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An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings.
Mendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This can be extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome. We use simulations and theoretical arguments to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a pleiotropic pathway, a mediator and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and how such tests can be extrapolated to the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index. We show that MVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.
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