A LATENT MIXTURE MODEL FOR HETEROGENEOUS CAUSAL MECHANISMS IN MENDELIAN RANDOMIZATION

• 成果类型: Article
• 署名作者: Long, Daniel; Zho, Qingyuan; Chen, Yang
• 署名单位: University of Michigan System; University of Michigan; University of Cambridge
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1816
• 发表日期: 2024
• 页码: 966-990
• 关键词: genome-wide association; BODY-MASS INDEX; inference; loci

摘要

Mendelian randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that identify a common causal effect. There is a general lack of awareness that this effect homogeneity assumption can be violated when there are multiple causal pathways involved, even if all the instrumental variables are valid. In this article we introduce a latent mixture model MR -Path that groups instruments that yield similar causal effect estimates together. We develop a Monte Carlo EM algorithm to fit this mixture model, derive approximate confidence intervals for uncertainty quantification, and adopt a modified Bayesian Information Criterion (BIC) for model selection. We verify the efficacy of the Monte Carlo EM algorithm, confidence intervals, and model selection criterion using numerical simulations. We identify potential mechanistic heterogeneity when applying our method to estimate the effect of high -density lipoprotein cholesterol on coronary heart disease and the effect of adiposity on type II diabetes.