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

Methods Mol Biol. 2017;1589:99-106. doi: 10.1007/7651_2015_262.

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

MeSH terms

  • Computational Biology / methods*
  • Computational Biology / statistics & numerical data
  • DNA Methylation*
  • Genome, Human*
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • Leukocytes, Mononuclear / cytology
  • Leukocytes, Mononuclear / metabolism
  • Regression Analysis*
  • Software*