Fig. 3: Simulation results of causal effect estimation when there is correlated and uncorrelated pleiotropy (MVMR). | Nature Communications

Fig. 3: Simulation results of causal effect estimation when there is correlated and uncorrelated pleiotropy (MVMR).

From: MR-EILLS: an invariance-based Mendelian randomization method integrating multiple heterogeneous GWAS summary datasets

Fig. 3

The number of IVs is 100, and the proportion of invalid IVs is 30%. The number of populations is \(E=3\). Among a total of eight exposures, two (\({X}_{1}\) and \({X}_{2}\)) are causal exposures (with a causal effect of 0.2), and the other six (\({X}_{3},{\mathrm{}}...,{X}_{8}\)) are spurious exposures (with a causal effect of 0). 200 repeated datasets were generated in all simulations. Data in boxplots are presented as median values and interquartile range.

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