Understanding the assumptions underlying Mendelian randomization

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.

Automated summary

With the availability of large genetic data sets, Mendelian Randomization (MR) has become popular as a secondary analysis method. Using genetic variants as instrumental variables, MR can estimate causal effects between phenotypes when experimental research is not feasible. However, strong assumptions are required, and not meeting these assumptions can lead to biased results. Therefore, understanding and evaluating these assumptions is crucial when using MR.