| 高雪,王慧,王彤.孟德尔随机化中多效性偏倚校正方法简介[J].Chinese journal of Epidemiology,2019,40(3):360-365 |
| 孟德尔随机化中多效性偏倚校正方法简介 |
| Review on correction methods related to the pleiotropic effect in Mendelian randomization |
| Received:September 18, 2018 |
| DOI：10.3760/cma.j.issn.0254-6450.2019.03.020 |
| KeyWord: 孟德尔随机化 多效性 无效工具变量 校正模型 |
| English Key Word: Mendelian randomization Pleiotropy Invalid instrumental variables Correction model |
| FundProject:国家自然科学基金（81872715） |
| Author Name | Affiliation | E-mail | | Gao Xue | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | | | Wang Hui | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | | | Wang Tong | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | tongwang@sxmu.edu.cn |
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| Abstract: |
| 孟德尔随机化以遗传变异作为工具变量，对感兴趣的暴露因素与结局的因果关联进行估计及评价。遗传变异作为有效工具变量需要满足强关联假设及无多效性假设。然而，由于遗传变异与表型性状间存在复杂的生物学效应，其作为工具变量的多效性往往无法避免。基于此，本文分别从工具变量筛选、无效工具变量检验、校正多效性的模型构建以及敏感性分析等方面介绍无效工具变量的多效性偏倚校正方法。在实际应用中，研究者应结合数据类型、样本含量、分析假设等多个方面选择合适的方法进行分析与推断，从而得到一致、稳健的因果效应估计量。 |
| English Abstract: |
| Mendelian randomization is an approach using the genetic variants as instrumental variable to estimate and assess the casual relationship between exposure of interest and outcomes. As a valid instrument, genetic variants have to meet the assumptions of strong correlation with exposure but without pleiotropic effect with the outcomes. However, pleiotropy of the variants is usually inevitable, owing to the existence of complex biological effects. Thus, correction methods related to pleiotropic bias are introduced in this paper regarding the selection of instrumental variables, testing of invalid instrumental variables, construction of pleiotropic effect correction models and sensitivity analysis of the robust results. For practical application, investigators should take consideration on the following areas including the types of data, sample size and other relative aspects, thereby selecting the suitable method for the inference of consistent and robust casual estimation. |
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