Human Genetics

, Volume 123, Issue 1, pp 15–33 | Cite as

Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?

Review

Abstract

Establishing causal relationships between environmental exposures and common diseases is beset with problems of unresolved confounding, reverse causation and selection bias that may result in spurious inferences. Mendelian randomization, in which a functional genetic variant acts as a proxy for an environmental exposure, provides a means of overcoming these problems as the inheritance of genetic variants is independent of—that is randomized with respect to—the inheritance of other traits, according to Mendel’s law of independent assortment. Examples drawn from exposures and outcomes as diverse as milk and osteoporosis, alcohol and coronary heart disease, sheep dip and farm workers’ compensation neurosis, folate and neural tube defects are used to illustrate the applications of Mendelian randomization approaches in assessing potential environmental causes of disease. As with all genetic epidemiology studies there are problems associated with the need for large sample sizes, the non-replication of findings, and the lack of relevant functional genetic variants. In addition to these problems, Mendelian randomization findings may be confounded by other genetic variants in linkage disequilibrium with the variant under study, or by population stratification. Furthermore, pleiotropy of effect of a genetic variant may result in null associations, as may canalisation of genetic effects. If correctly conducted and carefully interpreted, Mendelian randomization studies can provide useful evidence to support or reject causal hypotheses linking environmental exposures to common diseases.

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Noncommunicable Diseases Epidemiology Unit, Department of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
  2. 2.MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social MedicineUniversity of BristolBristolUK

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