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 does not 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.
Funding Statement
The research of Jun Shao was partially supported by the National Natural Science Foundation of China Grant 11831008 and the U.S. National Science Foundation Grant DMS-1914411. The research of Hyunseung Kang was supported in part by the U.S. National Science Foundation Grant DMS-1811414.
Acknowledgments
The authors would like to thank the anonymous referees, an Associate Editor and the Editor for their constructive comments that led to a much improved paper.
Citation
Ting Ye. Jun Shao. Hyunseung Kang. "Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization." Ann. Statist. 49 (4) 2079 - 2100, August 2021. https://doi.org/10.1214/20-AOS2027
Information