Page Christian M, Vos Linda, Rounge Trine B, Harbo Hanne F, Andreassen Bettina K
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.
Stat Appl Genet Mol Biol. 2018 Sep 19;17(5):/j/sagmb.2018.17.issue-5/sagmb-2017-0050/sagmb-2017-0050.xml. doi: 10.1515/sagmb-2017-0050.
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.
DNA甲基化在人类健康和疾病中起着重要作用，识别差异甲基化区域的方法越来越受到关注。目前缺乏能够妥善处理多重检验的统计方法，即控制全基因组范围内差异甲基化区域的显著性。我们引入了一种扫描统计量（DMRScan），它克服了这些局限性。我们使用基于真实甲基化数据的模拟研究，将DMRScan与两种成熟的方法（bumphunter、DMRcate）进行基准测试。DMRScan的一个实现可从Bioconductor获得。在不同的模拟场景中，我们的方法比其他方法具有更高的功效，特别是对于小效应量。DMRScan在统计建模方面表现出更大的灵活性，并且比当前方法可用于更复杂的设计。DMRScan是第一种妥善处理识别差异甲基化区域多重检验挑战的动态方法。DMRScan在功效方面优于其他方法，同时保持错误发现率可控。
