孟德尔随机化
工具变量
多效性
因果推理
随意的
推论
计量经济学
选择(遗传算法)
样本量测定
选择偏差
统计
计算机科学
遗传变异
数学
生物
遗传学
人工智能
基因型
表型
复合材料
材料科学
基因
作者
Xiaofeng Gao,H Wang,T Wang
出处
期刊:PubMed
日期:2019-03-10
卷期号:40 (3): 360-365
被引量:8
标识
DOI:10.3760/cma.j.issn.0254-6450.2019.03.020
摘要
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.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。.
科研通智能强力驱动
Strongly Powered by AbleSci AI