weighted (IVW) method which assumes in its simplest form that all genetic variants are valid IVs, and the method of MR‐Egger regression that allows all variants to violate the IV assumptions, albeit in a specific way. We investigate the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach, and propose statistics to quantify the relative goodness‐of‐fit of the IVW approach over MR‐Egger regression. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd