Dimou Niki L, Tsilidis Konstantinos K
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Methods Mol Biol. 2018;1793:211-230. doi: 10.1007/978-1-4939-7868-7_13.
Mendelian randomization (MR) is becoming a popular approach to estimate the causal effect of an exposure on an outcome overcoming limitations of observational epidemiology. The advent of genome-wide association studies and the increasing accumulation of summarized data from large genetic consortia make MR a powerful technique. In this review, we give a primer in MR methodology, describe efficient MR designs and analytical strategies, and focus on methods and practical guidance for conducting an MR study using summary association data. We show that the analysis is straightforward utilizing either the MR-base platform or available packages in R. However, further research is required for the development of specialized methodology to assess MR assumptions.
孟德尔随机化（MR）正成为一种流行的方法，用于估计暴露因素对结局的因果效应，克服了观察性流行病学的局限性。全基因组关联研究的出现以及大型遗传联盟汇总数据的不断积累，使MR成为一种强大的技术。在本综述中，我们提供了MR方法的入门知识，描述了有效的MR设计和分析策略，并重点介绍了使用汇总关联数据进行MR研究的方法和实用指南。我们表明，使用基于MR的平台或R中的可用软件包进行分析很简单。然而，需要进一步研究来开发专门的方法以评估MR假设。
