Statistical Models in Epigenomics

本提案的主要目标是开发用于DNA甲基化数据分析的统计模型。目前对基因表达谱提出的问题，如如何根据细胞范围内的基因表达模式对样本进行分类，或如何根据样本中的表达模式对功能未知的基因进行分类，可能对DNA甲基化模式提出类似的问题。我们的重点是发现基于DNA甲基化模式的新亚群。我们将DNA甲基化模式视为从暴露到结果的中间变量，并采用灵活的分层建模方法，可以纳入测量和未测量的协变量。我们的目标是受到南加州大学预防医学系和诺里斯癌症中心正在进行的和计划中的未来研究的激励。具体而言，我们建议：1。利用DNA甲基化的定量测量，调整位点特异性和样本特异性协变量效应，开发基于模型的类发现方法（聚类分析/无监督学习方法）。a.从肿瘤边缘取肿瘤组织和正常组织成对样本的分析。b.扩展模型以允许每个受试者有多个肿瘤。c.将模型扩展到样本和位点的二维聚类分析。2. 通过建模a)风险因素与甲基化模式集群之间的关联，以及b)甲基化模式集群与结果之间的关联，开发将甲基化模式作为中间变量表征的模型。3. 应用方法研究结直肠腺瘤和肺癌DNA甲基化。

The primary objective of this proposal is to develop statistical models for the analysis of DNA methylation data. Questions that are currently posed for gene expression profiles, such as how to classify samples based on cell-wide gene expression patterns or how to classify genes with unknown function, based on their expression patterns across samples, may be similarly posed for DNA methylation patterns. Our focus is on the discovery of new subgroups based on patterns of DNA methylation. We view DNA methylation patterns as an intermediate variable along a pathway from exposures to outcomes and take a flexible hierarchical modeling approach that can incorporate measured and unmeasured covariates. Our aims are motivated by ongoing and planned future studies in the Department of Preventive Medicine and Norris Cancer Center at the University of Southern California. Specifically, we propose to: 1. Develop model-based class discovery methods (cluster analysis/unsupervised learning approaches) using quantitative measures of DNA methylation, adjusting for locus-specific and sample-specific covariate effects. a. Analysis of paired samples of tumor tissue and normal tissue taken from the margin of the tumor. b. Extend model to allow for multiple tumors per subject. c. Extend model to a two-dimensional cluster analysis of samples and loci. 2. Develop models for characterizing methylation patterns as an intermediate variable by modeling a) the association between risk factors and methylation pattern clusters, and b) the association between methylation pattern clusters and outcome. 3. Apply methods to studies of DNA methylation in colorectacl adenomas and lung cancer.