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Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach

  • Meaghan J. Jones
  • Sumaiya A. Islam
  • Rachel D. Edgar
  • Michael S. Kobor
Part of the Methods in Molecular Biology book series (MIMB, volume 1589)

Abstract

Analysis of DNA methylation in a population context has the potential to uncover novel gene and environment interactions as well as markers of health and disease. In order to find such associations it is important to control for factors which may mask or alter DNA methylation signatures. Since tissue of origin and coinciding cell type composition are major contributors to DNA methylation patterns, and can easily confound important findings, it is vital to adjust DNA methylation data for such differences across individuals. Here we describe the use of a regression method to adjust for cell type composition in DNA methylation data. We specifically discuss what information is required to adjust for cell type composition and then provide detailed instructions on how to perform cell type adjustment on high dimensional DNA methylation data. This method has been applied mainly to Illumina 450K data, but can also be adapted to pyrosequencing or genome-wide bisulfite sequencing data.

Keywords:

DNA methylation Illumina Infinium HumanMethylation450 BeadChip Cell type Statistical adjustment R statistical software 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Meaghan J. Jones
    • 1
  • Sumaiya A. Islam
    • 1
  • Rachel D. Edgar
    • 1
  • Michael S. Kobor
    • 1
  1. 1.Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, Child and Family Research InstituteUniversity of British ColumbiaVancouverCanada

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