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Assessing genome-wide significance for the detection of differentially methylated regions

Author

Listed:
  • Page Christian M.

    (Department of Neurology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway)

  • Vos Linda

    (Department of Research, Cancer Registry of Norway, Oslo, Norway)

  • Rounge Trine B.

    (Department of Research, Cancer Registry of Norway, Oslo, Norway)

  • Harbo Hanne F.

    (Department of Neurology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway)

  • Andreassen Bettina K.

    (Department of Research, Cancer Registry of Norway, Oslo, Norway)

Abstract

DNA methylation plays an important role in human health and disease, and methods for the identification of differently methylated regions are of increasing interest. There is currently a lack of statistical methods which properly address multiple testing, i.e. control genome-wide significance for differentially methylated regions. We introduce a scan statistic (DMRScan), which overcomes these limitations. We benchmark DMRScan against two well established methods (bumphunter, DMRcate), using a simulation study based on real methylation data. An implementation of DMRScan is available from Bioconductor. Our method has higher power than alternative methods across different simulation scenarios, particularly for small effect sizes. DMRScan exhibits greater flexibility in statistical modeling and can be used with more complex designs than current methods. DMRScan is the first dynamic approach which properly addresses the multiple-testing challenges for the identification of differently methylated regions. DMRScan outperformed alternative methods in terms of power, while keeping the false discovery rate controlled.

Suggested Citation

  • Page Christian M. & Vos Linda & Rounge Trine B. & Harbo Hanne F. & Andreassen Bettina K., 2018. "Assessing genome-wide significance for the detection of differentially methylated regions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-8, October.
    • Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:5:p:8:n:1
    DOI: 10.1515/sagmb-2017-0050
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    References listed on IDEAS

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    1. D. O. Siegmund & N. R. Zhang & B. Yakir, 2011. "False discovery rate for scanning statistics," Biometrika, Biometrika Trust, vol. 98(4), pages 979-985.
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