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

2024-08-09

AI Summary

Mendelian randomization (MR) suffers from estimation bias and efficiency loss due to weak instruments, linkage disequilibrium (LD)-correlated SNPs, and horizontal pleiotropy. To address these challenges, we propose DEEM—a novel method that, for the first time within a summary-statistics framework, jointly models large numbers of correlated, weak, and potentially invalid SNPs. DEEM achieves this through debiased estimating equations, asymptotically linear inference, explicit modeling of pleiotropy, and correction for winner’s curse—thereby departing from conventional LD-pruning paradigms. Theoretically, DEEM is proven to be unbiased and asymptotically efficient, and it supports both one-sample and two-sample MR designs. Extensive simulations and real-data analyses demonstrate that, relative to state-of-the-art MR methods, DEEM improves estimation efficiency by 30–50%, reduces bias by over 90%, and substantially enhances robustness to violations of standard MR assumptions.

Technology Category

• Machine Learning: Ensemble Methods
• Reasoning under Uncertainty: Graphical Models
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Addresses bias from weak instruments in Mendelian randomization
• Incorporates correlated SNPs to improve estimation efficiency
• Allows for pleiotropic effects and adjusts winner's curse

Innovation

• Debiased Estimating Equation Method (DEEM) for MR
• Incorporates correlated, weak, and invalid SNPs
• Eliminates weak instrument bias efficiently