MethylPCA: a toolkit to control for confounders in methylome-wide association studies.
MethylPCA: a toolkit to control for confounders in methylome-wide association studies.
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DOI：
10.1186/1471-2105-14-74
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发表时间：
2013-03-02
影响因子：
3
通讯作者：
van den Oord EJ
中科院分区：
文献类型：
--
作者：
Chen W;Gao G;Nerella S;Hultman CM;Magnusson PK;Sullivan PF;Aberg KA;van den Oord EJ
在全甲基组关联研究（MWAS）中，病例和对照组之间可能存在许多差异（例如与生活方式、饮食和药物使用有关），这些差异可能会影响甲基组并产生假阳性结果。控制这些混杂因素的有效方法是首先捕获甲基化数据中变异的主要来源，然后在关联分析中回归出这些成分。然而，由于人类基因组中甲基化位点的数量极大，这种方法在计算上非常具有挑战性。我们介绍了MethylPCA，它是专门设计用于控制甲基化位点数量非常大的研究中潜在的混杂因素的。MethylPCA提供了一个完整和灵活的数据分析，包括1)一种自适应方法，通过经验结合邻近位点的甲基化数据，在PCA之前执行数据约简，2)一种高效的算法，在超高维数据矩阵上执行主成分分析（PCA），以及3)关联测试。为了实现这一点，MethylPCA允许并行执行任务，使用c++进行CPU和I/O密集型计算，并存储中间结果，以避免多次计算相同的统计数据或将结果保存在内存中。通过对1500名受试者的真实全甲基组MBD-seq研究的模拟和分析，我们表明甲基pca有效地控制了潜在的混杂因素。MethylPCA为用户提供了一个方便的MWAS工具。该软件有效地处理了内存和速度方面的挑战，当使用MWAS所需的样本量查询数百万个站点时，使用现有软件无法完成这些任务。
In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS.