Robust Mendelian Randomization Estimation using Weighted Quantile Regression
基于加权分位数回归的稳健孟德尔随机化估计AI 解读 ↓
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基于加权分位数回归的稳健孟德尔随机化估计
- 研究问题
- 遗传工具变量同时存在相关及不相关多效性时，如何估计暴露对结局的因果效应，尤其应对大量具有较弱多效性的无效工具？
- 方法与思路
- 作者提出 MR-Quantile，以加权分位数回归估计比值关系，并通过非对称 Laplace 分布的似然表述选择用于估计的分位点。
- 材料陈述的贡献
- 摘要称，该方法对两类多效性均具有稳健性，模拟展示其经验表现，并用两样本汇总数据分析静息心率对房颤的因果效应。
- 阅读关注点
- 应核对稳健性所需的无效工具结构、分位点选择准则、弱工具影响，以及两样本分析中的样本重叠和遗传相关处理。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核验。摘要未说明稳健性边界或实证效应数值；采用分位数回归不意味着识别结局分位数效应。
原文摘要
In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships between exposures and outcomes based on observational data. However, numerous genetic studies have shown the pervasive pleiotropy of genetic variants, meaning that many, if not most, variants are associated with multiple traits, potentially violating the core assumptions of IV estimation. Uncorrelated pleiotropy occurs when genetic variants have a direct effect on the outcome that is not mediated by the exposure, while correlated pleiotropy occurs when genetic variants affect the exposure and outcome via shared heritable confounders. In this work, we propose a novel MR method, called MR-Quantile, based on weighted quantile regression (WQR) that is robust to both correlated and uncorrelated pleiotropy. We propose a procedure for selecting the optimal quantile of the ratio estimates through a likelihood-based formulation of WQR using the asymmetric Laplace distribution. Monte Carlo simulations demonstrate the empirical performance of the proposed method, especially in settings with many invalid IVs with weak pleiotropic effects. Finally, we apply our method to study the causal effect of resting heart rate on atrial fibrillation. Genetic variants associated with heart rate were identified in a genome-wide association study of 425,748 individuals from the VA Million Veteran Program, and used as instruments in a two-sample MR analysis with summary statistics from a genetic meta-analysis of 228,926 AF cases across eight studies.