Comparison of Nonlinear Mendelian Randomization for Causal Inference

Music City Center 

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to infer causal effects between an exposure and an outcome based on observational data. While various MR methods have been proposed and applied in recent years, most rely on the assumption of a linear relationship between the exposure and outcome, though this relationship may actually be nonlinear. In this study, we compare several nonlinear IV regression approaches-such as spline-based models, polynomial regression, and deep learning techniques-alongside two stratification-based nonlinear MR methods: doubly-ranked stratification and residual stratification, for estimating localized average causal effects (LACE). These methods are evaluated for their accuracy, efficiency, and robustness in handling complex, nonlinear relationships between the exposure, instruments, and outcome. Our findings provide valuable insights into the performance of these methods, guiding the selection of the most appropriate approach for nonlinear causal inference in MR.

Causal effects

Genetic variants

Genome-Wide Association Studies (GWAS)

Transcriptome-Wide Association Studies (TWAS) 

Section on Statistics in Genomics and Genetics