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As a tool for the epidemiologist and a translational extension for the geneticist, Mendelian randomization (MR) has been exemplary and has been able to unify otherwise disconnected research specialties and show the genuine utility of genetic association studies. With this, the demand for and development of MR have of course brought with them a smoothing or facilitation of existing analytical process. What was once the reserved analytical space of a limited number of experts is now a burgeoning, semi-automated, plug-and-play approach to causal inference. This of course is to be praised in that applied methods are being used and accessed by many researchers and, with this, pertinent questions are being addressed. However, what is also true is that as we learn more about the source of genetic association signals and the complicating effects that our own favourite complex phenotypes might impart, the more we need to advise caution in inference. This does not mean that we should remove applied genetic epidemiology from the quiver of the analyst, rather that in the era of proliferation of genetic analysis we should remain critical and not allow the success of MR to become its own worst enemy. Into this context Swerdlow et al. have delivered a much needed examination of the factors which need to be considered when embarking on an MR study. Their paper ‘Selecting genetic tools for Mendelian randomization in the wake of genome-wide association studies’ discusses a series of important considerations when forming an MR study if working from the now substantial pools of genome-wide association study (GWAS) results that are available for causal pathway breakdown. Until relatively recently, MR studies have generally focused on a limited number of intermediate phenotypes. Recent applications of ‘omic’ technologies into large-scale population-based studies present new opportunities for identifying predictive biomarkers and causal links between established phenotypes and disease outcomes. Whereas there is no guarantee that use of multi-omic phenotype data will avoid any of the problems encountered in observational epidemiology, in combination with MR approaches there is an opportunity to undertake informative analyses which exploit genetically tractable intermediate phenotypes/biomarkers and to potentially identify novel predictive biomarkers and causal links between established phenotypes and disease outcomes. Swerdlow et al. focus on important factors when selecting genetic variants to act as proxy measures for exposures or intermediate phenotypes of interest. In contrast to direct measurement, germline genotypes reliably associated with risk factors can act in this role and offer several advantages: genotypes are relatively easy to measure, they are stable through time and between tissues (though their effects may not be), they are largely immutable and are not correlated with confounding factors as a result of the mechanisms of Mendelian inheritance. Important issues considered when choosing genetic variation to employ in MR studies include the nature of the original GWAS signal (including the frequency and effect size of the relevant mutation) and the specificity of the genetic effect which is being exploited to yield a causal estimate. The former of these is of course acknowledged as a function of the genetic architecture of any given exposure or intermediate phenotype; however, the authors appropriately note that nonspecificity of genetic effect can be derived from a number of sources including horizontal pleiotropy, biological complexity and complex pathway or regulatory systems. In light of this, a guiding set of rules or advice is provided in efforts simultaneously both to make the potential MR analyst aware of the issues in question and also to offer support in the undertaking of this type of applied analysis. The one area which remains important and perhaps should be highlighted even more is the potentially International Journal of Epidemiology, 2016, Vol. 45, No. 5 1617

Commentary: One size fits all: are there standard rules for the use of genetic instruments in Mendelian randomization?