Clinical and genetic analysis of retinopathy of prematurity

早产儿视网膜病变的临床及遗传学分析

• 批准号: 9301528
• 负责人: MICHAEL F. CHIANG
• 金额: $ 66.23万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9301528
• 关键词: Address; Affect; Algorithms; Automated Indexing; Bioinformatics; Biomedical Research; Blindness; Blood Vessels; Blood specimen; Candidate Disease Gene; Childhood; Clinical; Clinical Informatics; Clinical Medicine; Clinical Research; Cohort Analysis; Cohort Studies; Computational Biology; Computers; DNA; Data; Development; Diabetic Retinopathy; Diagnosis; Disease; Disease Progression; Disease susceptibility; Dizygotic Twins; Environmental Risk Factor; Eye; Genetic; Genetic Markers; Genetic Materials; Genomics; Genotype; Goals; Grant; Health; Healthcare; Image; Image Analysis; Infant; Informatics; Information Management; Judgment; Knowledge; Longitudinal Studies; Macular degeneration; Measurement; Medical Genetics; Methods; Modeling; Molecular Genetics; Ophthalmology; Oxygen; Paper; Pathogenesis; Pathology; Pathway interactions; Peer Review; Phenotype; Predisposition; Premature Birth; Premature Infant; Publishing; Reading; Recruitment Activity; Reference Standards; Research; Research Personnel; Research Project Grants; Retinal; Retinopathy of Prematurity; Risk; Risk Factors; Sampling; Study Subject; Testing; United States; Work; angiogenesis; base; biomedical informatics; clinical imaging; clinical phenotype; clinical risk; cohort; computer science; data access; data integration; diagnosis standard; disorder risk; exome; genetic analysis; genome-wide; high risk; imaging genetics; imaging informatics; improved; indexing; multidisciplinary; neovascular; neovascularization; novel; phenotypic data; prototype; public health relevance; quantitative imaging; rare variant; tool; trait

 DESCRIPTION (provided by applicant): The long-term goal of this project is to identify clinical and genetic features of retinopathy of prematurity (ROP) development, and to analyze their relationships. Although biomedical research data are being generated at an enormous pace, much less work has been done to integrate disparate scientific findings across the spectrum from genomics to imaging to clinical medicine. Our overall hypotheses are that genetic factors are involved in the initiation and modulation of ROP, and that analysis of relationships among clinical, imaging, and genetic findings in ROP using bioinformatics approaches will improve understanding of disease pathogenesis and diagnosis. These hypotheses will be tested using three Specific Aims: (1) Recruit, phenotype, and collect genetic material from ~600 additional premature infants at-risk for ROP from 8 centers. This renewal will continue work from the original project, to obtain a total cohort size of >1600 infants for genetic analysis. Demographic and clinical features from serial ophthalmic examinations will be ascertained fully, retinal images will be captured, rigorous reference standards will be established, and blood samples will be collected. (2) Perform imaging and informatics analysis of this cohort. This will: (a) Create quantitative indices for computer- based diagnosis of severe ROP. Features from automated image analysis that best correlate with severe ROP and "plus disease" will be identified, and quantitative indices will be defined and validated for clinical use. (b) Combine this into disease prediction models incorporating the effects of quantitative image traits, clinical features, and environmental risk factors on ROP susceptibility. (3) Perform genetic and bioinformatics analysis of this cohort. This will: (a) analyze exome sequence data (previously obtained) from 100 phenotypically extreme subjects that were also preferentially enriched as being dizygotic twin pairs, to identify rare variants; (b) perform and analyze genome-wide genotyping to test against clinical and imaging findings, testing specific candidate genes related to vascular pathology in the eye and the vasculature at large, followed by performing a pathway-based analysis; and (c) perform bioinformatics analysis to combine findings from (3a) and (3b) in relation to the clinical findings. Ultimately, these studies should improve understanding of neovascularization in ROP and related ocular diseases, and of normal vascular development in infants. In addition, this work should demonstrate a prototype for health information management which combines genotypic and phenotypic data. This project will be performed by a multi-disciplinary team of collaborative investigators with expertise in ophthalmology, biomedical informatics, computer science, computational biology, ophthalmic genetics, genetic analysis, and statistical genetics.

 描述(申请人提供)：本项目的长期目标是确定早产儿视网膜病变(ROP)发育的临床和遗传特征，并分析它们之间的关系。尽管生物医学研究数据正在以巨大的速度产生，但在整合从基因组学到成像再到临床医学的不同科学发现方面所做的工作要少得多。我们的总体假设是，遗传因素参与了ROP的启动和调节，使用生物信息学方法分析ROP的临床、影像和遗传学表现之间的关系将有助于提高对疾病发病机制和诊断的理解。这些假设将通过三个具体目标进行验证：(1)从8个中心招募、表型和收集另外600名有ROP风险的早产儿的遗传物质。这次更新将继续原来项目的工作，获得1600名婴儿的总队列大小，用于基因分析。一系列眼科检查的人口学和临床特征将被完全确定，视网膜图像 将被捕获，将建立严格的参考标准，并将采集血样。(2)对该队列进行影像和信息学分析。这将：(A)为基于计算机的严重ROP诊断创建量化指标。将从自动图像分析中确定与严重ROP和“加重病”最相关的特征，并将定义和验证临床使用的量化指标。(B)将这一点结合到疾病预测模型中，纳入量化图像特征、临床特征和环境风险因素对ROP易感性的影响。(3)对该队列进行遗传和生物信息学分析。这将：(A)分析100名表型极端受试者的外显子组序列数据(以前获得)，这些受试者也优先被浓缩为双卵双胞胎，以确定罕见的变异；(B)执行和分析全基因组基因分型，以对照临床和成像结果进行测试，测试与眼睛和整个血管系统相关的特定候选基因，然后进行基于途径的分析；以及(C)进行生物信息学分析，以结合(3a)和(3b)与临床发现相关的结果。最终，这些研究将提高对ROP和相关眼部疾病中新生血管形成以及婴儿正常血管发育的理解。此外，这项工作应该展示一个结合了基因和表型数据的健康信息管理原型。该项目将由一个由多学科合作研究人员组成的团队执行，该团队具有眼科、生物医学信息学、计算机科学、计算生物学、眼科遗传学、遗传分析和统计遗传学方面的专业知识。