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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
Abstract #309732
Title: Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization
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

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.

Authors who are presenting talks have a * after their name.

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