title: 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.
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK and Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK sb452@medschl.cam.ac.uk.
authors.forename: Stephen
authors.initials: S
authors.lastname: Burgess
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK and Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Rhian M
authors.initials: RM
authors.lastname: Daniel
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK and Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Adam S
authors.initials: AS
authors.lastname: Butterworth
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK and Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Simon G
authors.initials: SG
authors.lastname: Thompson
mesh_terms: D015984:Causality; D014644:Genetic Variation; D006801:Humans; D057182:Mendelian Randomization Analysis; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis
keywords: Mendelian randomization; direct effect; indirect effect; instrumental variable; mediation