High-Dimensional Regression for Data Integration

项目1：用于数据集成的高维回归 摘要 与高维基因组(例如基因组、转录组、表观基因组)特征相关联 丰富的功能和监管注释、途径信息和疾病特定知识 来自以前的研究，通常被用来解释对基因组数据的分析。在这个项目中，我们 建议开发能够结合这些外部因素的综合回归方法 信息是先验的，而不是事后的，以提高预测性能，选择预测 以及相关的特征，并深入了解在高血压病研究中潜在的生物学机制。 维度组学数据。在我们的第一个具体目标中，我们提出了一个一般的高维混合 用于将元特征(例如，功能注释、路径)集成到OMIC中的建模框架 研究，具有处理定量、分类和事件发生时间结果的灵活性，以及 通过包含随机效果来容纳相关数据的能力。我们的建议 该方法结合了混合建模、高维正则化回归和经验 贝叶斯策略，从数据分析中直接估计调整惩罚参数 而且在计算上很容易处理。所提出的集成模型可以在预测模式下部署 开发诊断和预后特征，或处于“发现模式”以识别基因组特征 与疾病结果相关。我们还提出了一套相应的工具来进行推理和 模型解释。我们的第二个具体目标是综合高维回归 转录范围关联研究的模型(TWAS)。我们建议利用最近的进展 连接增强子和其他DNA调控元件及其近端的靶基因以改善 预测受基因调控的基因表达，以提高能力和定位的目标 三次世界大战的能力。在我们的第三个具体目标中，我们重点介绍目标1和目标2中的方法的应用 几个癌症数据集。

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.