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

TLDR

Mendelian randomization uses genetic variants as instrumental variables to infer causal effects, and two‑sample summary‑data MR is now common, but the large number of variants increases the risk of pleiotropy violating IV assumptions. The paper develops and evaluates methods that detect and correct pleiotropy in two‑sample summary‑data MR by adapting meta‑regression and random‑effects models and proposing goodness‑of‑fit statistics to compare IVW and MR‑Egger. It focuses on the Inverse Variance Weighted (IVW) method, which assumes all variants are valid IVs, and MR‑Egger regression, which allows all variants to violate IV assumptions, and investigates two random‑effects models to provide robustness to pleiotropy under IVW while proposing statistics to compare the two approaches. © 2017 The Authors; published in Statistics in Medicine by John Wiley & Sons Ltd.

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