Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

Abstract

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation.

Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid.

Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates.

Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes.

Keywords: Mendelian randomization; direct effect; indirect effect; instrumental variable; mediation.

Figure 1.

Causal directed acyclic graph (DAG) of Mendelian randomization assumptions.

Figure 2.

Causal directed acyclic graph (DAG) leading to direct and indirect causal effects of variable X on Y with mediator Z, associated instrumental variables G_X and G_Z, and confounders U.

Figure 3.

Causal directed acyclic graph (DAG) illustrating direct and indirect causal effects of variable X on Y with mediator Z with post-treatment confounder U*.

Figure 4.

Path diagram for estimation of causal effect of exposure (X) on outcome (Y) in the presence of unmeasured confounding using instrumental variable (G).

Figure 5.

Path diagram for estimation of causal direct and indirect effects of exposure (X) on outcome (Y) with mediator (Z) in the presence of unmeasured confounding using instrumental variables (G_X,G_Z) in a structural equation model (SEM) framework.

Figure 6.

Diagram illustrating mediation scenarios: (i) typically investigated in the context of a randomized trial, (ii) proposed in this paper, with G_X and G_Z representing genetic variants used as instrumental variables. Confounding variables are omitted from the diagram.