AI 中文总结
针对两样本孟德尔随机化汇总数据，提出MR-ALasso和MR-ALasso-B两种方法。前者引入自适应惩罚权重改进工具变量识别，后者结合自适应lasso选择与自助平滑提升选择后推断效果，通过理论分析、模拟研究和实际应用验证了方法的有效性。
AI 中文摘要
两样本孟德尔随机化利用遗传变异作为工具变量，通过汇总关联统计量从观测数据中估计因果效应。然而，水平多效性会使标准估计器无效并导致因果推断有偏差。已提出多种方法，如MR-Lasso，但它可能无法一致识别无效工具变量，其选择后推断也不可靠。本文针对汇总数据开发了两种lasso型方法。MR-ALasso通过引入多效性效应的自适应惩罚权重扩展了MR-Lasso，以改进有效和无效工具变量的识别。MR-ALasso-B将自适应lasso选择与自助平滑相结合以改进选择后推断。理论结果表明MR-ALasso在识别无效工具变量和选择后行为方面有优势。模拟研究显示MR-ALasso在估计准确性和识别无效工具变量方面优于MR-Lasso，MR-ALasso-B在覆盖率和I型错误控制方面表现更好。实际数据应用也说明了这些方法的实用性。我们提供了R包MRAlasso以方便实现。
英文摘要
Two-sample Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate causal effects from observational data using summary association statistics. However, horizontal pleiotropy can invalidate standard MR estimators and lead to biased causal inference. Pleiotropy-robust methods have been proposed to address this issue, including regularization-based approaches such as MR-Lasso. However, MR-Lasso may fail to identify invalid instruments consistently, and its post-selection inference can be unreliable. In this paper, we develop two lasso-type procedures for two-sample MR with summary-level data. The first, MR-ALasso, extends MR-Lasso by introducing adaptive penalty weights for pleiotropic effects in order to improve the identification of valid and invalid instruments. The second, MR-ALasso-B, combines adaptive lasso selection with bootstrap smoothing to improve post-selection inference. We establish theoretical results for MR-ALasso under the two-sample summary data framework, including invalid instrument identification consistency and oracle-type post-selection behavior. Simulation studies show that MR-ALasso generally improves upon MR-Lasso in estimation accuracy and invalid-instrument identification, whereas MR-ALasso-B substantially improves coverage and type-I error control relative to naive post-selection inference. A real-data application based on bidirectional analyses of multiple complex traits further illustrates the practical usefulness of the proposed methods. We provide an R package, MRAlasso, to facilitate implementation.
Comments50 pages, 4 Figures
