A Two-Sample Robust Bayesian Mendelian Randomization Method Accounting for Linkage Disequilibrium and Idiosyncratic Pleiotropy with Applications to the COVID-19 Outcome
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兼顾连锁不平衡与特异性多效性的两样本稳健贝叶斯孟德尔随机化
- 研究问题
- 两样本孟德尔随机化中，遗传变异相关及重尾直接效应可能导致偏差或功效损失，如何在统一模型中处理这两类问题？
- 方法与思路
- 作者显式纳入连锁不平衡结构，以多变量广义 t 分布建模直接效应，利用高斯尺度混合和 EM 类算法估计并校准标准误。
- 材料陈述的贡献
- 摘要称，模型在模拟和基准数据中表现稳健，并报告冠心病与重症新冠风险的关联，同时提供相应 R 软件包。
- 阅读关注点
- 应核对遗传工具变量与多效性假设、连锁不平衡信息来源，以及标准误校准、两样本设计和应用结论的因果解释条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明完整识别条件，所述疾病关联不能直接视为已验证因果结论。
原文摘要
Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample MR methods based on genome-wide association study summary level data, however, those methods could suffer from potential power loss or/and biased inference when the chosen genetic variants are in linkage disequilibrium (LD), and also have relatively large direct effects on the outcome whose distribution might be heavy-tailed which is commonly referred to as the idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more robust multivariate generalized t-distribution to model such direct effects in a probabilistic model framework which can also incorporate the LD structure explicitly. The generalized t-distribution can be represented as a Gaussian scaled mixture so that our model parameters can be estimated by the EM-type algorithms. We compute the standard errors by calibrating the evidence lower bound using the likelihood ratio test. Through extensive simulation studies, we show that our RBMR has robust performance compared to other competing methods. We also apply our RBMR method to two benchmark data sets and find that RBMR has smaller bias and standard errors. Using our proposed RBMR method, we find that coronary artery disease is associated with increased risk of critically ill coronavirus disease 2019 (COVID-19). We also develop a user-friendly R package RBMR for public use.