Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

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

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596

Mathematical Citation Quotient (MCQ) 2016: 0.06

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 12, Issue 2 (Mar 2013)

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Detection of epigenetic changes using ANOVA with spatially varying coefficients

Xiao Guanghua
  • Division of Biostatistics, Department of Clinical Sciences, The University of Texas Southwestern Medical Center at Dallas, TX 75390, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Wang Xinlei / LaPlant Quincey / Eric J. Nestler / Yang Xie
  • Corresponding author
  • Division of Biostatistics, Department of Clinical Sciences, The University of Texas Southwestern Medical Center at Dallas, TX 75390, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-03-13 | DOI: https://doi.org/10.1515/sagmb-2012-0057

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

References

  • Berhow, M. T., N. Hiroi, L. A. Kobierski, S. E. Hyman and E. J. Nestler (1996): “Influence of cocaine on the JAK-STAT pathway in the mesolimbic dopamine system,” J. Neurosci., 16(24), 8019–8026.Google Scholar

  • Berton, O., C. A. McClung, R. J. Dileone, V. Krishnan, W. Renthal, S. J. Russo, D. Graham, N. M. Tsankova, C. A. Bolanos, M. Rios, L. M. Monteggia, D. W. Self and E. J. Nestler (2006): “Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress,” Science, 311, 864–868.CrossrefPubMedGoogle Scholar

  • Bird, A. (2007): “Perceptions of epigenetics,” Nature, 447(7143), 396–398.Web of ScienceGoogle Scholar

  • Buck, M. J., A. B. Nobel and J. D. Lieb (2005): “ChIPOTle: a user-friendly tool for the analysis of ChIP-chip data,” Genome Biol., 6(11), R97.CrossrefGoogle Scholar

  • Coryell, M. W., A. M. Wunsch, J. M. Haenfler, J. E. Allen, M. Schnizler, A. E. Ziemann, M. N. Cook, J. P. Dunning, M. P. Price, J. D. Rainier, Z. Liu, A. R. Light, D. R. Langbehn and J. A. Wemmie (2009): “Acid-sensing ion channel-1a in the amygdala, a novel therapeutic target in depression-related behavior,” J. Neurosci., 29(17), 5381–5388. doi:10.1523/JNEUROSCI.0360-09.2009.Google Scholar

  • Flight, M. H. (2009): “Mood disorders: channel inhibitor shows antidepressant potential,” Nat. Rev. Drug. Discov., 8(7), 540–540.CrossrefGoogle Scholar

  • Gelfand, A. E., H. J. Kim, C. F. Sirmans and S. Banerjee (2003): “Spatial modeling with spatially varying coefficient processes,” J. Am. Stat. Assoc., 98, 387–396.CrossrefGoogle Scholar

  • Gelfond, J.A., M. Gupta and J.G. Ibrahim (2009): “A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data,” Biometrics, 65, 1087–1095.CrossrefPubMedWeb of ScienceGoogle Scholar

  • Gottardo, R., W. Li, W. E. Johnson and X. S. Liu (2008): “A flexible and powerful bayesian hierarchical model for chip-chip experiments,” Biometrics, 64(2), 468–78.CrossrefWeb of SciencePubMedGoogle Scholar

  • Hemby, S.E., B. Horman and W. Tang (2005): “Differential regulation of ionotropic glutamate receptor subunits following cocaine self-administration,” Brain Res., 1064(1–2), 75–82.Google Scholar

  • Humburg, P., D. Bulger and G. Stone (2008): “Parameter estimation for robust HMM analysis of ChIP-chip data,” Bioinformatics, 9, 343.Google Scholar

  • Iyer, V.R., C. E. Horak, C. S. Scafe, D. Botstein, M. Snyder and P. O. Brown (2001): “Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF,” Nature, 409(6819), 533–538.Google Scholar

  • Ji, H. and W. Wong (2005): “Tilemap: create chromosomal map of tiling array hybridizations,” Bioinformatics, 18, 3629–3636.CrossrefGoogle Scholar

  • Johnson, W. E., W. Li, C. A. Meyer, R. Gottardo, J. S. Carroll, M. Brown and X. S. Liu (2006): “Model-based analysis of tiling-arrays for ChIP-chip,” PNAS, 103, 12457–12462.CrossrefGoogle Scholar

  • Jones, R. S. (2007): “Epigenetics: reversing the ’irreversible’”, NatWure, 450(7168), 357–359.Google Scholar

  • Keles, S. (2007): “Mixture modeling for genome-wide localization of transcription factors,” Biometrics, 63, 10–21.PubMedCrossrefWeb of ScienceGoogle Scholar

  • Kim, T. H., L. O. Barrera, M. Zheng, C. Qu, M. A. Singer, T. A. Richmand, Y. Wu, R. D. Green and B. Ren (2005): “A high-resolution map of active promoters in the human genome,” Nature, 436, 876–880.PubMedCrossrefGoogle Scholar

