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

By J. Bowden, F. Del Greco M, Cosetta Minelli, George Davey Smith, Nuala Sheehan, John F. Thompson. Statistics in Medicine, 2017

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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

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/94535b29e636821fe47eb72ac85adcbb66993ba2

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Citations in the corpus

The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “Here, we consider each SNP to mimic an independent RCT and we can adapt tools developed for meta-analysis (Bowden et al., 2017a) to combine the results obtained from each of the SNPs, giving an overall causal estimate that is better powered (Bowden et al.”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, background): “t and its standard error are denoted as ^ j and ˙ Y j . For j= 1;2;:::;N, we assume: ^ j ˘N( j;˙ 2 X j ); (2) 2^ j ˘N( j;˙ Y j ): (3) and j ˘N(0;˙2): Because of the evidence of systematic pleiotropy (Bowden et al., 2017; Smith and Ebrahim, 2004; Zhao et al., 2018), some IVs may also slightly aect th”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “It is a special instance of the INstrument Strength Independent of Direct Effect (INSIDE) assumption [9] that is common in the summary-data MR literature.”
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, methodology): “Approaches such as MR-Egger and weighted median MR can provide reliable evidence regarding causation even in the presence of confounding through unbalanced horizontal pleiotropy [69].”
Interpreting findings from Mendelian randomization using the MR-Egger method (2017, neutral): “Similar concerns have been raised elsewhere [25, 29, 31, 49].”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, methodology): “This is referred to as the IVW estimate and the general framework as two-sample summary data MR.(2,3) For further details, see Box 1.”
Multivariable Mendelian Randomization and Mediation. (2020, background): “There are a number of sensitivity analyses that can be used to try to identify pleiotropic effects in the MR analysis (Chen et al. 2008; Davey Smith and Hemani 2014; Bowden et al. 2015; Bowden et al. 2016; Bowden et al. 2017; Hartwig et al. 2017; Spiller et al. 2018).”
An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings (2018, background): “The causes and consequences of pleiotropy in MR are described in detail elsewhere.(1,9,10,13,14,20) In addition to assumptions IV1–IV3, there are additional assumptions and considerations that apply to all instrumental variable estimation, including MR and MVMR.”
Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption (2017, background): “, the Instrument Strength Independent of Direct Effects – InSIDE – assumption).(5,8) More recently, other summary data MR methods that allow relaxing (but not eliminating) the instrumental variable assumptions regarding horizontal pleiotropy have been proposed.”
Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption (2018, methodology): “When / is set to 1 in Equations (7) and (8), this is equivalent to assuming a fixed-effect model, and minimizing Equation (7) with respect to b gives the fixed-effect exact IVW estimate, as described in Box 2.”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, methodology): “This is referred to as the IVW estimate and the general framework as two-sample summary data MR.(2,3) For further details, see Box 1.”