Saddiki Hachem, Colicino Elena, Lesseur Corina
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Curr Environ Health Rep. 2022 Dec;9(4):625-630. doi: 10.1007/s40572-022-00374-4. Epub 2022 Aug 30.
DNA methylation (DNAm) is essential to human development and plays an important role as a biomarker due to its susceptibility to environmental exposures. This article reviews the current state of statistical methods developed for differential variability analysis focusing on DNAm data.
With the advent of high-throughput technologies allowing for highly reliable and cost-effective measurements of DNAm, many epigenome studies have analyzed DNAm levels to uncover biological mechanisms underlying past environmental exposures and subsequent health outcomes. These studies typically focused on detecting sites or regions which differ in their mean DNAm levels among exposure groups. However, more recent studies highlighted the importance of identifying differentially variable sites or regions as biologically relevant features. Currently, the analysis of differentially variable DNAm sites has not yet gained widespread adoption in environmental studies; yet, it is important to examine the effects of environmental exposures on inter-individual epigenetic variability. In this article, we describe six of the most widely used statistical approaches for analyzing differential variability of DNAm levels and provide a discussion of their advantages and current limitations.
DNA甲基化（DNAm）对人类发育至关重要，并且由于其对环境暴露的敏感性，作为生物标志物发挥着重要作用。本文综述了针对DNAm数据的差异变异性分析所开发的统计方法的现状。
随着高通量技术的出现，能够对DNAm进行高度可靠且经济高效的测量，许多表观基因组研究分析了DNAm水平，以揭示过去环境暴露及后续健康结果背后的生物学机制。这些研究通常侧重于检测暴露组之间平均DNAm水平不同的位点或区域。然而，最近的研究强调了识别差异可变位点或区域作为生物学相关特征的重要性。目前，差异可变DNAm位点的分析在环境研究中尚未得到广泛应用；然而，研究环境暴露对个体间表观遗传变异性的影响很重要。在本文中，我们描述了六种最广泛使用的分析DNAm水平差异变异性的统计方法，并讨论了它们的优点和当前局限性。
