Abstract

For obvious ethical reasons, risk factors cannot be assessed in randomized controlled trials. Epidemiologists therefore usually identify risk factors using observational data. It is, however, difficult to establish causal relationships between risk factors and common complex diseases because observational studies are prone to spurious results due to confounding factors, reverse causation, and/or selection biases.1 Atherosclerosis is a common complex trait influenced by several cardiovascular risk factors that tend to cluster. In this context, determining whether a putative risk factor is causaly related to atherosclerosis, independently of all other risk factors (i.e. ceteris paribus ), is a challenging task. The concept of Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. By analogy with the fact that the random allocation of treatment in a randomized controlled trial renders confounding unlikely, a genetic variant of interest should not be associated with known and unknown confounding factors.2 Mendelian randomization has recently been proposed as a new tool to overcome some of the problems encountered in observational epidemiology, such as reducing residual confounding and protecting against reverse causation.3 In Mendelian randomization, a functional genetic variant, or a variant in strong linkage disequilibrium with it, is used to retrieve an unbiased estimate of the association of a modifiable exposure [e.g. C-reactive protein (CRP), fibrinogen, or body mass index (BMI)] with a disease (e.g. coronary heart disease, stroke, or atherosclerosis).1 As such, Mendelian randomization may prove a valuable tool to infer causality in cardiovascular observational epidemiology. However, it is not, and should not be viewed, as a panacea, and its limitations should be clearly acknowledged. The concept of Mendelian randomization is … *Corresponding author. Tel: +41 21 314 7254, Fax: +41 21 314 7373. Email: murielle.bochud{at}chuv.ch

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