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.
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
Funding statement: 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].
Acknowledgement
We acknowledge Folkhälsan Research Center and the Fin-HIT study group: Sabina Simola, Stephanie Von Kreamer, Jesper Skand, Catharina Sarkkola, Sajan Raju and Elisabete Weiderpass (Helsinki, Finland) for providing data for benchmarking the different models. Institute for Molecular Medicine Finland (FIMM) provided computational infrastructure and preformed the sequencing to this project. Suzanne Campbell and Marissa LaBlanc for critical evaluation of this manuscript.
List of abbreviations
- AR(p)
Autoregressive process of order p
- ChIP
Chromatin Immunoprecipitation
- DMR
Differentially methylated region
- Ek
Expected number of significant windows of size k
- FDR
False discovery rate
- MCMC
Markov Chain Monte Carlo
- OU-process
Ornstein-Uhlenbeck process
- tk
Window threshold for sliding windows of size k
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.
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Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/sagmb-2017-0050).
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