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Cell-Type Heterogeneity in DNA Methylation Studies: Statistical Methods and Guidelines

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Epigenetic Epidemiology

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Abstract

Studies in epigenetic epidemiology have reported increasing numbers of epigenetic biomarkers associated with a wide range of exposures and outcomes. Due to cost and technical difficulties, these markers are usually derived from complex tissues that are composed of many different cell-types. This cell-type heterogeneity prevents the identification of cell-type specific epigenetic alterations, posing significant challenges to the interpretation and understanding of these markers. Consequently, there is a strong need to develop cost-effective computational solutions to tackle the cell-type heterogeneity problem. Here, I discuss some recently proposed cell-type deconvolution algorithms aimed at estimating cell-type fractions and identifying cell-type specific differential DNA methylation changes. I describe their successful application to epigenome studies. We also discuss their main limitations, providing general guidelines for their successful implementation and for correctly interpretating results derived from them.

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Abbreviations

DMC:

differentially methylated cytosine

DMCT:

differentially methylated cell-type

DNAm:

DNA methylation

EWAS:

Epigenome-Wide Association Study

FDR:

False Discovery Rate

FPR:

False Positive Rate

LSR:

least squares regression

mQTL:

methylation quantitative trait loci

PR C2:

Polycomb-Repressive-Complex-2

scRNA-Seq:

single-cell RNA-Seq

SE:

Sensitivity

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Authors and Affiliations

  1. CAS Key Laboratory of Computational Biology, Shanghai Institute for Nutrition and Health, Chinese Academy of Sciences, Shanghai, China

    Andrew E. Teschendorff

  2. UCL Cancer Institute, University College London, London, UK

    Andrew E. Teschendorff

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  1. Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, USA

    Karin B. Michels

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Teschendorff, A.E. (2022). Cell-Type Heterogeneity in DNA Methylation Studies: Statistical Methods and Guidelines. In: Michels, K.B. (eds) Epigenetic Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-030-94475-9_4

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