A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization

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Title: A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization
Authors: Bowden, J
Del Greco M, F
Minelli, C
Davey Smith, G
Sheehan, N
Thompson, J
Item Type: Journal Article
Abstract: 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.
Issue Date: 23-Jan-2017
Date of Acceptance: 10-Dec-2016
ISSN: 1097-0258
Publisher: Wiley
Start Page: 1783
End Page: 1802
Journal / Book Title: Statistics in Medicine
Volume: 36
Issue: 11
Copyright Statement: © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Mathematical & Computational Biology
Public, Environmental & Occupational Health
Medical Informatics
Medicine, Research & Experimental
Statistics & Probability
Research & Experimental Medicine
instrumental variables
Mendelian randomization
MR-Egger regression
0104 Statistics
1117 Public Health And Health Services
Publication Status: Published
Conference Place: England
Open Access location:
Appears in Collections:Infectious Disease Epidemiology
National Heart and Lung Institute
Faculty of Medicine

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