Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

Abstract

Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence

Fig 1

Examples of Mendelian randomisation and potential violations of assumptions. (A) A simplified causal diagram depicting confounding of the association of alcohol consumption and blood pressure by existing disease or social deprivation. The instrumental variable assumptions are that the genetic variants are associated with the risk factor, that theyhave no other influence on the outcome, except through alcohol, and that there are no confounders of the genetic variants-outcome association. (B) Confounding by ancestry could occur if variants associated with alcohol consumption had different frequencies in different ethnic groups in the population sampled and if cultural differences affected blood pressure between ethnic groups. This would violate the second instrumental variable assumption— the independence assumption. (C) An example of horizontal pleiotropy, in which the genetic variants associated with alcohol consumption also affect tobacco consumption (violating the third assumption— the exclusion restriction assumption). (D) An example of vertical pleiotropy, in which the effect of ALDH2 on coronary heart disease is mediated by blood pressure. This example does not violate the Mendelian randomisation assumptions and does not cause bias.

Fig 2

Example of genetic pleiotropy in Mendelian randomisation: HDL cholesterol and risk of heart disease. Variants associated with HDL cholesterol are likely to have pleiotropic effects on risk of heart disease because they also associate with LDL cholesterol and triglycerides. Thus the inverse variance weighted Mendelian randomisation estimate, which assumes no pleiotropy, provides (biased) evidence of a protective role for HDL cholesterol in coronary heart disease. But the estimates using MR Egger, weighted median, and weighted mode, which allow for genetic pleiotropy, are attenuated towards the null. The MR Egger estimator assumes that for the variants with pleiotropic effects on coronary heart disease the magnitude of these effects do not correlate with the magnitude of the variants’ effects on HDL cholesterol. These results suggest that the inverse variance weighted estimate is driven by genetic pleiotropy and that HDL cholesterol is unlikely to have a major causal role in the development of coronary heart disease. CHD=coronary heart disease; ERFC=Emerging Risk Factors Collaboration; HDL-C=high density lipoprotein cholesterol; SD=standard deviation

Fig 3

Example associations between risk factors and outcomes from traditional observational epidemiology and Mendelian randomisation instrumental variable estimates. For some associations—such as vitamin D and mortality—the Mendelian randomisation results potentially confirm some causal relation. For other associations—CRP and heart disease—the Mendelian randomisation results are consistent with there being no causal effect. BMI=body mass index; CRP=C reactive protein; HDL-C=high density lipoprotein cholesterol; LDL-C=low density lipoprotein cholesterol

Fig 4

A hierarchy of observational and experimental data. Mendelian randomisation studies sit at the interface of experimental and observational studies. Their findings can be used to provide more reliable evidence to guide interventional research and provide information about potential public health interventions when a randomised controlled trial may not be feasible. Although we adapt the conventional pyramid of evidence for presentation purposes, we consider that triangulation of findings from different study designs should be used.