A more robust approach to multivariable Mendelian randomization

• 成果类型: Article
• 署名作者: Wu, Yinxiang; Kang, Hyunseung; Ye, Ting
• 署名单位: University of Washington; University of Washington Seattle; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaf053
• 发表日期: 2025
• 关键词: instrumental variables regression; generalized-method; weak instruments; variants; rare

摘要

Multivariable Mendelian randomization uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, multivariable Mendelian randomization often faces greater challenges with many weak instruments, which can lead to bias not necessarily toward zero and inflation of Type-I errors. In this work, we introduce a new asymptotic regime that allows exposures to have varying degrees of instrument strength, providing a more accurate theoretical framework for studying multivariable Mendelian randomization estimators. Under this regime, our analysis of the widely used multivariable inverse-variance-weighted method shows that it is often biased and tends to produce misleadingly narrow confidence intervals in the presence of many weak instruments. To address this, we propose a simple, closed-form modification to the multivariable inverse-variance-weighted estimator to reduce bias from weak instruments, and additionally introduce a novel spectral regularization technique to improve finite-sample performance. We show that the resulting spectral-regularized estimator remains consistent and asymptotically normal under many weak instruments. Through simulations and real data applications, we demonstrate that our proposed estimator and asymptotic framework can enhance the robustness of multivariable Mendelian randomization analyses.