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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Detection of epigenetic changes using ANOVA with spatially varying coefficients

Xiao Guanghua1 / Wang Xinlei2 / LaPlant Quincey3 / Eric J. Nestler3 / 1

1Division of Biostatistics, Department of Clinical Sciences, The University of Texas Southwestern Medical Center at Dallas, TX 75390, USA

2Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA

3Fishberg Department of Neuroscience, Mount Sinai School of Medicine, New York, NY 10029, USA

Corresponding author: Yang Xie, Division of Biostatistics, Department of Clinical Sciences, The University of Texas Southwestern Medical Center at Dallas, TX 75390, USA

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 12, Issue 2, Pages 189–205, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.1515/sagmb-2012-0057, March 2013

Publication History

Published Online:
2013-03-13

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. High-throughput 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.

Keywords: AR1; autoregressive; Bayesian hierarchical model; epigenetic changes

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