INTEGRATING MENDELIAN RANDOMIZATION WITH CAUSAL MEDIATION ANALYSES FOR CHARACTERIZING DIRECT AND INDIRECT EXPOSURE-TO-OUTCOME EFFECTS

• Published in: The annals of applied statistics Vol. 18; no. 3; p. 2656
• Main Authors: Yang, Fan, Chen, Lin S, Oveisgharan, Shahram, Darbar, Dawood, Bennett, David A
• Format: Journal Article
• Language: English
• Published: United States 01.09.2024
• Subjects: causal mediation analysis; Mendelian Randomization; integrative analysis
• ISSN: 1932-6157
• DOI: 10.1214/24-aoas1901

Mendelian randomization (MR) assesses the total effect of exposure on outcome. With the rapidly increasing availability of summary statistics from genome-wide association studies (GWASs), MR leverages existing summary statistics and is widely used to study the causal effects among complex traits and diseases. The total effect in the population is a sum of indirect and direct effects. For complex disease outcomes with complicated etiologies, and/or for modifiable exposure traits, there may exist more than one pathway between exposure and outcome. The direct effect and the indirect effect via a mediator of interest could be of opposite directions, and the total effect estimates may not be informative for treatment and prevention decision-making or may be even misleading for different subgroups of patients. Causal mediation analysis delineates the indirect effect of exposure on outcome operating through the mediator and the direct effect transmitted through other mechanisms. However, causal mediation analysis often requires individual-level data measured on exposure, outcome, mediator and confounding variables, and the power of the mediation analysis is restricted by sample size. In this work, motivated by a study of the effects of atrial fibrillation (AF) on Alzheimer's dementia, we propose a framework for Integrative Mendelian randomization and Mediation Analysis (IMMA). The proposed method integrates the total effect estimates from MR analyses based on large-scale GWASs with the direct and indirect effect estimates from mediation analysis based on individual-level data of a limited sample size. We introduce a series of IMMA models, under the scenarios with or without exposure-mediator interaction and/or study heterogeneity. The proposed IMMA models improve the estimation and the power of inference on the direct and indirect effects in the population, as well as the characterization of the variation of effects. Our analyses showed a significant positive direct effect of AF on Alzheimer's dementia risk not through the use of the oral anticoagulant treatment and a significant indirect effect of AF-induced anticoagulant treatment in reducing Alzheimer's dementia risk. The results suggested potential Alzheimer's dementia risk prediction and prevention strategies for AF patients, and paved the way for future re-evaluation of anticoagulant treatment guidelines for AF patients. A sensitivity analysis was conducted to assess the sensitivity of the conclusions to a key assumption of the IMMA approach.