TY - JOUR T1 - Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption JF - bioRxiv DO - 10.1101/159442 SP - 159442 AU - Jack Bowden AU - Fabiola Del Greco M AU - Cosetta Minelli AU - Qingyuan Zhao AU - Debbie A Lawlor AU - Nuala A Sheehan AU - John Thompson AU - George Davey Smith Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/02/27/159442.abstract N2 - 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 of each ratio estimate. We show that the most popular (1st order) approximation can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, an ostensibly more accurate (2nd order) approximation can dramatically increase the chances of failing to detect heterogeneity, when it is truly present. Here we derive a modified 2nd order approximation to the variance that makes use of the derived causal estimate to mitigate both of these adverse effects.Results Using Monte Carlo simulations, we show that the modified 2nd order approximation outperforms both its 1st and 2nd order counterparts in terms of heterogeneity quantification and causal estimation. The added benefit is most noticeable when the genetic instruments are weak, or the causal effect 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 2nd order weighting within two-sample summary data MR studies for model fitting, quantifying heterogeneity and outlier detection. ER -