Systems Level Causal Discovery in Heterogeneous TOPMed Data

异构 TOPMed 数据中的系统级因果发现

• 批准号: 9310591
• 负责人: PANAGIOTIS V BENOS
• 金额: $ 60.79万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-18 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9310591
• 关键词: Address; Algorithms; Area; Biological Markers; Biological Models; Blood; Cause of Death; Characteristics; Chronic Obstructive Airway Disease; Classification; Clinical; Clinical Data; Cloud Computing; Collaborations; Communities; Computational Biology; Computer software; Consensus; Data; Data Collection; Data Set; Development; Diagnostic; Disease; Disease Progression; Disease model; Disease susceptibility; Functional Imaging; Functional disorder; Funding; Future; Genes; Genetic Determinism; Genomic medicine; Genomics; Genotype; Goals; Graph; Health Care Costs; Heart; Hospitals; Image; Individual; Internet; Learning; Lifting; Link; Machine Learning; Methods; Modality; Modeling; Molecular; Morphology; Outcome; Outcome Assessment; Pathology; Patients; Peripheral Blood Mononuclear Cell; Phenotype; Physiological; Precision therapeutics; Procedures; Process; Pulmonology; Recording of previous events; Research; Research Personnel; Respiratory physiology; Risk; Risk Factors; Science; Stream; Subgroup; Syndrome; System; Technology; Testing; The Cancer Genome Atlas; Tissues; Trans-Omics for Precision Medicine; United States National Institutes of Health; Universities; Variant; Visit; X-Ray Computed Tomography; analytical method; base; clinical imaging; clinically relevant; cloud based; cohort; computer science; cost effective; data integration; disability; disease phenotype; disorder subtype; graphical user interface; high throughput technology; innovation; longitudinal dataset; medical schools; metabolomics; mortality; multimodality; new technology; novel; outcome forecast; precision genomic medicine; precision medicine; prognostic; repository; success; tool; transcriptome sequencing; user-friendly

SYSTEMS LEVEL CAUSAL DISCOVERY IN HETEROGENEOUS TOPMED DATA ABSTRACT The advent of new technologies for collecting and analyzing multiple heterogeneous data streams from the same individual makes possible the detailed phenotypic characterization of diseases and paves the way for the development of individualized precision therapies. A major bottleneck in this process is the lack of robust, efficient and truly integrative analytic methods for such multi-modal data. This proposal builds on the ongoing efforts of our group in the area of causal learning in biomedicine. The objective of this application is to extend, modify and tailor our causal probabilistic graphical models to data typically collected by TOPMed projects, such as –omics data (SNPs, metabolomics, RNA-seq, etc), imaging, patients' history, and clinical data. COPDGene® is one of the TOPMed projects and has generated datasets with those modalities for 10,000 patients with chronic obstructive pulmonary disease (COPD), the third leading cause of death and a major cause of disability and health care costs in the US. The prevailing view is that COPD is a syndrome, consisting of multiple diseases with different characteristics. There is currently no satisfactory method for COPD subtyping or prediction of disease progression. In this project we will apply, test and validate our approaches on COPDGene® and another large independent COPD cohort. The extension and application of our methods to cross-sectional and longitudinal data will also allow us to investigate a number of important questions and aspects related to COPD. Mechanistically, we will investigate how SNPs, genes and their networks are causally linked to disease phenotypes. In pathology, we will identify conditional biomarkers, which will lead to disease sub-classification and identification of causal components in each subtype. In pathophysiology, we will identify features that are directly linked to lung function decline and outcome. We will make all our algorithms and results available to the community through web and public cloud interfaces. The deliverables will be (1) new probabilistic approaches for integration and analysis of multi-modal cross-sectional and longitudinal data, including SNPs, blood biomarkers, CT scans and clinical data; (2) new cloud-based server to make these approaches available to the research community; (3) results on the mechanism, pathology and pathophysiology of COPD facilitation and progression. To guarantee the success of the project we have assembled a team of experts in genomics, machine learning, cloud computing and COPD. This cross- disciplinary team project will have a positive impact beyond the above deliverables, since the generality of our approaches makes them applicable to any disease. We expect that during this U01 we will have the opportunity to collaborate with other teams in the TOPMed consortium to help them investigate the causes of their corresponding disease phenotypes. We do believe that data integration in a single probabilistic framework will be in the heart of precision medicine strategies in the future, when massive high-throughput data collection will become a routine diagnostic and prognostic procedure in all hospitals.

异质TOPMED数据中的系统级因果发现 摘要 用于收集和分析来自 同一个人使疾病的详细表型特征成为可能，并为 个性化精准治疗的发展。这一过程中的一个主要瓶颈是缺乏强大的、 对这种多模式数据的高效和真正一体化的分析方法。这项提议建立在正在进行的 我们小组在生物医学因果学习领域的努力。此应用程序的目标是扩展， 根据TOPMed项目通常收集的数据修改和定制我们的因果概率图形模型，例如 AS-组学数据(SNPs、代谢组学、RNA-Seq等)、成像、患者病史和临床数据。 COPDgene®是TOPMed项目之一，已经为10,000名患者生成了这些医疗设备的数据集 慢性阻塞性肺疾病(COPD)是导致死亡的第三大原因，也是 美国的残疾原因和医疗费用。流行的观点认为慢性阻塞性肺病是一种综合征，包括 具有不同特征的多种疾病。目前还没有令人满意的治疗COPD的方法 疾病进展的亚型或预测。在这个项目中，我们将应用、测试和验证我们的方法 在COPDgene®和另一个大型独立COPD队列中。我们方法的推广和应用 横截面和纵向数据也将使我们能够调查一些重要的问题和 与慢性阻塞性肺病相关的方面。从机制上讲，我们将研究SNPs、基因及其网络是如何 与疾病表型有因果关系。在病理学方面，我们将识别条件生物标记物，这将导致 疾病亚类和每个亚型中的因果成分的识别。在病理生理学方面，我们将 确定与肺功能下降和预后直接相关的特征。我们将把我们所有的算法 并通过Web和公共云界面向社区提供结果。交付成果将为(1) 多模式横断面和纵向数据整合和分析的新概率方法， 包括SNP、血液生物标志物、CT扫描和临床数据；(2)新的基于云的服务器，以实现这些 可供研究界使用的方法；(3)关于机制、病理学和 慢性阻塞性肺疾病易化和进展的病理生理学。为了保证我们的项目的成功 组建了一支由基因组学、机器学习、云计算和慢性阻塞性肺病专家组成的团队。这个十字架- 纪律团队项目将产生上述交付成果之外的积极影响，因为我们的 方法使它们适用于任何疾病。我们预计在今年的U01期间，我们将有 有机会与TOPMed联盟中的其他团队合作，帮助他们调查 它们对应的疾病表型。我们确实认为，在单一概率框架中进行数据集成 将成为未来精准医疗战略的核心，当海量高通量数据收集时 将成为所有医院的常规诊断和预后程序。