A-clustering: A novel method for the detection of co-regulated methylation regions, and regions associated with exposure

Tamar Sofer*, Elizabeth D. Schifano, Jane A. Hoppin, Lifang Hou, Andrea A. Baccarelli

*Corresponding author for this work

Research output: Contribution to journalArticle

47 Scopus citations

Abstract

Motivation: DNA methylation is a heritable modifiable chemical process that affects gene transcription and is associated with other molecular markers (e.g. gene expression) and biomarkers (e.g. cancer or other diseases). Current technology measures methylation in hundred of thousands, or millions of CpG sites throughout the genome. It is evident that neighboring CpG sites are often highly correlated with each other, and current literature suggests that clusters of adjacent CpG sites are co-regulated. Results: We develop the Adjacent Site Clustering (A-clustering) algorithm to detect sets of neighboring CpG sites that are correlated with each other. To detect methylation regions associated with exposure, we propose an analysis pipeline for high-dimensional methylation data in which CpG sites within regions identified by A-clustering are modeled as multivariate responses to environmental exposure using a generalized estimating equation approach that assumes exposure equally affects all sites in the cluster. We develop a correlation preserving simulation scheme, and study the proposed methodology via simulations. We study the clusters detected by the algorithm on high dimensional dataset of peripheral blood methylation of pesticide applicators.

Original languageEnglish (US)
Pages (from-to)2884-2891
Number of pages8
JournalBioinformatics
Volume29
Issue number22
DOIs
StatePublished - Nov 15 2013

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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