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.

中文翻译：

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评估全基因组差异甲基化区域检测的意义。

DNA甲基化在人类健康和疾病中起着重要作用，并且识别不同甲基化区域的方法越来越受到关注。当前缺乏统计方法来正确应对多种检测，即控制差异甲基化区域的全基因组重要性。我们引入了一种扫描统计信息（DMRScan），它克服了这些限制。我们使用基于真实甲基化数据的模拟研究，对照两种完善的方法（bumphunter，DMRcate）对DMRScan进行了基准测试。可从Bioconductor获得DMRScan的实现。在不同的模拟场景中，特别是对于小效果尺寸，我们的方法具有比替代方法更高的功效。DMRScan在统计建模方面显示出更大的灵活性，并且与当前方法相比，可用于更复杂的设计。DMRScan是第一种动态方法，可以正确解决识别不同甲基化区域的多重测试难题。DMRScan在功率方面优于其他方法，同时可控制错误发现率。