Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease

将神经影像、多组学和临床数据整合到复杂疾病中

• 批准号: 9287487
• 负责人: Dokyoon Kim
• 金额: $ 36.71万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9287487
• 关键词: Address; Advanced Development; Aging; Alzheimer' s Disease; Beds; Behavioral; Big Data; Biological; Biological Markers; Brain; Clinical; Clinical Data; Cohort Studies; Complex; Data; Data Set; Detection; Development; Diagnostic tests; Discipline; Disease; Disease Progression; Evaluation; Genes; Genetic; Genetic Variation; Genomics; Genotype; Health; Heterogeneity; Indiana; Individual; Informatics; Knowledge; Machine Learning; Magnetic Resonance Imaging; Mediating; Mediation; Medical Imaging; Memory; Meta-Analysis; Methods; Modeling; Molecular; Multivariate Analysis; Nerve Degeneration; Neurodegenerative Disorders; Outcome; Phenotype; Positron-Emission Tomography; Proteomics; Public Health; Science; Statistical Methods; Structure; Susceptibility Gene; Technology; Testing; Time; Validation; Variant; base; biomedical informatics; cohort; data integration; diagnostic biomarker; disease classification; endophenotype; epigenomics; genetic association; genetic variant; high dimensionality; high risk; improved; insight; interest; learning strategy; mathematical model; metabolomics; multimodality; neuroimaging; new therapeutic target; novel; novel diagnostics; predict clinical outcome; rare variant; risk variant; therapeutic target; transcriptomics; user friendly software

ABSTRACT Rapid progress in biomedical informatics has generated massive high-dimensional data sets (“big data”), ranging from clinical information and medical imaging to genomic sequence data. The scale and complexity of these data sets hold great promise, yet present substantial challenges. To fully exploit the potential informativeness of big data, there is an urgent need to find effective ways to integrate diverse data from different levels of informatics technologies. Existing approaches and methods for data integration to date have several important limitations. In this project, we propose novel statistical methods and strategies to integrate neuroimaging, multi-omics, and clinical/behavioral data sets. To increase power for association analysis compared to existing methods, we propose a novel multi-phenotype multi-variant association method that can evaluate the cumulative effect of common and rare variants in genes or regions of interest, incorporate prior biological knowledge on the multiple phenotype structure, identify associated phenotypes among multiple phenotypes, and be computationally efficient for high-dimensional phenotypes. To improve the prediction of clinical outcomes, we propose a novel machine learning strategy that can integrate multimodal neuroimaging and multi-omics data into a mathematical model and can incorporate prior biological knowledge to identify genomic interactions associated with clinical outcomes. The ongoing Alzheimer's Disease Neuroimaging Initiative (ADNI) and Indiana Memory and Aging Study (IMAS) projects as a test bed provide a unique opportunity to evaluate/validate the proposed methods. Specific Aims: Aim 1: to develop powerful statistical methods for multivariate tests of associations between multiple phenotypes and a single genetic variant or set of variants (common and rare) in regions of interest, and to develop methods for mediation analysis to integrate neuroimaging, genetic, and clinical data to test for direct and indirect genetic effects mediated through neuroimaging phenotypes on clinical outcomes; Aim 2: to develop a novel multivariate model that combines multi-omics and neuroimaging data using a machine learning strategy to predict individuals with disease or those at high-risk for developing disease, and to develop a novel multivariate model incorporating prior biological knowledge to identify genomic interactions associated with clinical outcomes; Aim 3: to evaluate and validate the proposed methods using real data from the ADNI and IMAS cohorts; and Aim 4: to disseminate and support publicly available user-friendly software that efficiently implements the proposed methods. RELEVANCE TO PUBLIC HEALTH: Alzheimer's disease (AD) as an exemplar is an increasingly common progressive neurodegenerative condition with no validated disease modifying treatment. The proposed multivariate methods are likely to help identify novel diagnostic biomarkers and therapeutic targets for AD. Identifying new susceptibility loci/biomarkers for AD has important implications for gaining greater insight into the molecular mechanisms underlying AD.

抽象的 生物医学信息的快速进步产生了大量的高维数据集（“大数据”）， 从临床信息和医学成像到基因组序列数据。规模和复杂性 在这些数据集中，有很大的希望，但出现了重大挑战。充分探索潜力 大数据的信息性，迫切需要寻找有效的方法来整合来自 不同水平的信息技术。迄今为止的现有方法和数据集成方法 有几个重要的局限性。在这个项目中，我们提出了新颖的统​​计方法和策略 综合神经影像学，多媒体和临床/行为数据集。增加关联的力量 与现有方法相比，我们提出了一种新型的多型多变量关联 可以评估普通和稀有变体在感兴趣的基因或感兴趣区域的累积效应的方法， 在多种表型结构上纳入了先前的生物学知识，识别相关的表型 在多种表型中，对于高维表型具有计算效率。改进 临床结果的预测，我们提出了一种新颖的机器学习策略，可以整合 多模式的神经影像学和多媒体数据中的数学模型，可以合并先验 生物学知识以识别与临床结果相关的基因组相互作用。正在进行的 阿尔茨海默氏病神经影像学倡议（ADNI）和印第安纳州记忆与老化研究（IMAS）项目 作为测试床提供了一个独特的机会来评估/验证所提出的方法。具体目的：目标1： 开发强大的统计方法，用于多种表型之间关联的多元测试 以及感兴趣的区域中的单个遗传变异或一组变体（常见和稀有），并发展 调解分析的方法将神经影像学，遗传和临床数据整合到直接和 通过神经影像型对临床结果介导的间接遗传作用；目标2：发展 一种新型的多元模型，使用机器学习结合了多词和神经影像学数据 预测患有疾病的人或高危疾病的策略，并发展 新颖的多元模型编码先前的生物学知识以识别相关的基因组相互作用 临床结果；目标3：使用ADNI的实际数据评估和验证提出的方法 和Imas同伙；目标4：传播和支持公开可用的用户友好软件 有效地实现了所提出的方法。与公共卫生有关：阿尔茨海默氏病 （AD）作为示例是一种日益常见的进行性神经退行性条件，未经验证 疾病修改治疗。提出的多元方法可能有助于识别新型诊断 AD的生物标志物和治疗靶标。确定广告的新敏感性局部/生物标志物 对AD基础的分子机制有更深入地了解的重要意义。