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检测具有多个不同关联的差异甲基化区域。

Detecting differentially methylated regions with multiple distinct associations.

机构信息

Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA.

Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, 94704, USA.

出版信息

Epigenomics. 2021 Mar;13(6):451-464. doi: 10.2217/epi-2020-0344. Epub 2021 Mar 1.

Abstract

We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff. We used a simulation study and real data analysis to evaluate performance. Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood. Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1-2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods. This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.

摘要

我们评估了五种用于检测差异甲基化区域(DMR)的方法:DMRcate、comb-p、seqlm、GlobalP 和 dmrff。我们使用模拟研究和实际数据分析来评估性能。此外,我们还评估了使用与祖先匹配的参考队列来估计脐带血中 CpG 位点之间相关性的方法。几种方法的Ⅰ型错误率膨胀,在更严格的显著水平下,这种膨胀增加。在具有相同效应方向的 1-2 个因果 CpG 位点的功效模拟中,dmrff 始终是最有效的方法之一。本研究说明了在评估新方法时需要更彻底的模拟研究。必须做更多的工作来开发具有良好控制Ⅰ型错误率且不需要个体水平数据的方法。

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