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Human genetics
Jun, 2011;129 (6): 585-95.

Statistical approaches for the analysis of DNA methylation microarray data.

Kimberly D Siegmund
Author Information
  1. Kimberly D Siegmund: Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA 90089, USA. kims@usc.edu
PMID: 21519831 DOI: 10.1007/s00439-011-0993-x

Abstract

Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.

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Grants

  1. P30 ES007048/NIEHS NIH HHS
  2. R01 CA097346/NCI NIH HHS
  3. R01 CA097346-06/NCI NIH HHS
  4. P30 ES07048/NIEHS NIH HHS

MeSH Term

DNA Methylation
Humans
Oligonucleotide Array Sequence Analysis
Journal Article Research Support, N.I.H., Extramural Review
Article has an altmetric score of 3

Word Cloud

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