Systems Level Causal Discovery in Heterogeneous TOPMed Data

异构 TOPMed 数据中的系统级因果发现

基本信息

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
项目类别：
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

项目摘要

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.

异构顶级数据中的系统级因果发现抽象的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得疾病的详细表型特征成为可能，并为研究铺平了道路。这一过程中的一个主要瓶颈是缺乏稳健的、精准的治疗方法。该提案建立在正在进行的多模态数据的基础上。我们小组在生物医学因果学习领域的努力的目的是扩展，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，例如作为组学数据（SNP、代谢组学、RNA-seq 等）、影像、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 例数据集慢性阻塞性肺疾病 (COPD) 是导致患者死亡的第三大原因，美国的残疾原因和医疗费用普遍认为慢性阻塞性肺病是一种综合症，包括多种不同特点的疾病目前尚无令人满意的治疗方法。在这个项目中，我们将应用、测试和验证我们的方法。关于 COPDGene® 和另一个大型独立 COPD 队列我们方法的扩展和应用。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从机制上讲，我们将研究 SNP、基因及其网络的作用。在病理学中，我们将识别条件生物标志物，这将导致在病理生理学中，我们将进行疾病的细分和每个亚型的病因成分的识别。我们将确定与肺功能下降和结果直接相关的特征。通过网络和公共云界面向社区提供的结果将是 (1)。用于整合和分析多模式横截面和纵向数据的新概率方法， (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 促进和进展的病理生理学为了保证该项目的成功，我们进行了研究。组建了一个由基因组学、机器学习、云计算和 COPD 领域的专家组成的团队。纪律小组项目将产生超出上述可交付成果的积极影响，因为我们的普遍性方法使它们适用于任何疾病，我们预计在本次 U01 期间我们将拥有有机会与 TOPMed 联盟中的其他团队合作，帮助他们调查问题的原因我们确实相信将数据整合到一个概率框架中。当大量高通量数据收集时，将成为未来精准医学战略的核心将成为所有医院的常规诊断和预后程序。