Gene-set analysis is severely biased when applied to genome-wide methylation data
Gene-set analysis is severely biased when applied to genome-wide methylation data
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DOI：
10.1093/bioinformatics/btt311
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发表时间：
2013-08-01
期刊：
影响因子：
5.8
通讯作者：
Seoighe, Cathal
中科院分区：
文献类型：
--
作者：
Geeleher, Paul;Hartnett, Lori;Seoighe, Cathal
动机：DNA甲基化是一种表观遗传标记，可以稳定抑制基因表达。由于其生物学和临床意义，已经开发了几种方法来比较样本组之间的全基因组甲基化模式。应用基因集分析来识别富含差异甲基化基因的相关基因组通常是这些数据分析的主要组成部分。例如，这可以用来识别疾病发展中受到干扰的过程或途径。我们发现，基因组分析通常应用于全基因组甲基化检测，但由于与不同类别的基因和基因启动子相关的 CpG 位点数量存在差异，因此存在严重偏差。结果：我们使用肺癌中差异 CpG 岛甲基化研究的已发表数据以及我们为研究长期溃疡性结肠炎患者的甲基化变化而生成的数据集证明了这种偏差。我们表明，一些看似丰富的基因集也可以通过随机数据来识别。我们建议采用两种现有方法来纠正偏差。考虑肺癌和溃疡性结肠炎数据集的偏差，分别为甲基化在癌症发展和慢性炎症中的作用提供了新的生物学见解。我们的结果对许多先前的全基因组甲基化研究具有重要意义，这些研究是在这种强烈偏差的分析的基础上得出结论的。
Motivation: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters.Results: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis.