A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization
File(s)
Published version
Author(s)
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.
Date Issued
2017-01-23
Date Acceptance
2016-12-10
Citation
Statistics in Medicine, 2017, 36 (11), pp.1783-1802
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 (https://creativecommons.org/licenses/by/3.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Identifier
http://www.ncbi.nlm.nih.gov/pubmed/28114746
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Mathematical & Computational Biology
Public, Environmental & Occupational Health
Medical Informatics
Medicine, Research & Experimental
Statistics & Probability
Research & Experimental Medicine
Mathematics
instrumental variables
Mendelian randomization
meta-analysis
MR-Egger regression
pleiotropy
REGRESSION DILUTION BIAS
CORONARY-HEART-DISEASE
CAUSAL INFERENCE
METAANALYSIS
HETEROGENEITY
TESTIMATION
0104 Statistics
1117 Public Health And Health Services
Publication Status
Published
Coverage Spatial
England