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Detection of Epigenetic Field Defects Using a Weighted Epigenetic Distance-Based Method

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Part of the Methods in Molecular Biology book series (MIMB, volume 2117)

Abstract

Identifying epigenetic field defects, notably early DNA methylation alterations, is important for early cancer detection. Research has suggested these early methylation alterations are infrequent across samples and identifiable as outlier samples. Here we developed a weighted epigenetic distance-based method characterizing (dis)similarity in methylation measures at multiple CpGs in a gene or a genetic region between pairwise samples, with weights to up-weight signal CpGs and down-weight noise CpGs. Using distance-based approaches, weak signals that might be filtered out in a CpG site-level analysis could be accumulated and therefore boost the overall study power. In constructing epigenetic distances, we considered both differential methylation (DM) and differential variability (DV) signals. We demonstrated the superior performance of the proposed weighted epigenetic distance-based method over nonweighted versions and site-level EWAS (epigenome-wide association studies) methods in simulation studies. Application to breast cancer methylation data from Gene Expression Omnibus (GEO) comparing normal-adjacent tissue to tumor of breast cancer patients and normal tissue of independent age-matched cancer-free women identified novel epigenetic field defects that were missed by EWAS methods, when majority were previously reported to be associated with breast cancer and were confirmed the progression to breast cancer. We further replicated some of the identified epigenetic field defects.

Key words

DNA methylation Epigenetic field defects Weighted distance-based method 
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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Biostatistics, Mailman School of Public HealthColumbia UniversityNew YorkUSA
  2. 2.Department of BiostatisticsGeorge Washington UniversityWashingtonUSA
  3. 3.CAS Key Lab of Computational Biology, Shanghai Institute for Biological SciencesChinese Academy of SciencesBeijingChina
  4. 4.Statistical Cancer Genomics, UCL Cancer InstituteUniversity College LondonLondonUK

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