  • LaPlant, Q., V. Vialou, 3rd H. E. Covington, D. Dumitriu, J. Feng, B. L. Warren, I. Maze, D. M. Dietz, E. L. Watts, S. D. Iniguez, J. W. Koo, E. Mouzon, W. Renthal, F. Hollis, H. Wang, M. A. Noonan, Y. Ren, A. J. Eisch, C. A. Bolanos, M. Kabbaj, G. Xiao, R. L. Neve, Y. L. Hurd, R. S. Oosting, G. Fan, J. H. Morrison and E. J. Nestler (2010): “Dnmt3a regulates emotional behavior and spine plasticity in the nucleus accumbens,” Nat. Neurosci., 13(9), 1137–1143.Google Scholar

  • Liu, X. S., D. L. Brutlag and J. S. Liu (2002): “An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments,” Nat. Biotechnol., 20, 835–839.Google Scholar

  • Li, W., C. Meyer and X. Liu (2005): “A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences,” Bioinformatics, 21(suppl I), 274–282.CrossrefGoogle Scholar

  • Mash, D. C., J. ffrench Mullen, N. Adi, Y. Qin, A. Buck and J. Pablo (2007): “Gene expression in human hippocampus from cocaine abusers identifies genes which regulate extracellular matrix remodeling,” PLoS One, 2(11), e1187.Google Scholar

  • Mo, Q. and F. Liang (2010): “A hidden Ising model for ChIP-chip data analysis,” Bioinformatics, 26, 777–783.CrossrefPubMedWeb of ScienceGoogle Scholar

  • Mo, Q. and F. Liang (2010): “Bayesian modeling of chip-chip data through a high-orderising model,” Biometrics, 66(4), 1284–1294. doi:10.1111/j.1541-0420.2009.01379.x.PubMedCrossrefWeb of ScienceGoogle Scholar

  • Mo, Q. (2012): “A fully bayesian hidden Ising model for ChIP-seq data analysis,” Biostatistics, 13(1):113–128. doi: 10.1093/biostatistics/kxr029.CrossrefWeb of SciencePubMedGoogle Scholar

  • Nestler, E.J. and G. K. Aghajanian (1997): “Molecular and cellular basis of addiction,” Science 278(5335), 58–63.Google Scholar

  • Pan, W. and P. A. K. Wei (2008): “A parametric joint model of DNA-protein binding, gene expression and DNA sequence data to detect target genes of a transcription factor,” Pacific Symposium on Biocomputing, NIL:465–476.Google Scholar

  • Wei, P. and W. Pan (2008): “Incorporating gene functions into regression analysis of dna-protein binding data and gene expression data to construct transcriptional networks,” IEEE/ACM Transact. Comput. Biol. Bioinformatics, 5, 401–415.Web of ScienceGoogle Scholar

  • Qi, Y., A. Rolfe, K. D. MacIsaac, G. K. Gerber, D. Pokholok, J. Zeitlinger, T. Dan-ford, R. D. Dowell, E. Fraenkel, T. S. Jaakkola, R. A. Young and D. K. Gifford (2006): “High-resolution computational models of genome binding events,” Nat. Biotechnol., 21, 963–970.Google Scholar

  • Qiu, J. (2006): “Epigenetics: unfinished symphony,” Nature, 441(7090), 143–145.Google Scholar

  • Reiss, D.J., M. T. Facciotti and N. S. Baliga (2008): “Model-based deconvolution of genome-wide DNA binding,” Bioinformatics, 24, 396–403.PubMedWeb of ScienceCrossrefGoogle Scholar

  • Ren, B., F. Robert, J. J. Wyrick, O. Aparicio, E. G. Jennings, I. Simon, J. Zeitlinger, J. Schreiber, N. Hannett, E. Kanin, T. L. Volkert, C. J. Wilson, S. P. Bell and R. A. Young (2000): “Genome-wide location and function of DNA binding proteins,” Science, 290(5500), 2306–2309.Google Scholar

  • Renthal, W., A. Kumar, G. Xiao, M. Wilkinson, R. H. E. Covington, I. Maze, D. Sikder, A. J. Robison, Q. LaPlant, D. M. Dietz, S. J. Russo, V. Vialou, S. Chakravarty, T. J. Kodadek, A. Stack, M. Kabbaj and E. J. Nestler (2009): “Genome-wide analysis of chromatin regulation by cocaine reveals a role for sirtuins,” Neuron, 62(3), 335–348.PubMedCrossrefWeb of ScienceGoogle Scholar

  • Renthal, W. and E. J. Nestler (2008): “Epigenetic mechanisms in drug addiction,” Trends Mol. Med., 14, 341–350.CrossrefPubMedWeb of ScienceGoogle Scholar

  • Song, J. S., W. E. Johnson, X. Zhu, X. Zhang, W. Li, A. K. Manrai, J. S. Liu, R. Chen and X. S. Liu (2007): “Model-based analysis of two-color arrays (ma2c),” Genome Biol., 8(8), R178.Web of ScienceCrossrefGoogle Scholar

