A More Credible Approach to Multivariable Mendelian Randomization
Abstract Number:
992
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yinxiang Wu (1), Hyunseung Kang (2), Ting Ye (1)
Institutions:
(1) University of Washington, N/A, (2) University of Wisconsin-Madison, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable MR, MVMR 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 different degrees of instrument strength, providing a more credible theoretical framework for studying MVMR estimators. 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 spectral regularized estimator and show that the estimator is consistent and asymptotically normal under many weak instruments. We demonstrate through simulations and real applications that our proposed estimator would bring more credibility to MVMR analysis.
Keywords:
Causal inference|genetic variation|GWAS|instrumental variable|weak instruments|
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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