Journal
JOURNAL OF CLINICAL EPIDEMIOLOGY
Volume 69, Issue -, Pages 208-216Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2015.08.001
Keywords
Mendelian randomization; Instrumental variable; Causal inference; Genetic variants; Genetic predictors; Aetiology; Translational Genetics
Funding
- Wellcome Trust [100114]
- MRC [MR/L003120/1] Funding Source: UKRI
- British Heart Foundation [RG/08/014/24067, SP/08/007/23628] Funding Source: researchfish
- Medical Research Council [MR/L003120/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0512-10165] Funding Source: researchfish
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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. (C) 2016 The Authors. Published by Elsevier Inc.
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