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

By Jack Bowden, Fabiola Del Greco M, Cosetta Minelli, Qingyuan Zhao, Debbie A Lawlor, Nuala A Sheehan, John Thompson, George Davey Smith

Posted 05 Jul 2017
bioRxiv DOI: 10.1101/159442 (published DOI: 10.1093/ije/dyy258)

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 and causal estimation. The added benefit is most noticeable when the genetic instruments are weak, the causal effect is large and the number of instruments is large. 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 model fitting, quantifying heterogeneity and outlier detection.

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