On the use of Mendelian randomization to infer causality in observational epidemiology

Extract

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.

What is Mendelian randomization?

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 an application of the theory of instrumental variables.^2,4,5 Instrumental variables are used to make causal inference in non-experimental conditions and have been widely explored by econometricians.² As illustrated in Figure 1 of the paper by Kivimäki et al.,⁶ an instrumental variable (e.g. rs9939609 FTO variant) is a variable that is associated with the outcome (e.g. atherosclerosis) only through its association with the exposure of interest (e.g. lifetime BMI). The reader should keep in mind that the use of instrumental variables in statistical genetics is still in its infancy and that more theoretical work is needed in this context. An important limitation in statistical genetics is the weakness of the instrument, i.e. the genetic variant is only weakly correlated with the exposure of interest.