Abstract Number:
2791
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Ruoyu Wang (1), Haoyu Zhang (2), Xihong Lin (1)
Institutions:
(1) Harvard T.H. Chan School of Public Health, Boston, (2) National Cancer Institute, Rockville
Co-Author(s):
Xihong Lin
Harvard T.H. Chan School of Public Health
First Author:
Ruoyu Wang
Harvard T.H. Chan School of Public Health
Presenting Author:
Abstract Text:
Mendelian randomization (MR) is a powerful tool for uncovering the causal effects in the presence of unobserved confounding. It utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to estimate the causal effect. However, SNPs often have small effects on complex traits, leading to bias and low statistical efficiency in MR analysis. The strong linkage disequilibrium among SNPs is compounding this issue, which poses additional statistical hurdles. To address these challenges, this paper proposes DEEM (Debiased Estimating Equation Method), a summary statistics-based MR approach that can incorporate numerous correlated SNPs with weak effects. DEEM effectively eliminates the weak IV bias, adequately accounts for the correlations among SNPs, and enhances efficiency by leveraging information from correlated weak IVs. DEEM is a versatile method that allows adjustment for pleiotropic effects and applies to both two-sample and one-sample MR analyses. We establish the consistency and asymptotic normality of the resulting estimator. Extensive simulations and two real data examples demonstrate that DEEM can improve the efficiency and robustness of MR analysis.
Keywords:
Causal inference|Estimating equation|Genome-wide association studies|Pleiotropic effects|Unmeasured confounder| Weak instruments
Sponsors:
Biometrics Section
Tracks:
Miscellaneous
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