Statistical Methods for Integrative Genomics in Cancer

总体摘要 该计划项目的总体目标是开发新的统计方法 通过合作解决癌症的病因、预后和治疗的多组学数据 四个密切相关的项目和四个 共享核心(见插图)。四个 项目可以概括地描述为 跨越分析的光谱 挑战包括功能选择、 调解、互动和 人物刻画。其中第一个， “数据的高维回归” 整合，“开发新战略 对于纵向经济的分析 合并外部泛函的数据 信息，保持严谨 推理基础。第二 项目，“将数字数据集成到 估计调解或潜伏期 结构，开发新的潜在因素 和中介模型，使用高维OMIC数据或GWAS摘要统计数据来确定 并区分基因、暴露和基因效应。第三个项目“OMIC的整合” 《基因x环境交互作用分析中的数据》结合了基因表达和 其他-将组学数据转换为强大的多步骤方法，以利用 暴露或疾病边缘关联。项目3还将添加新的方法来确定 转录相互作用，具有遗传约束的分层GxE模型(即，需要 相互作用以包括相应的主效应)，以及对纵向、生存、 和数量性状。第四个项目是“基因组特征的统计方法”， 使用系统发育推理自动注释基因功能，以识别新的癌症- 保守DNA甲基化的特定区域。项目4还提出了一种新的方法 不可知途径基因集浓缩分析。这些项目将得到四个核心的支持： 管理核心(A)、功能注释核心(B)、计算和软件 开发核心(C)和数据分析和研究翻译核心(D)。核心B将 维护关键生物信息学资源的最新副本，并将开发软件 该应用程序将提供单个统一门户，用于创建集成了 来自多个资源的数据。酷睿C将帮助满足高容量计算需求，并将 开发实现新方法的用户友好的软件包。核心D将重点放在 新方法的翻译，既支持对真实癌症数据集的应用，也通过 开发培训外部调查人员使用我们的方法和软件的材料。 我们提议的工作将具有方法论和实质性的重要性。一对一 另一方面，我们将开发适用于广泛癌症的新的统计方法 流行病学研究和临床试验。例如，这些方法将允许更强大的 通过利用生物信息发现遗传关联和相互作用 其他消息来源。它们将在风险预测和预测领域具有翻译意义 有针对性的干预。我们的计划被设计为高度整合，与各种 项目和核心是相互关联的，因此它们加在一起将比任何一个都更有信息量 他们可能要靠自己了。计划成员可以访问以下地址的非凡数据资源： 南加州大学和其他地方，确保我们开发的方法将受到激励并适用 对，当前癌症研究中出现的重要问题。

OVERALL ABSTRACT The overall goal of this Program Project is to develop novel statistical methods for integrating multi-omic data to address etiology, prognosis, and treatment of cancer through a collaboration of four closely related projects and four shared cores (see inset). The four projects can be broadly described as spanning the spectrum of analysis challenges including feature selection, mediation, interaction, and characterization. The first of these, “High-Dimensional Regression for Data Integration,” develops new strategies for the analysis of longitudinal -omic data incorporating external functional information, maintaining a rigorous inferential foundation. The second project, “Integration of Omic Data to Estimate Mediation or Latent Structures,” develops novel latent factor and mediation models using high-dimensional omic data or GWAS summary statistics to identify and distinguish genotype, exposure and omic effects. The third project, “Integration of Omic Data in the Analysis of Gene x Environment Interaction,” incorporates gene expression and other -omics data into powerful multi-step approaches to scan for interactions leveraging exposure or disease marginal associations. Project 3 will also add novel approaches to identify transcriptional interactions, hierarchical GxE models with heredity constraints (i.e., requiring interactions to include the corresponding main effects), and extensions to longitudinal, survival, and quantitative traits. The fourth project, “Statistical Methods for Genome Characterization,” automates annotation of gene function using phylogenetic inference to identify new cancer- specific regions of conserved DNA methylation. Project 4 also proposes a novel approach for agnostic pathway gene set enrichment analysis. These projects will be supported by four cores: Administrative Core (A), Functional Annotation Core (B), Computation and Software Development Core (C), and Data Analysis and Research Translation Core (D). Core B will maintain up-to-date copies of key bioinformatics resources and will develop a software application that will provide a single unified portal for creating annotation files that integrates data from multiple resources. Core C will assist with high-volume computing needs and will develop user-friendly software packages that implement novel methods. Core D will focus on translation of new methods, both by supporting applications to real cancer datasets and by developing materials for training outside investigators in the use of our methods and software. Our proposed work will have both methodological and substantive importance. On the one hand, we will develop novel statistical methods that will be applicable to a wide range of cancer epidemiology studies and clinical trials. These methods will, for example, allow more powerful discovery of genetic associations and interactions through leveraging biological information from other sources. They will have translational significance in the areas of risk prediction and targeted interventions. Our program is designed to be highly integrative, with the various projects and cores being inter-related, so that together they will be more informative than any of them could be on their own. Program members have access to extraordinary data resources at USC and elsewhere, assuring that the methods we develop will be motivated by, and applicable to, important questions arising in current cancer research.