Debiased Estimating Equation Method for Robust and Efficient Mendelian Randomization Using a Large Number of Correlated Weak and Invalid Instruments

TL;DR

This paper introduces DEEM, a new Mendelian randomization method that effectively uses many correlated, weak, and invalid SNPs to improve causal inference accuracy and efficiency, overcoming limitations of existing approaches.

Contribution

The paper proposes DEEM, a novel summary statistics-based MR method that incorporates correlated SNPs, corrects for weak instruments, and handles invalid instruments, enhancing robustness and efficiency.

Findings

DEEM reduces bias from weak instruments and improves estimation efficiency.

Simulation studies show DEEM outperforms existing MR methods.

Real data applications confirm DEEM's robustness and accuracy.

Abstract

Mendelian randomization (MR) is a widely used tool for causal inference in the presence of unmeasured confounders, which uses single nucleotide polymorphisms (SNPs) as instrumental variables to estimate causal effects. However, SNPs often have weak effects on complex traits, leading to bias in existing MR analysis when weak instruments are included. In addition, existing MR methods often restrict analysis to independent SNPs via linkage disequilibrium clumping and result in a loss of efficiency in estimating the causal effect due to discarding correlated SNPs. To address these issues, we propose the Debiased Estimating Equation Method (DEEM), a summary statistics-based MR approach that can incorporate a large number of correlated, weak-effect, and invalid SNPs. DEEM effectively eliminates the weak instrument bias and improves the statistical efficiency of the causal effect estimation by leveraging information from a large number of correlated SNPs. DEEM also allows for pleiotropic effects, adjusts for the winner's curse, and applies to both two-sample and one-sample MR analyses. Asymptotic analyses of the DEEM estimator demonstrate its attractive theoretical properties. Through extensive simulations and two real data examples, we demonstrate that DEEM significantly improves the efficiency and robustness of MR analysis compared with existing methods.