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

• Activity Number: 29 - Biometrics Section Byar Award Student Paper Session I
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Biometrics Section
• Author(s): Ting Ye* and Jun Shao and Hyunseung Kang
• Companies: University of Pennsylvania and University of Wisconsin-Madison and University of Wisconsin-Madison
• Keywords: Causal inference; Inverse variance weighted estimator; Many weak instruments; Mendelian randomization; Summary data

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 full theoretical characterization of the statistical properties of a popular estimator in MR, the inverse-variance weighted (IVW) estimator, 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 pre-screening. 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.