A0762
Title: A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes
Authors: Zhonghua Liu - Columbia University (United States) [presenting]
Abstract: Mendelian randomization utilizes genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure variable on an outcome of interest, even in the presence of unmeasured confounders. However, the popular inverse-variance weighted (IVW) estimator could be biased in the presence of weak IVs, a common challenge in MR studies. A novel penalized inverse-variance weighted (pIVW) estimator, which adjusts the original IVW estimator, is developed to account for the weak IV issue by using a penalization approach to prevent the denominator of the pIVW estimator from being close to zero. Moreover, the variance estimation of the pIVW estimator is adjusted to account for the presence of balanced horizontal pleiotropy. It is shown that the recently proposed debiased IVW (dIVW) estimator is a special case of the proposed pIVW estimator. Further, it is proved that the pIVW estimator has a smaller bias and variance than the dIVW estimator under some regularity conditions. Extensive simulation studies and real data analysis are also conducted to demonstrate the performance of the proposed pIVW estimator.
