Mendelian randomization

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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]


An important focus of observational epidemiology is the identification of modifiable causes of common diseases that are of public health interest. In order to have firm evidence that a recommended public health intervention will have the desired beneficial effect, the observed association between the particular risk factor and disease must imply that the risk factor 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 (RCTs) to be non-causal. For instance, it has now been shown that hormone replacement therapy will not prevent cardiovascular disease, as was previously thought, and may have other adverse health effects.[4] The reason for such spurious findings in observational epidemiology is most likely to be confounding by social, behavioural or physiological factors which are difficult to control for and particularly difficult to measure accurately. Moreover, many findings cannot be replicated by RCTs for ethical reasons.

Randomization approach[edit]

"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[5]

Mendelian randomization 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.[citation needed] Because genotypes are assigned randomly when passed from parents to offspring during meiosis, if we assume that choice of mate is not associated with genotype (panmixia), then the population genotype distribution should be unrelated to the confounders that typically plague observational epidemiology studies. In this regard, Mendelian randomization can be thought of as a “natural” randomized controlled trial. Because the polymorphism is the instrument, Mendelian randomization is dependent on genetic association studies having provided good candidate genes for response to risk exposure.

From a statistical perspective, MR is an application of the technique of instrumental variables[6][7] with genotype acting as an instrument for the exposure of interest. The method has also been applied in economic research studying the effects of obesity on earnings and other labor market outcomes. [8]

MR is based on a number of assumptions. These include that there is no direct relationship between the instrument and the dependent variable, and that there are no direct paths between the instrument and any potential confounders. In addition to direct effects of the instrument on the disease misleading the analyst, misleading conclusions may also arise in the presence of linkage disequilibrium with unmeasured directly-causal variants, genetic heterogeneity, pleiotropy (often detected as a genetic correlation), or population stratification.[9]


  1. ^ a b Katan MB (March 1986). "Apolipoprotein E isoforms, serum cholesterol, and cancer". Lancet. 1 (8479): 507–8. doi:10.1016/s0140-6736(86)92972-7. PMID 2869248.
  2. ^ Gray R, Wheatley K (1991). "How to avoid bias when comparing bone marrow transplantation with chemotherapy". Bone Marrow Transplantation. 7 Suppl 3: 9–12. PMID 1855097.
  3. ^ Smith GD (September 2010). "Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene × Environment Interactions". Perspectives on Psychological Science. 5 (5): 527–45. doi:10.1177/1745691610383505. PMID 26162196.
  4. ^ Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, Jackson RD, Beresford SA, Howard BV, Johnson KC, Kotchen JM, Ockene J (July 2002). "Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women's Health Initiative randomized controlled trial". JAMA. 288 (3): 321–33. doi:10.1001/jama.288.3.321. PMID 12117397.
  5. ^ Fisher RA (April 2010). "Statistical methods in genetics. 1951". International Journal of Epidemiology. 39 (2): 329–35. doi:10.1093/ije/dyp379. PMID 20176585.
  6. ^ Thomas DC, Conti DV (February 2004). "Commentary: the concept of 'Mendelian Randomization'". International Journal of Epidemiology. 33 (1): 21–5. doi:10.1093/ije/dyh048. PMID 15075141.
  7. ^ Didelez V, Sheehan N (August 2007). "Mendelian randomization as an instrumental variable approach to causal inference". Statistical Methods in Medical Research. 16 (4): 309–30. doi:10.1177/0962280206077743. PMID 17715159.
  8. ^ Bockerman P, Cawley J, Viinikainen J, Lehtimaki T, Rovio S, Seppala I, Pehkonen J, Raitakari O (2019). "The effect of weight on labor market outcomes: An application of genetic instrumental variables". Health Economics. 28 (1): 65–77. doi:10.1002/hec.3828.
  9. ^ Smith GD, Ebrahim S (February 2003). "'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?". International Journal of Epidemiology. 32 (1): 1–22. doi:10.1093/ije/dyg070. PMID 12689998.

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