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^[1] and subsequently described by Gray and Wheatley^[2] 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.^[3]

Motivation[]

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.^{[citation needed]}

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 affect health.^[4] Another example is some observational studies found an association between habitual coffee consumption and improved cardiovascular health, from which some inferred that coffee consumption has cardiovascular health benefits. However, it has since been suggested that the true causality is reversed, and researchers using the Mendelian randomization technique found statistical evidence that people do tend to reduce their coffee consumption in response to their own blood pressure levels and/or heart rate.^[5]

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.

Randomization approach[]

“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^[6]

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^[1]). Importantly, the genotype must only affect the disease status indirectly via its effect on the exposure of interest.^[7]

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.^{[citation needed]}

Statistical analysis[]

From a statistical perspective, Mendelian randomization (MR) is an application of the technique of instrumental variables^[8][9] 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.^[10]

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 relations between the instrumental variable and any possible confounding variables. In addition to being misled by direct effects of the instrument on the disease, the analyst may also be misled by linkage disequilibrium with unmeasured directly-causal variants, genetic heterogeneity, pleiotropy (often detected as a genetic correlation), or population stratification.^[11] Mendelian randomization is widely used in analyzing data of the large-scale Genome-wide association study, which usually adopts a case-control design. The conventional assumptions for instrumental variables under a case-control design are instead made in the population of controls.^[12] Ignoring the ascertainment bias of a case-control study when performing a Mendelian randomization can lead to considerable bias in the estimation of causal effects.^[12]

History[]

The basics of MR were invented by Martijn B. Katan in 1986, when he suggested the use of apolipoprotein E alleles, that had known effects on blood cholesterol levels, to study the causality between blood cholesterol and cancer.^[1][3] However, MR is based on instrumental variables of econometrics, which were already invented in 1928 by Philip Green Wright and Sewall Wright.^[13] The term "Mendelian randomization" was first used by Richard Gray and Keith Wheatley in 1991.^[2][14] It comes from the name of Gregor Mendel and the fact that alleles are distributed randomly in people at fertilisation.^[14] MR studies have become more common between 2007–2010 due to coincidental progress of omics-type of genetic research, which has provided lots of previously unknown connections between alleles and modifiable exposures.^[15]

References[]

Further reading[]

• Davey Smith G, Ebrahim S, Lewis S, Hansell AL, Palmer LJ, Burton PR (2005). "Genetic epidemiology and public health: hope, hype, and future prospects". Lancet. 366 (9495): 1484–98. doi:10.1016/S0140-6736(05)67601-5. PMID 16243094. S2CID 38483453.
• Davey Smith G, Ebrahim S (May 2005). "What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?". BMJ. 330 (7499): 1076–9. doi:10.1136/bmj.330.7499.1076. PMC 557238. PMID 15879400.
• Maier RM, Visscher PM, Robinson MR, Wray NR (August 2017). "Embracing polygenicity: a review of methods and tools for psychiatric genetics research". Psychological Medicine. 48 (7): 1055–1067. doi:10.1017/S0033291717002318. PMC 6088780. PMID 28847336.

External links[]

• Making sense of Mendelian randomisation and its use in health research
• G. Davey Smith (2006). Capitalising on Mendelian randomization to assess the effects of treatments. James Lind Library.