Epigenetic hypothesis tests for methylation and acetylation in a triple microarray system

Lang Li, Huidong Shi, Constantin Yiannoutsos, Tim Hui Ming Huang, Kenneth P. Nephew

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


To fully elucidate the functional relationship between DNA methylation and histone hypoacetylation in gene silencing, we have developed an integrated "triple" microarray system that allows us to begin to decipher the influence of epigenetic hierarchies on the regulation of gene expression in cancer cells. Our hypothesis is that in the promoter region of a silenced gene, reversal of two epigenetic factors (i.e., DNA demethylation and/or histone hyperacetylation) is highly correlated with gene reexpression after treatment of the human epithelial ovarian cancer cell line CP70 with the drug combination 5-aza-2′-deoxycytidine (DAC), a demethylating agent, and trichostatin A (TSA), an inhibitor of histone deacetylases. To estimate the posterior probabilities for genes with altered expression, DNA methylation. and histone acetylation status measured with a triple-microarray system, we have employed an established empirical Bayes model. Two methods have been proposed to test our hypothesis that DNA demethylation and histone hyperacetylation are highly correlated among those up-regulated genes. One method follows a weighted least squares regression, while the other is derived from a chi-square statistic. The data derived by these approaches, which have been further verified through bootstrap analyses, support the proposed epigenetic correlation (p-values are less than 0.001). Further simulations suggest that even if the constant variance and normality assumptions do not hold, the power of those two tests is robust.

Original languageEnglish (US)
Pages (from-to)370-390
Number of pages21
JournalJournal of Computational Biology
Issue number3
StatePublished - 2005
Externally publishedYes


  • Acetylation
  • Empirical Bayes
  • Epigenetics
  • Methylation
  • Microarray

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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