Mendelian randomization

In epidemiology, Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies. The design was first proposed in 1986 and subsequently described by Gray and Wheatley as a method for obtaining unbiased estimates of the effects of a putative causal variable without conducting a traditional randomised trial. These authors also coined the term Mendelian randomization. The design has a powerful control for reverse causation and confounding, which often impede or mislead epidemiological studies.“Genetics is indeed in a peculiarly favoured condition in that Providence has shielded the geneticist from many of the difficulties of a reliably controlled comparison. The different genotypes possible from the same mating have been beautifully randomised by the meiotic process. A more perfect control of conditions is scarcely possible, than that of different genotypes appearing in the same litter.” — R.A. Fisher In epidemiology, Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies. The design was first proposed in 1986 and subsequently described by Gray and Wheatley as a method for obtaining unbiased estimates of the effects of a putative causal variable without conducting a traditional randomised trial. These authors also coined the term Mendelian randomization. The design has a powerful control for reverse causation and confounding, which often impede or mislead epidemiological studies. An important focus of observational epidemiology is to identify modifiable causes of diseases of public health concern. In order to have firm evidence that some prospective intervention will have the desired beneficial effect on public health, the association observed between the particular risk factor and disease must imply that the risk factor either aggravates or actually causes the disease. Well-known successes include the identified causal links between smoking and lung cancer, and between blood pressure and stroke. However, there have also been notable failures when identified exposures were later shown by randomised controlled trials to be non-causal. For instance, it was previously thought that hormone replacement would prevent cardiovascular disease, but it is now known to have no such benefit and may even adversely effect health. Spurious findings in observational epidemiology are most likely caused by social, behavioural, or physiological confounding factors, which are particularly difficult to measure accurately and difficult to control for. Moreover, many epidemiological findings cannot be ethically replicated in clinical trials. Mendelian randomization (MR) is a method that allows one to test for, or in certain cases to estimate, a causal effect from observational data in the presence of confounding factors. It uses common genetic polymorphisms with well-understood effects on exposure patterns (e.g., propensity to drink alcohol) or effects that mimic those produced by modifiable exposures (e.g., raised blood cholesterol). Importantly, the genotype must only affect the disease status indirectly via its effect on the exposure of interest. Because genotypes are assigned randomly when passed from parents to offspring during meiosis, if we assume that mate choice is not associated with genotype (panmixia), then the population genotype distribution should be unrelated to the confounding factors that typically plague observational epidemiology studies. In this regard, Mendelian randomization can be thought of as a “naturally” randomized controlled trial. Because the polymorphism is the instrument, Mendelian randomization is dependent on prior genetic association studies having provided good candidate genes for response to risk exposure. From a statistical perspective, Mendelian randomization (MR) is an application of the technique of instrumental variables with genotype acting as an instrument for the exposure of interest. The method has also been used in economic research studying the effects of obesity on earnings, and other labor market outcomes. Accuracy of MR depends on a number of assumptions: That there is no direct relationship between the instrumental variable and the dependent variables, and that there are no direct relation between the instrumental variable and any possible confounding variables. In addition to being mislead by direct effects of the instrument on the disease, the analyst may also be mislead by linkage disequilibrium with unmeasured directly-causal variants, genetic heterogeneity, pleiotropy (often detected as a genetic correlation), or population stratification.