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

By J. Bowden, F. Del Greco M, Cosetta Minelli, Qingyuan Zhao, Deborah Lawlor, Nuala Sheehan, J. Thompson, George Davey Smith. bioRxiv, 2018

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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 requires an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular ‘1st order’ weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate ‘2nd 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 1st and 2nd 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 1st and 2nd 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 to 1st order weighting. Moreover, 1st 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 1st order weights) but further research is required to understand their strengths and weaknesses in specific settings.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/85c6b8884e12e03c3672fcf4694c4b9db64bdaf0

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Citations in the corpus

Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, methodology): “[10] considered using each SNP’s contribution to the generalized Q statistic to assess whether it is an outlier.”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, methodology): “Under first-order weighting, Figure 3 (right) is equivalent to the funnel plot first used by Bowden et al.11 to visualize MR analyses and to look for asymmetry as a sign of pleiotropy.”
Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018, background): “and Bowden et al.(4) They are included here for the purposes of illustration, rather than to draw any novel epidemiological conclusions.”
Meta‐analysis and Mendelian randomization: A review (2019, methodology): “For further technical details including a multiplicative random effects model implementation, see other studies.(5,20)”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, methodology): “Cochran’s Q statistic to detect heterogeneity due to pleiotropy when it is truly present (51) (i.”