TY - JOUR
T1 - Assessing Differential Variability of High-Throughput DNA Methylation Data
AU - Saddiki, Hachem
AU - Colicino, Elena
AU - Lesseur, Corina
N1 - Funding Information:
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.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - DNA methylation
KW - Differential methylation
KW - Differential variability
KW - Mean and variance test
KW - Variability test
UR - http://www.scopus.com/inward/record.url?scp=85137104495&partnerID=8YFLogxK
U2 - 10.1007/s40572-022-00374-4
DO - 10.1007/s40572-022-00374-4
M3 - Review article
AN - SCOPUS:85137104495
SN - 2196-5412
VL - 9
SP - 625
EP - 630
JO - Current Environmental Health Reports
JF - Current Environmental Health Reports
IS - 4
ER -