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An empirically driven data reduction method on the human 450K methylation array to remove tissue specific

Rachel D Edgar1, Meaghan J Jones1, Wendy P Robinson1

  • 1Department of Medical Genetics, BC Children's Hospital, University of British Columbia, Vancouver, Canada.

Clinical Epigenetics
|February 11, 2017
PubMed
Summary
This summary is machine-generated.

Identifying non-variable cytosine-guanine pairs (CpGs) in human tissues like blood, buccal cells, and placenta can significantly reduce data dimensionality. This filtering enhances the power of epigenetic association studies by improving differential DNA methylation detection.

Keywords:
450KDNA methylationDimensionality reductionFilterMultiple-test correctionNon-variablePowerTissue

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Area of Science:

  • Epigenetics
  • Genomics
  • Bioinformatics

Background:

  • Genome-wide epigenetic association studies are increasing due to cost-effective technologies like the Illumina 450K Human Methylation Array.
  • The high number of measured cytosine-guanine pairs (CpGs) on the 450K array presents statistical challenges, particularly multiple test correction, due to many non-variable CpGs.
  • Non-variable CpGs offer limited information content in specific tissues, complicating the detection of differential DNA methylation.

Purpose of the Study:

  • To identify non-variable CpGs in commonly studied human tissues using meta-analysis of Illumina 450K data.
  • To develop empirical data reduction methods for epigenetic studies.
  • To enhance the statistical power for detecting differential DNA methylation in human methylome studies.

Main Methods:

  • Performed a meta-analysis of Illumina 450K methylation data from three human tissues: blood (605 samples), buccal epithelial cells (121 samples), and placenta (157 samples).
  • Developed lists of CpGs exhibiting no variability within each respective tissue type.
  • Utilized these lists as filters to reduce data dimensionality.

Main Results:

  • Generated substantial lists of non-variable CpGs for each tissue: 114,204 for blood, 120,009 for buccal epithelial cells, and 101,367 for placenta.
  • These lists serve as valuable filters for epigenetic association studies.
  • Demonstrated significant data dimensionality reduction, thereby lessening the severity of multiple testing correction.

Conclusions:

  • Propose an empirically derived method for data reduction in human methylome studies.
  • This approach increases statistical power for detecting differential DNA methylation associated with exposures.
  • Facilitates more effective analysis of epigenetic variations in population-based studies.