Mendelian Randomization with longitudinal exposure data: simulation study and real data application
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Background and aim
Mendelian Randomization (MR) is a widely used tool to estimate causal effects using genetic variants as instrumental variables. MR is limited to cross-sectional summary statistics of different samples and time points to analyse time-varying effects. We aimed at using longitudinal summary statistics for an exposure in a multivariable MR setting and validating the effect estimates for the mean, slope and within-individual variability.
Simulation study
We tested our approach in various scenarios for power and type I error, depending on the correlation structure between the mean, slope and variability, and regression model. We observed high power to detect causal effects of the mean and slope throughout the simulation, but the variability effect was low powered in case of correlation between the mean and variability. Mis-specified regression models led to lower power and increased the type I error.
Real data application
We applied our approach to two real data sets (POPS, UK Biobank). We detected significant causal estimates for both the mean and the slope in both cases, but no effect of the variability. However, even in the UK Biobank we only had weak instruments.
Conclusion
We used a new approach to test a time-varying exposure for causal effects of the exposure’s mean, slope and variability. The simulation with strong instruments seems promising, but also highlights three crucial points: 1) the difficulty to define the correct exposure regression model, 2) the dependency on the genetic correlation, and 3) the lack of strong instruments in real data. Taken together, this demands a cautious evaluation of the results, taking into account known biology and trajectory of the exposure.
