Department of Biostatistics, Columbia University Mailman School of Public Health, 722 West 168Th Street, New York, NY, 10032, USA.
Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain.
Curr Environ Health Rep. 2024 Jun;11(2):109-117. doi: 10.1007/s40572-024-00436-9. Epub 2024 Feb 22.
Epigenetic changes can be highly influenced by environmental factors and have in turn been proposed to influence chronic disease. Being able to quantify to which extent epigenomic processes are mediators of the association between environmental exposures and diseases is of interest for epidemiologic research. In this review, we summarize the proposed mediation analysis methods with applications to epigenomic data.
The ultra-high dimensionality and high correlations that characterize omics data have hindered the precise quantification of mediated effects. Several methods have been proposed to deal with mediation in high-dimensional settings, including methods that incorporate dimensionality reduction techniques to the mediation algorithm. Although important methodological advances have been conducted in the previous years, key challenges such as the development of sensitivity analyses, dealing with mediator-mediator interactions, including environmental mixtures as exposures, or the integration of different omic data should be the focus of future methodological developments for epigenomic mediation analysis.
表观遗传变化受环境因素的影响很大,反过来也被认为会影响慢性疾病。因此,能够定量评估表观基因组过程在环境暴露与疾病之间关联中的中介作用程度,这对流行病学研究很有意义。在这篇综述中,我们总结了应用于表观基因组数据的拟议中介分析方法。
组学数据的超高维度和高度相关性阻碍了中介效应的精确量化。已经提出了几种方法来处理高维环境中的中介作用,包括将降维技术纳入中介算法的方法。尽管近年来在方法学上取得了重要进展,但仍存在一些关键挑战,例如开发敏感性分析、处理中介-中介相互作用、将环境混合物作为暴露因素纳入其中,或整合不同的组学数据,这些都是未来进行表观基因组中介分析方法学发展的重点。