Detection of epigenetic changes using ANOVA with spatially varying coefficients

Xiao Guanghua, Wang Xinlei, LaPlant Quincey, Eric J. Nestler, Yang Xie

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Identification of genome-wide epigenetic changes, the stable changes in gene function without a change in DNA sequence, under various conditions plays an important role in biomedical research. Highthroughput epigenetic experiments are useful tools to measure genome-wide epigenetic changes, but the measured intensity levels from these high-resolution genome-wide epigenetic profiling data are often spatially correlated with high noise levels. In addition, it is challenging to detect genome-wide epigenetic changes across multiple conditions, so efficient statistical methodology development is needed for this purpose. In this study, we consider ANOVA models with spatially varying coefficients, combined with a hierarchical Bayesian approach, to explicitly model spatial correlation caused by location-dependent biological effects (i.e., epigenetic changes) and borrow strength among neighboring probes to compare epigenetic changes across multiple conditions. Through simulation studies and applications in drug addiction and depression datasets, we find that our approach compares favorably with competing methods; it is more efficient in estimation and more effective in detecting epigenetic changes. In addition, it can provide biologically meaningful results.

Original languageEnglish (US)
Pages (from-to)189-205
Number of pages17
JournalStatistical Applications in Genetics and Molecular Biology
Volume12
Issue number2
DOIs
StatePublished - May 2013

Keywords

  • AR1
  • Autoregressive
  • Bayesian hierarchical model
  • Epigenetic changes

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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