title: A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.
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. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.
authors.affiliation: MRC Integrative Epidemiology Unit, University of Bristol, U.K.
authors.forename: Jack
authors.initials: J
authors.lastname: Bowden
authors.affiliation: Center for Biomedicine, EURAC research, Bolzano, Italy.
authors.forename: Fabiola
authors.initials: F
authors.lastname: Del Greco M
authors.affiliation: Population Health and Occupational Disease, NHLI, Imperial College, London, U.K.
authors.forename: Cosetta
authors.initials: C
authors.lastname: Minelli
authors.affiliation: MRC Integrative Epidemiology Unit, University of Bristol, U.K.
authors.forename: George
authors.initials: G
authors.lastname: Davey Smith
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, U.K.
authors.forename: Nuala
authors.initials: N
authors.lastname: Sheehan
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, U.K.
authors.forename: John
authors.initials: J
authors.lastname: Thompson
mesh_terms: D003627:Data Interpretation, Statistical; D058685:Genetic Pleiotropy; D006801:Humans; D057182:Mendelian Randomization Analysis; D015201:Meta-Analysis as Topic; D015233:Models, Statistical
keywords: MR-Egger regression; Mendelian randomization; instrumental variables; meta-analysis; pleiotropy