Statistical Models in Epigenomics

表观基因组学的统计模型

• 批准号: 6760111
• 负责人: KIMBERLY D SIEGMUND
• 金额: $ 23.16万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6760111
• 关键词: DNA methylation; adenoma; artificial intelligence; cancer risk; colorectal neoplasms; computer simulation; gene expression; human data; lung neoplasms; mathematical model; model design /development; statistics /biometry

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.

该提案的主要目标是开发用于分析DNA甲基化数据的统计模型。目前对基因表达谱提出的问题，例如如何基于细胞范围的基因表达模式对样品进行分类，或如何基于样品中基因的表达模式对具有未知功能的基因进行分类，可能类似地对DNA甲基化模式提出。 我们的重点是发现基于DNA甲基化模式的新亚群。 我们将DNA甲基化模式视为一个中间变量，沿着从暴露到结果的途径，并采取灵活的分层建模方法，可以将测量和未测量的协变量。 我们的目标是在南加州大学预防医学系和诺里斯癌症中心正在进行和计划的未来研究的动机。 具体而言，我们建议：1.开发基于模型的类发现方法（聚类分析/无监督学习方法），使用DNA甲基化的定量测量，调整基因座特异性和样本特异性协变量效应。a.分析取自肿瘤边缘的肿瘤组织和正常组织的配对样品。B.扩展模型以允许每例受试者有多个肿瘤。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）风险因素和甲基化模式簇之间的关联，以及B）甲基化模式簇和结果之间的关联，开发用于表征甲基化模式作为中间变量的模型。3.应用方法研究结直肠腺瘤和肺癌的DNA甲基化。