Multi-omic Subtyping of Chronic Obstructive Pulmonary Disease

慢性阻塞性肺疾病的多组学亚型

基本信息

批准号：
9975882
负责人：
Brian Daniel Hobbs
金额：
$ 17.28万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
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：
2017-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9975882
关键词：
Affect Bioinformatics Biological Biological Assay Blood Cause of Death Characteristics Chronic Chronic Obstructive Airway Disease Clinical Complement Complex Data Diagnostic radiologic examination Disease Disease Management Expert Opinion Gene Expression Genes Genetic Genetic Determinism Genetic Diseases Genetic Risk Genetic Variation Genomics Goals Impairment Individual Lung Lung diseases Lung volume reduction surgery Machine Learning Measures Methods MicroRNAs Molecular Molecular Profiling Multiomic Data Muscular Atrophy Network-based Outcome Participant Pathway interactions Patients Phenotype Principal Investigator Pulmonary Emphysema RNA Research Personnel Respiratory physiology SNP array Severities Spirometry Testing Training Trans-Omics for Precision Medicine United States base clinical phenotype clinically relevant cohort disorder subtype exome genome sequencing genome wide association study genomic data machine learning method miRNA expression profiling molecular subtypes mortality multiple omics next generation sequencing novel outcome forecast patient subsets peripheral blood personalized approach programs quantitative imaging respiratory risk variant transcriptome sequencing transcriptomics whole genome

项目摘要

PROJECT SUMMARY/ABSTRACT Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory mortality in the United States. COPD is a highly heterogeneous disease and some COPD therapies are only applied to specific clinically defined subtypes. With the advent of multiple high-throughput biological assays and machine learning approaches, data-driven subtypes are increasingly being recognized. We hypothesize that such subtypes exist in COPD and that they can be identified using an integrative, multi-'omic approach. To accomplish this goal, we first propose to complement existing RNA and whole genome sequencing data in the well-phenotyped COPDGene study with peripheral blood microRNA sequencing. We will study the relationship of microRNA to genetic variation and gene expression in COPD. Next, we will apply a patient-based network similarity method to these three data types to identify COPD molecular subtypes. Finally, we will associate these subtypes with important clinical phenotypes and outcomes, and validate these subtypes in an independent subset of subjects. Our analysis targets a key clinical problem in COPD management, and will allow the mentee to become an independent investigator, applying bioinformatic and machine learning methods to genomic data in respiratory diseases.

项目摘要/摘要慢性阻塞性肺疾病（COPD）是联合呼吸道死亡率的主要原因国家。 COPD是一种高度异质性疾病，某些COPD疗法仅适用于特定临床定义的亚型。随着多个高通量生物学测定和机器学习的出现方法，数据驱动的亚型越来越多地被识别。我们假设存在这种亚型在COPD中，可以使用综合性的多'派方法来识别它们。实现这一目标目标，我们首先建议在良好的型号中补充现有的RNA和整个基因组测序数据 Copdgene研究与外周血microRNA测序。我们将研究microRNA与 COPD中的遗传变异和基因表达。接下来，我们将采用基于患者的网络相似性方法对于这三种数据类型，以识别COPD分子亚型。最后，我们将将这些子类型与重要的临床表型和结果，并在独立子集中验证这些亚型主题。我们的分析针对COPD管理中的关键临床问题，将使受训者能够成为独立研究者，将生物信息学和机器学习方法应用于基因组数据呼吸系统疾病。