Abstract
Motivation
Mendelian randomization (MR) is widely used for causal inference using genetic data, yet most existing MR methods treat exposures as static and have limited capacity to analyze longitudinal measurements. In many biomedical studies, multiple exposures evolve over time, are strongly correlated, and share unobserved temporal structure. Ignoring these features can lead to unstable estimation and reduced power.Results
We propose factor-augmented multivariable functional Mendelian randomization (FA-MFMR) for estimating time-varying causal effects of multiple longitudinal exposures. FA-MFMR integrates genetic instruments with functional representations of exposure trajectories and a low-dimensional factor structure to capture shared temporal patterns. By separating common temporal variation from exposure-specific components, it mitigates multicollinearity, improves estimation stability, and accommodates sparse causal structures in which some exposures have no effect throughout the observation period. Simulations demonstrate lower estimation error and more efficient uncertainty quantification than competing approaches, particularly for highly correlated and sparsely observed longitudinal exposures. Applied to longitudinal biomarkers in a Parkinson’s disease cohort, FA-MFMR characterizes age-dependent patterns in the estimated trajectory-level coefficient functions while highlighting uncertainty in localized effects.Availability and implementation
The developed R package and code to reproduce all the results are available at https://github.com/YQHuFD/FA-MFMR.