High-Dimensional Regression for Data Integration

数据集成的高维回归

• 批准号: 10411239
• 负责人: Juan Pablo Lewinger
• 金额: $ 27.58万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411239
• 关键词: Address; Biological; Categories; Chromosome Mapping; Colorectal Cancer; Complex; DNA; Data; Data Analytics; Data Set; Diagnostic; Disease; Disease Outcome; Elements; Enhancers; Evaluation; Event; Gene Expression; Gene Expression Profiling; Genes; Genetic Transcription; Genomics; Goals; Joints; Knowledge; Link; Malignant Neoplasms; Malignant neoplasm of ovary; Malignant neoplasm of prostate; Mediating; Methods; Modeling; Molecular; Outcome; Pathway interactions; Performance; Play; Population; Regulatory Element; Risk Factors; Role; Scanning; Statistical Methods; Time; Transcriptional Regulation; Untranslated RNA; Variant; Work; computerized tools; data integration; diagnostic signature; diagnostic tool; epigenomics; feature selection; flexibility; genome wide association study; high dimensionality; improved; insight; novel; predictive modeling; prognostic; prognostic signature; prognostic tool; tool; trait; transcriptomics

Project 1: High-Dimensional Regression for Data Integration Abstract Associated with high-dimensional omic (e.g. genomic, transcriptomic, epigenomic) features there is a rich set of functional and regulatory annotations, pathway information, and disease-specific knowledge from previous studies that is routinely used to interpret analyses of omic data. In this project, we propose to develop integrative regression methods capable of incorporating this array of external information a priori, rather than post hoc, to improve prediction performance, selection of predictive and associated features, and to gain insight into potential biological mechanisms in studies with high- dimensional omic data. In our first Specific Aim we propose a general high-dimensional mixed modelling framework for integrating meta-features (e.g. functional annotations, pathways) into omic studies, with the flexibility to handle quantitative, categorical, and time-to-event outcomes, as well as the ability to accommodate correlated data through the inclusion of random effects. Our proposed approach brings together mixed modeling, high-dimensional regularized regression, and an empirical Bayes strategy that makes the direct estimation of tuning penalty parameters from the data analytically and computationally tractable. The proposed integrative models can be deployed in ‘predictive mode’ to develop diagnostic and prognostic signatures, or in ‘discovery mode’ to identify omic features associated with disease outcomes. We also propose an accompanying set of tools for inference and model interpretation. Our second Specific Aim focuses on integrative high-dimensional regression models for transcription-wide association studies (TWAS). We propose to leverage recent advances linking enhancers and other DNA regulatory elements and their proximal target genes to improve the prediction of genetically regulated gene expression with the goal of boosting the power and localization ability of TWAS. In our third Specific Aim, we focus on applications of the methods in Aims 1 and 2 to several cancer datasets.

项目1：用于数据集成的高维回归 摘要 与高维组学（例如基因组学、转录组学、表观基因组学）特征相关，存在一种 丰富的功能和调控注释、途径信息和疾病特异性知识 从以前的研究，这是常规用于解释分析的组学数据。本课题 我建议开发能够将这一系列外部因素纳入的综合回归方法， 信息先验，而不是事后，以提高预测性能，选择预测 和相关特征，并深入了解高血压研究中的潜在生物学机制， 三维组学数据。在我们的第一个具体目标，我们提出了一个一般的高维混合 将元特征（例如功能注释、途径）整合到组学中的建模框架 研究，可灵活处理定量、分类和事件发生时间结局，以及 通过包含随机效应来适应相关数据的能力。我们提出的 这种方法将混合建模、高维正则化回归和经验回归结合在一起， 贝叶斯策略，从分析数据中直接估计调谐惩罚参数 并且易于计算。建议的综合模型可以部署在'预测模式' 开发诊断和预后特征，或以“发现模式”识别组学特征 与疾病结果相关。我们还提出了一套配套的推理工具， 模型解释我们的第二个具体目标集中在集成高维回归 全转录关联研究（TWAS）。我们建议利用最新的进展 连接增强子和其他DNA调控元件及其近端靶基因，以改善 预测基因调控的基因表达，目的是提高功率和定位 TWAS的能力。在我们的第三个具体目标中，我们着重于目标1和2中的方法的应用， 几个癌症数据集。