  • Song, J. S., W. E. Johnson, X. Zhu, X. Zhang, W. Li, A. K. Manrai, J. S. Liu, R. Chen and X. S. Liu (2007): “1R01 HG004069-01/HG/NHGRI NIH HHS/United States 1U01 HG004270-01/HG/NHGRI NIH HHS/United States R01 HG004069-02/HG/NHGRI NIH HHS/United States Research Support, N.I.H,” Extramural England Genome biology Genome Biol., 8(8), R178.Google Scholar

  • Taslim, C., J. Wu, P. Yan, G. Singer, J. Parvin, T. Huang, S. Lin and K. Huang (2009): “Comparative study on ChIP-seq data: normalization and binding pattern characterization,” Bioinformatics, 25(18), 2334–2340.Web of SciencePubMedCrossrefGoogle Scholar

  • Tsankova, N, W. Renthal, A. Kumar and E. J. Nestler (2007): “Epigenetic regulation in psychiatric disorders,” Nat. Rev. Neurosci., 8, 355–367.Google Scholar

  • Tsankova, N. M., O. Berton, W. Renthal, A. Kumar, R. L. Neve and E. J. Nestler (2006): “Sustained hippocampal chromatin regulation in a mouse model of depression and antidepressant action,” Nat. Neurosci., 9, 519–525.Google Scholar

  • Tusher, V.G., R. Tibshirani and G. Chu (2001): “Significance analysis of microarrays applied to the ionizing radiation response,” Proc. Natl. Acad. Sci. USA, 98, 5116–5121.CrossrefGoogle Scholar

  • Tuteja, G., P. White, J. Schug and K. H. Kaestner (2009): “Extracting transcription factor targets from ChIP-Seq data,” Nucleic Acids Res., 37, e113.Web of ScienceCrossrefGoogle Scholar

  • Valouev, A., D. S. Johnson, A. Sundquist, C. Medina, E. Anton, S. Batzoglou, R. M. Myers and A. Sidow (2008): “Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data,” Nat. Methods, 5, 829–834.Google Scholar

  • van den Oord, E.J., P. H. Kuo, A. M. Hartmann, B. T. Webb, H. J. Moller, J. M. Hettema, I. Giegling, J. Bukszar and D. Rujescu (2008): “Genomewide association analysis followed by a replication study implicates a novel candidate gene for neuroticism,” Arch. Gen. Psychiatry, 65, 1062–1071.PubMedWeb of ScienceGoogle Scholar

  • Wang, X., M. Zang and G. Xiao (2012): “Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models,” Stat. Med., doi: 10.1002/sim.5658. [Epub ahead of print].Web of SciencePubMedCrossrefGoogle Scholar

  • Wemmie, J. A., M. W. Coryell, C. C. Askwith, E. Lamani, A. S. Leonard, C. D. Sigmund and M. J. Welsh (2004): “Overexpression of acid-sensing ion channel 1a in transgenic mice increases acquired fear-related behavior,” Proc. Nat. Acad. Sci. USA, 101(10), 3621–3626. doi:10.1073/pnas.0308753101.CrossrefGoogle Scholar

  • Wilkinson, M. B., G. Xiao, A. Kumar, Q. LaPlant, W. Renthal, D. Sikder, T. J. Ko-dadek and E. J. Nestler (2009): “Imipramine treatment and resiliency exhibit similar chromatin regulation in the mouse nucleus accumbens in depression models,” J. Neurosci., 29(24), 7820–7832.CrossrefWeb of ScienceGoogle Scholar

  • Wu, M., F. Liang and Y. Tian (2009): “Bayesian modeling of ChIP-chip data using latent variables,” BMC Bioinformatics, 10, 352.CrossrefWeb of SciencePubMedGoogle Scholar

  • Xu, H., C. L. Wei, F. Lin and W. K. Sung (2008): “An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data,” Bioinformatics, 24(20), 2344–2349.Web of ScienceCrossrefPubMedGoogle Scholar

  • Zhang, Y., T. Liu, C. A. Meyer, J. Eeckhoute, D. S. Johnson, B. E. Bernstein, C. Nusbaum, R. M. Myers, M. Brown, W. Li and X. S. Liu (2008): “Model-based analysis of ChIP-Seq (MACS),” Genome Biol., 9, R137.CrossrefWeb of ScienceGoogle Scholar

  • Zheng, M., L. O. Barrera, B. Ren and Y. N. Wu (2007): “ChIP-chip: data, model, and analysis,” Biometrics, 63, 787–796.PubMedWeb of ScienceCrossrefGoogle Scholar

About the article

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


Published Online: 2013-03-13


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2012-0057.

Export Citation

©2013 by Walter de Gruyter Berlin Boston. Copyright Clearance Center

Comments (0)

Please log in or register to comment.
Log in