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Assessing Differential Variability of High-Throughput DNA Methylation Data

  • Environmental Epigenetics (A Kupsco and A Cardenas, Section Editors)
  • Published:
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Abstract

Purpose of Review

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.

Recent Findings

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.

Summary

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.

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Funding

During the preparation of this manuscript, EC was supported by the National Institute of Environmental Health Science (NIEHS): R01ES032242, 5U2CES026555-03, and P30ES023515. CL was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): R00HD097286 and NIEHS P30ES023515.

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Authors and Affiliations

  1. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Hachem Saddiki, Elena Colicino & Corina Lesseur

Authors
  1. Hachem Saddiki
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  2. Elena Colicino
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  3. Corina Lesseur
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Correspondence to Corina Lesseur.

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Saddiki, H., Colicino, E. & Lesseur, C. Assessing Differential Variability of High-Throughput DNA Methylation Data. Curr Envir Health Rpt 9, 625–630 (2022). https://doi.org/10.1007/s40572-022-00374-4

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