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. 2016 Jan:69:208-16.
doi: 10.1016/j.jclinepi.2015.08.001. Epub 2015 Aug 18.

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

Stephen Burgess  1 , Adam S Butterworth  2 , John R Thompson  3
Affiliations

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

Stephen Burgess et al. J Clin Epidemiol. 2016 Jan.

Abstract

Objective: Mendelian randomization is a popular technique for assessing and estimating the causal effects of risk factors. If genetic variants which are instrumental variables for a risk factor are shown to be additionally associated with a disease outcome, then the risk factor is a cause of the disease. However, in many cases, the instrumental variable assumptions are not plausible, or are in doubt. In this paper, we provide a theoretical classification of scenarios in which a causal conclusion is justified or not justified, and discuss the interpretation of causal effect estimates.

Results: A list of guidelines based on the 'Bradford Hill criteria' for judging the plausibility of a causal finding from an applied Mendelian randomization study is provided. We also give a framework for performing and interpreting investigations performed in the style of Mendelian randomization, but where the choice of genetic variants is statistically, rather than biologically motivated. Such analyses should not be assigned the same evidential weight as a Mendelian randomization investigation.

Conclusion: We discuss the role of such investigations (in the style of Mendelian randomization), and what they add to our understanding of potential causal mechanisms. If the genetic variants are selected solely according to statistical criteria, and the biological roles of genetic variants are not investigated, this may be little more than what can be learned from a well-designed classical observational study.

Keywords: Aetiology; Causal inference; Genetic predictors; Genetic variants; Instrumental variable; Mendelian randomization; Translational Genetics.

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Figures

Fig. 1
Fig. 1
Diagrams illustrating scenarios of causal relationships between selected genetic variant(s) G, putative causal trait A, and putative effect trait B, compatible with genetic variant(s) being associated with both traits.
Fig. 2
Fig. 2
Diagram illustrating causal relationships between genetic variant(s) G, putative causal trait (risk factor) A, putative effect trait (outcome) B, and confounders U necessary for instrumental variable assumptions to be satisfied.
Fig. 3
Fig. 3
Diagrams illustrating the difference between pleiotropy (left), where genetic variant G is independently associated with traits A and M, and mediation (right), where G is associated with trait M only via the effect of A.
Fig. 4
Fig. 4
Diagram illustrating additional scenario of causal relationships between selected genetic variant(s) G, underlying putative causal trait C, measured proxy variable A, and putative effect trait B, compatible with genetic variant(s) being associated with both traits A and B (confounding variables are omitted from the diagram).
See this image and copyright information in PMC

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