FA-MFMR: a multivariable functional Mendelian randomization method that accounts for correlated longitudinal exposures via factor augmentation.
- 1State Key Laboratory of Genetics and Development of Complex Phenotypes, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, Yangpu District 200438, China.
- 2Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, Yangpu District 200438, China.
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.
Related
- Multivariable Mendelian Randomization With Weak Instruments: A Comparison of Bayesian and Frequentist Methods.
- Longitudinal Subject Pairing in Cross-Sectional Neonatal Data Reveals Asynchronous Structural and Functional Brain Maturation
- NET-Associated Transcriptional Signatures and T-Cell Functional States During Allogeneic CAR-γδT-Cell Therapy: A Longitudinal Single-Cell Analysis
- Fast Generalized Longitudinal Functional Principal Components Analysis with Applications to Population-Level Neuronal Dynamics
- Edentulism and functional disability in older adults: A longitudinal and cross-national study in England, the United States, and India
BibTeX
@article{chen2026famfmr,
title = {{FA-MFMR: a multivariable functional Mendelian randomization method that accounts for correlated longitudinal exposures via factor augmentation.}},
author = {Dong Chen and Junrong Deng and Siyuan Shen and Yuyang Zhou and Hanyu Cui and Yu Qiu and Qiming Li and Yue-Qing Hu},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag725},
url = {https://europepmc.org/article/MED/42847660},
note = {CC BY},
}
RIS
TY - JOUR
TI - FA-MFMR: a multivariable functional Mendelian randomization method that accounts for correlated longitudinal exposures via factor augmentation.
AU - Dong Chen
AU - Junrong Deng
AU - Siyuan Shen
AU - Yuyang Zhou
AU - Hanyu Cui
AU - Yu Qiu
AU - Qiming Li
AU - Yue-Qing Hu
PY - 2026
DA - 2026/10/01
JO - Bioinformatics (Oxford, England)
DO - 10.1093/bioinformatics/btag725
UR - https://europepmc.org/article/MED/42847660
AB - 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.
SN - 1367-4803
SN - 1367-4811
ER -