标题： 两样本汇总数据孟德尔随机化中的去偏逆方差加权估计量 显示英文标题

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

摘要： 孟德尔随机化（MR）已成为一种流行的方法，通过使用遗传变异作为工具变量来研究可改变的暴露对结果的影响。 MR中的一个挑战是，每个遗传变异在暴露中解释的方差比例相对较小，并且存在许多这样的变异，这种情形被称为多个弱工具变量。 为此，我们在多个弱工具变量的情况下，提供了对两种流行的MR估计量——逆方差加权（IVW）估计量和使用独立选择数据集筛选工具变量的IVW估计量——的统计特性的理论描述。 随后，我们提出了一种去偏的IVW估计量，这是IVW估计量的一个简单修改，对多个弱工具变量具有鲁棒性，并且不需要筛选。 此外，当有选择数据集可用时，我们提出了两种工具选择方法，以提高新估计量的效率。 还讨论了去偏IVW估计量在处理平衡水平多效性方面的扩展。 最后，我们通过模拟和真实数据集展示了我们的结果。 显示英文摘要

摘要： 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.