论文标题
GWAS摘要数据测试双向因果效应的重点框架
A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data
论文作者
论文摘要
孟德尔随机化（MR）是一种强大的方法，它使用遗传变异作为仪器变量（IVS）来推断可修改暴露对结果的因果关系。尽管近年来已经看到许多基本MR方法对某些假设的违规行为进行了鲁棒性，但很少有人提出推断双向因果关系的方法，尤其是对于具有有限生物学理解的表型。水平多效性的存在增加了另一层复杂性。在本文中，我们表明，在双向关系的存在中，常见MR方法的假设通常是不可能或太严格的。然后，我们提出了一个新的聚焦框架，用于测试两个可能是多效性遗传变异的特征之间的双向因果关系。我们的建议可以与许多最先进的MR方法相结合。我们提供有关I型错误和提议方法的功能的理论保证。我们使用几个模拟和真实数据集证明了所提出方法的鲁棒性。
Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR methods to be robust to certain violations of assumptions, few methods were proposed to infer bi-directional causal relationships, especially for phenotypes with limited biological understandings. The presence of horizontal pleiotropy adds another layer of complexity. In this article, we show that assumptions for common MR methods are often impossible or too stringent in the existence of bi-directional relationships. We then propose a new focusing framework for testing bi-directional causal effects between two traits with possibly pleiotropic genetic variants. Our proposal can be coupled with many state-of-art MR methods. We provide theoretical guarantees on the Type I error and power of the proposed methods. We demonstrate the robustness of the proposed methods using several simulated and real datasets.
