Reverse causation occurs when the outcome variable at an earlier timepoint, or a proximal precursor of the outcome (such as pre-clinical disease), has a causal effect on the risk factor. This confounding by pre-clinical or prior outcome can lead to bias when trying to estimate an average causal effect of the risk factor on the outcome. By proposing genetic variants as instrumental variables, Mendelian randomization can make inferences about the effect of shifting the trajectory of a risk factor on a later-measured outcome. As genetic variants are fixed at conception, it has been stated that Mendelian randomization investigations are not affected by reverse causation. While Mendelian randomization analyses have some protection against bias due to reverse causation, the claim of complete immunity is a misconception. We provide three plausible scenarios and accompanying examples of how reverse causation can influence Mendelian randomization estimates: 1) the effect of the genetic variant on the risk factor is mediated (at least in part) by the outcome, 2) the genetic association with the outcome is distorted by a feedback mechanism whereby the risk factor and outcome variable both influence each other over time, and 3) the genetic association with the outcome is distorted by an effect of the outcome in the parental generation on the outcome in the offspring generation. In these scenarios, we show that all causal effects typically estimated in Mendelian randomization analyses will be biased, though hypothesis testing can still be valid in Scenario 2 and 3 for a sharp causal null hypothesis. This underscores the general recommendation to view Mendelian randomization as primarily testing a causal null hypothesis rather than estimating a causal effect.