Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption

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Title: Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption
Authors: Bowden, J
Del Greco M, F
Minelli, C
Zhao, Q
Lawlor, DA
Sheehan, NA
Thompson, J
Davey Smith, G
Item Type: Journal Article
Abstract: Background: Two-sample summary-data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated. Methods: Causal estimation and heterogeneity assessment in MR require an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular 'first-order' weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate 'second-order' weights can dramatically increase the chances of failing to detect heterogeneity when it is truly present. We derive modified weights to mitigate both of these adverse effects. Results: Using Monte Carlo simulations, we show that the modified weights outperform first- and second-order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using first- and second-order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared with first-order weighting. Moreover, first-order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary-data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk. Conclusions: We propose the use of modified weights within two-sample summary-data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with first-order weights) but further research is required to understand their strengths and weaknesses in specific settings.
Issue Date: 18-Dec-2018
Date of Acceptance: 9-Nov-2018
ISSN: 1464-3685
Publisher: Oxford University Press (OUP)
Journal / Book Title: International Journal of Epidemiology
Copyright Statement: ┬ęThe Author(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unre-stricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited
Keywords: 0104 Statistics
1117 Public Health And Health Services
Publication Status: Published online
Conference Place: England
Open Access location:
Online Publication Date: 2018-12-18
Appears in Collections:Infectious Disease Epidemiology
National Heart and Lung Institute

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