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Statistical Applications in Genetics and Molecular Biology

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

Christian M. PageORCID iD: http://orcid.org/0000-0002-1897-3666
  • Department of Neurology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
  • Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, N-0407 Oslo, Norway
  • Department of Non-Communicable Diseases, Norwegian Institute of Public Health, N-0403 Oslo, Norway
  • orcid.org/0000-0002-1897-3666
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Linda Vos / Trine B. Rounge
  • Department of Research, Cancer Registry of Norway, Oslo, Norway
  • Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hanne F. Harbo
  • Department of Neurology, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
  • Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, N-0407 Oslo, Norway
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Bettina K. Andreassen
Published Online: 2018-09-19 | DOI: https://doi.org/10.1515/sagmb-2017-0050

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.

This article offers supplementary material which is provided at the end of the article.

Keywords: differentially methylated regions; genomics scan statistics; sliding window

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About the article

aThese authors contributed equallly to this work.

bCurrent address: Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, N-0407 Oslo, Norway


Published Online: 2018-09-19


Funding Source: University of Oslo

Award identifier / Grant number: 531217/1231

Funding Source: Folkhälsan Research Foundation; The Academy of Finland

Award identifier / Grant number: 250704

This work was supported by the University of Oslo [Funder Id: 10.13039/501100005366, grant number 531217/1231]; Folkhälsan Research Foundation; The Academy of Finland [grant number 250704]; The Life and Health Medical Fund [grant number 1-23-28]; The Swedish Cultural Foundation in Finland [grant number 15/0897]; The Signe and Ane Gyllenberg Foundation [grant number 37-1977-43]; and The Yrjö Jahnsson Foundation [grant number 11486].


Declarations

Ethics: The Coordinating Ethics Committees of the Hospital Districts of Helsinki and Uusimaa approved the study. Informed consent was obtained from all participants and as well as one of their legal guardians.

Availability of data and materials: The R package is placed at Bioconductor under the name DMRScan, along with the example data set used in this paper. The R-code for comparing the methods can be found in the GitHub repos for the of the R package: https://github.com/christpa/DMRScan.

Conflict of interest statement: The authors declare that they have no competing interests.


Citation Information: Statistical Applications in Genetics and Molecular Biology, 20170050, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/sagmb-2017-0050.

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