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：数据集成的高维回归 抽象的 与高维度（例如基因组，转录组，表观基因组）相关的特征 丰富的功能和调节注释，途径信息和疾病特定知识 从以前的研究中通常用于解释OMIC数据的分析。在这个项目中，我们 提出开发能够合并这一数组外部的集成回归方法的提议 先验信息，而不是事后事后，以提高预测性能，选择预测性 和相关的特征，并深入了解具有高的研究的潜在生物学机制 维度数据。在我们的第一个特定目标中，我们提出了一般的高维混合 将元功能（例如功能注释，途径）集成到OMIC的建模框架 研究的灵活性可以处理定量，分类和事件的结果以及 通过包含随机效应来适应相关数据的能力。我们提出的 方法汇集了混合建模，高维正规回归和经验 贝叶斯策略，从数据分析中直接估算从数据进行调整惩罚参数 和计算可拖动。提出的集成模型可以以“预测模式”部署 开发诊断和预后签名，或以“发现模式”来识别OMIC特征 与疾病结局有关。我们还提出了一组随附的推理工具和 模型解释。我们的第二个特定目的重点是集成的高维回归 转录整个关联研究模型（TWA）。我们建议利用最近的进步 连接增强子和其他DNA调节元件及其代理靶基因以改善 预测一般调节的基因表达，以增强功率和定位的目的 两条的能力。在我们的第三个特定目的中，我们专注于目标1和2中的方法的应用 几个癌症数据集。