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.

非均匀TOPMED数据的系统级因果发现 摘要 用于收集和分析多个异构数据流的新技术的出现， 同一个人使疾病的详细表型表征成为可能，并为疾病的诊断铺平了道路。 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， 有效的和真正的综合分析方法，这样的多模态数据。该提案建立在正在进行的 我们的团队在生物医学因果学习领域的努力。本申请的目的是扩展， 修改和定制我们的因果概率图形模型，以适应TOPM项目通常收集的数据，例如 如组学数据（SNP、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，已使用这些模式生成了10，000 慢性阻塞性肺疾病（COPD）患者，第三大死亡原因和主要 残疾原因和医疗保健费用在美国。流行的观点是COPD是一种综合征， 多种不同特征的疾病。目前尚无令人满意的COPD治疗方法 亚型或疾病进展的预测。在这个项目中，我们将应用，测试和验证我们的方法 COPDGene®和另一个大型独立COPD队列。我们的方法的推广和应用 横向和纵向数据也将使我们能够调查一些重要的问题， 与COPD有关的问题从机制上讲，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。在病理学中，我们将确定条件性生物标志物，这将导致 疾病亚分类和每种亚型中致病成分的鉴定。在病理生理学中，我们将 识别与肺功能下降和结果直接相关的特征。我们将使我们所有的算法 以及通过网络和公共云接口向社区提供的结果。可交付成果将是（1） 用于多模态横截面和纵向数据的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，使这些 研究界可用的方法;（3）机制，病理学和 COPD易化和进展的病理生理学。为了保证项目的成功， 组建了一个基因组学、机器学习、云计算和COPD专家团队。这个十字架- 学科团队项目将产生积极的影响，超出上述交付成果，因为我们的一般性， 这些方法使它们适用于任何疾病。我们希望在U 01期间， 有机会与TOPMed联盟中的其他团队合作，帮助他们调查 它们相应的疾病表型。我们相信在单一概率框架中的数据集成 将在未来的精准医疗战略的核心，当大量的高通量数据收集， 将成为所有医院的常规诊断和预后程序。