Mendelian randomization: prospects, potentials, and limitations.

Mendelian randomization is the term applied to the random assortment of alleles at the time of gamete formation. This results in population distributions of genetic variants that are generally independent of behavioural and environmental factors that typically confound epidemiological associations between putative risk factors and disease. In some circumstances this can provide a study design akin to randomized comparisons. The principles of Mendelian randomization can serve to limit several potential problems in observational epidemiology (Table 1). The avoidance of confounding is clearly a key advantage, and in view of this, Martin Tobin and colleagues6 have suggested that the approach should be termed ‘Mendelian deconfounding’. However, there are several additional and perhaps equally important ways in which Mendelian randomization can strengthen inferences drawn from observational studies. In the example Katan originally presented—that of the association between low serum cholesterol levels and cancer—the most plausible bias would be introduced by reverse causation. The early stages of cancer could lead to a decrease in circulating cholesterol levels, generating an inverse association between cholesterol levels and cancer morbidity or mortality.1 Early stages of cancer will not, however, change inherited genetic variants that are associated with cholesterol levels. Thus if low cholesterol level were a cause of increased cancer risk then individuals with genetic variants associated with lower cholesterol levels should have a higher cancer risk. If, on the other hand, reverse causation is responsible for the association between cholesterol level and cancer, there should be no association between genetic variants related to cholesterol level and cancer risk. Biological forms of reverse causation may influence many epidemiological associations—for example, those between markers of inflammation and coronary heart disease, where existing atherosclerosis may influence the level of factors such as fibrinogen and C-reactive protein.13 Reverse causation can also occur through exposure assignment—for example, people with early stages of coronary heart disease may take vitamin supplements because they believe these will reduce their risk of cardiovascular events. This will tend to generate a positive association between vitamin intake and disease. A form of reverse causation can also occur through reporting bias, with the presence of disease influencing reporting disposition. In casecontrol studies people with the disease under investigation may report on their prior exposure history in a different way than do controls—perhaps because the former will think harder about potential reasons that account for why they have developed the disease. In this situation the association between genetic variants related to the exposure and disease outcome will not usually be biased. In observational studies associations between an exposure and disease will generally be biased if there is selection according to an exposure–disease combination in case-control studies, or according to an exposure–disease risk combination in prospective studies. If, for example, people with an exposure and at low risk of disease for other reasons were differentially excluded from a study the exposure would appear to be positively related to disease outcome, even if there were no such association in the underlying population. This is a form of ‘Berkson’s bias’, well known to epidemiologists.14 A possible example of such associative selection bias relates to the finding in the large American Cancer Society volunteer cohort that high alcohol consumption was associated with a reduced risk of stroke.15 This is somewhat counter-intuitive as the outcome category included haemorrhagic stroke (for which there is no obvious mechanism through which alcohol would reduce risk) and because alcohol is known to increase blood pressure16,17— a major causal factor for stroke.18 Population-based studies have found that alcohol tends to increase stroke risk.19–21 Heavy drinkers who volunteer for a study known to be about the health effects of their lifestyle are likely to be very unrepresentative of all heavy drinkers in the population, in ways that render them to be at low risk of stroke. Moderate and non-drinkers