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.

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]

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[5]

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.[6]

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.

Statistical analysis

From a statistical perspective, Mendelian randomization (MR) is an application of the technique of instrumental variables[7][8] 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.[9]

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.[10]

References
  1. 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. Davey Smith, G. (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, R.A. (April 2010). "Statistical methods in genetics 1951". International Journal of Epidemiology. 39 (2): 329–335. doi:10.1093/ije/dyp379. PMID 20176585.
  6. Holmes, Michael V.; Ala-Korpela, Mika; Davey Smith, George (October 2017). "Mendelian randomization in cardiometabolic disease: Challenges in evaluating causality". Nature Reviews Cardiology. 14 (10): 577–590. doi:10.1038/nrcardio.2017.78. ISSN 1759-5010. PMC 5600813. PMID 28569269.
  7. 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.
  8. 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.
  9. 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. PMC 6585973. PMID 30240095.
  10. Davey Smith, G.; 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.

Further reading
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