Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization

TL;DR

This paper introduces a debiased inverse-variance weighted estimator for Mendelian randomization that is robust to many weak instruments, improving accuracy without requiring instrument screening, and demonstrates its effectiveness through simulations and real data.

Contribution

We propose a novel debiased IVW estimator for MR that handles many weak instruments without screening and extend it to address horizontal pleiotropy, with methods to enhance efficiency.

Findings

The debiased IVW estimator reduces bias in weak instrument settings.

Simulation studies show improved accuracy over traditional estimators.

Real data analysis confirms practical effectiveness.

Abstract

Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a relatively small proportion of variance in the exposure and there are many such variants, a setting known as many weak instruments. To this end, we provide a theoretical characterization of the statistical properties of two popular estimators in MR, the inverse-variance weighted (IVW) estimator and the IVW estimator with screened instruments using an independent selection dataset, under many weak instruments. We then propose a debiased IVW estimator, a simple modification of the IVW estimator, that is robust to many weak instruments and doesn't require screening. Additionally, we present two instrument selection methods to improve the efficiency of the new estimator when a selection dataset is available. An extension of the debiased IVW estimator to handle balanced horizontal pleiotropy is also discussed. We conclude by demonstrating our results in simulated and real datasets.