Managing the exponential growth of Mendelian randomization studies

There has been a rapid growth in studies that simply use summary genome-wide association studies (GWAS) data to estimate the causal effect of X on Y. With the increasing risk of genetic confounding, these studies offer little more than a conventional observational study and are a low priority for publication unless they also use additional methods. This could include exploring complex exposures and/or outcomes (including testing for possible mediation via multivariable MR), incorporating negative controls and/or evidence from other study designs such as natural experiments and advancing plausible biological mechanisms.

Much of the research we publish relates to questions of cause and effect. In an ideal world, we would subject these questions to experimentation, randomizing study participants to different conditions. However, in many cases—particularly in the context of addiction—such randomization is simply not possible. We cannot randomize tobacco-naïve children to use e-cigarettes, for example, to determine whether or not vaping acts as a ‘gateway’ to subsequent smoking. In these cases, we have to rely on observational methods, and these suffer from well described problems of confounding, including reverse causality.