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