A method to detect differentially methylated loci with next-generation sequencing.
A method to detect differentially methylated loci with next-generation sequencing.
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DOI：
10.1002/gepi.21726
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发表时间：
2013-05
影响因子：
2.1
通讯作者：
George V
中科院分区：
文献类型：
--
作者：
Xu H;Podolsky RH;Ryu D;Wang X;Su S;Shi H;George V
表观遗传学变化，特别是CpG位点的DNA甲基化在癌症和其他复杂疾病中具有重要意义。随着新一代测序技术的发展，采用病例对照设计来研究全基因组基因座甲基化状态的差异是可行的。然而，缺乏适当和有效的统计检验。有几个挑战。首先，与使用微阵列的甲基化实验不同，在微阵列中，一个个体在特定CpG位点的甲基化只有一种测量方法，这里我们有每个个体的甲基化等位基因和非甲基化等位基因的计数。其次，由于样品制备的性质，测量的甲基化反映了样品制备中涉及的细胞混合物的甲基化状态。因此，所测量的甲基化水平的潜在分布是未知的，并且稳健的测试比参数方法更可取。第三，目前NGS测量超过200万个CpG位点的甲基化。任何统计检验都必须在计算上有效，以便应用于NGS数据。考虑到这些挑战，我们提出了一种基于聚类数据分析的差异甲基化测试，通过建模的甲基化计数。我们进行了模拟，以表明它在测量的甲基化水平的几种分布下是稳健的。它具有良好的功率，并且计算效率高。最后，我们将测试应用于我们的慢性淋巴细胞白血病的NGS数据。结果表明，该方法具有较好的应用前景.
Epigenetic changes, especially DNA methylation at CpG loci have important implications in cancer and other complex diseases. With the development of next-generation sequencing (NGS), it is feasible to generate data to interrogate the difference in methylation status for genome-wide loci using case-control design. However, a proper and efficient statistical test is lacking. There are several challenges. First, unlike methylation experiments using microarrays, where there is one measure of methylation for one individual at a particular CpG site, here we have the counts of methylation allele and unmethylation allele for each individual. Second, due to the nature of sample preparation, the measured methylation reflects the methylation status of a mixture of cells involved in sample preparation. Therefore, the underlying distribution of the measured methylation level is unknown, and a robust test is more desirable than parametric approach. Third, currently NGS measures methylation at over 2 million CpG sites. Any statistical tests have to be computationally efficient in order to be applied to the NGS data. Taking these challenges into account, we propose a test for differential methylation based on clustered data analysis by modeling the methylation counts. We performed simulations to show that it is robust under several distributions for the measured methylation levels. It has good power and is computationally efficient. Finally, we apply the test to our NGS data on chronic lymphocytic leukemia. The results indicate that it is a promising and practical test.
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