Mendelian Randomization Methods for Causal Inference: Estimands, Identification and Inference
孟德尔随机化因果推断方法：估计对象、识别与推断AI 解读 ↓
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孟德尔随机化因果推断方法：估计对象、识别与推断
- 研究问题
- 如何围绕明确的因果估计对象选择孟德尔随机化方法，并处理无效或弱遗传工具、不同样本结构及研究设计带来的解释问题？
- 方法与思路
- 作者以教程综述形式组织方法，比较群体与家系、个体与汇总数据、单样本与双样本设计，并讨论工具诊断、修正策略及软件。
- 材料陈述的贡献
- 摘要称综述整合了估计对象、识别挑战、设计比较和近期软件进展，并以生物样本库及阿尔茨海默病蛋白组应用说明实践。
- 阅读关注点
- 应核对各设计对应的因果对象、遗传工具有效性和弱工具条件、诊断方法的局限，以及软件实现是否匹配所用数据结构。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。本文属于教程综述，摘要未提供具体方法的识别条件或性能结果，不能当作新估计量验证。
原文摘要
Mendelian randomization (MR) has become an essential tool for causal inference in biomedical and public health research. By using genetic variants as instrumental variables, MR helps address unmeasured confounding and reverse causation, offering a quasi-experimental framework to evaluate causal effects of modifiable exposures on health outcomes. Despite its promise, MR faces substantial methodological challenges, including invalid instruments, weak instrument bias, and design complexities across different data structures. In this tutorial review, we provide a comprehensive overview of MR methods for causal inference, emphasizing clarity of causal interpretation, study design comparisons, availability of software tools, and practical guidance for applied scientists. We organize the review around causal estimands, ensuring that analyses are anchored to well-defined causal questions. We discuss the problems of invalid and weak instruments, comparing available strategies for their detection and correction. We integrate discussions of population-based versus family-based MR designs, analyses based on individual-level versus summary-level data, and one-sample versus two-sample MR designs, highlighting their relative advantages and limitations. We also summarize recent methodological advances and software developments that extend MR to settings with many weak or invalid instruments and to modern high-dimensional omics data. Real-data applications, including UK Biobank and Alzheimer's disease proteomics studies, illustrate the use of these methods in practice. This review aims to serve as a tutorial-style reference for both methodologists and applied scientists.