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Differential methylation tests of regulatory regions

Author

Listed:
  • Ryu Duchwan
  • Xu Hongyan
  • George Varghese

    (Department of Biostatistics and Epidemiology, Augusta University, Augusta, GA, USA)

  • Su Shaoyong
  • Wang Xiaoling

    (Georgia Prevention Institute, Department of Pediatrics, Augusta University, Augusta, GA, USA)

  • Shi Huidong

    (AU Cancer Center, Department of Biochemistry and Molecular Biology, Augusta University, Augusta, GA, USA)

  • Podolsky Robert H.

    (Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI, USA)

Abstract

Differential methylation of regulatory elements is critical in epigenetic researches and can be statistically tested. We developed a new statistical test, the generalized integrated functional test (GIFT), that tests for regional differences in methylation based on the methylation percent at each CpG site within a genomic region. The GIFT uses estimated subject-specific profiles with smoothing methods, specifically wavelet smoothing, and calculates an ANOVA-like test to compare the average profile of groups. In this way, possibly correlated CpG sites within the regulatory region are compared all together. Simulations and analyses of data obtained from patients with chronic lymphocytic leukemia indicate that GIFT has good statistical properties and is able to identify promising genomic regions. Further, GIFT is likely to work with multiple different types of experiments since different smoothing methods can be used to estimate the profiles of data without noise. Matlab code for GIFT and sample data are available at http://www.augusta.edu/mcg/biostatepi/people/software/gift.html.

Suggested Citation

  • Ryu Duchwan & Xu Hongyan & George Varghese & Su Shaoyong & Wang Xiaoling & Shi Huidong & Podolsky Robert H., 2016. "Differential methylation tests of regulatory regions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(3), pages 237-251, June.
  • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:3:p:237-251:n:3
    DOI: 10.1515/sagmb-2015-0037
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    References listed on IDEAS

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    1. Morris, Jeffrey S. & Vannucci, Marina & Brown, Philip J. & Carroll, Raymond J., 2003. "Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 573-583, January.
    2. Morris, Jeffrey S. & Arroyo, Cassandra & Coull, Brent A. & Ryan, Louise M. & Herrick, Richard & Gortmaker, Steven L., 2006. "Using Wavelet-Based Functional Mixed Models to Characterize Population Heterogeneity in Accelerometer Profiles: A Case Study," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1352-1364, December.
    3. Frank Emmert-Streib & Galina V Glazko, 2011. "Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-6, May.
    4. Jeffrey S. Morris & Raymond J. Carroll, 2006. "Wavelet‐based functional mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 179-199, April.
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