Challenges in undertaking nonlinear Mendelian randomization

Abstract

Mendelian randomization (MR) is a widely used method that exploits the unique properties of germline genetic variation to strengthen causal inference in relationships between exposures and outcomes. Nonlinear MR allows estimation of the shape of these relationships. In a previous paper, the authors applied linear and nonlinear MR to estimate the effect of BMI on mortality in UK Biobank, providing evidence for a J-shaped association. However, it is now clear that there are problems with widely used nonlinear MR methods, which draws attention to the likely erroneous nature of the conclusions regarding the shapes of several explored relationships. Here, the authors explore the utility and likely biases of these nonlinear MR methods with the use of a negative control design. Although there remains good evidence for a causal effect of higher BMI increasing the risk of mortality, the pattern of this association across different levels of BMI requires further characterization.