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 生物医学信息学的快速进步产生了海量的高维数据集（“大数据”）， 范围从临床信息和医学成像到基因组序列数据。规模和复杂性 这些数据集蕴藏着巨大的希望，但也带来了巨大的挑战。充分挖掘潜力 大数据的信息量巨大，迫切需要找到有效的方法来整合来自不同领域的各种数据。 不同水平的信息技术。迄今为止现有的数据集成途径和方法 有几个重要的限制。在这个项目中，我们提出了新的统计方法和策略 整合神经影像、多组学和临床/行为数据集。增强协会力量 与现有方法相比，我们提出了一种新的多表型多变异关联 可以评估基因或感兴趣区域中常见和罕见变异的累积效应的方法， 结合先前关于多种表型结构的生物学知识，识别相关的表型 在多个表型之间，并且对于高维表型具有计算效率。改善 为了预测临床结果，我们提出了一种新颖的机器学习策略，可以集成 将多模态神经影像和多组学数据整合到一个数学模型中，并且可以合并先前的 生物学知识来识别与临床结果相关的基因组相互作用。正在进行的 阿尔茨海默病神经影像计划 (ADNI) 和印第安纳州记忆与衰老研究 (IMAS) 项目 作为测试平台，提供了评估/验证所提出的方法的独特机会。具体目标： 目标 1： 开发强大的统计方法来对多种表型之间的关联进行多变量测试 以及感兴趣区域中的单个遗传变异或一组变异（常见和罕见），并开发 中介分析方法整合神经影像、遗传和临床数据以测试直接和 通过神经影像表型介导的间接遗传效应对临床结果；目标2：发展 一种新颖的多变量模型，使用机器学习结合多组学和神经影像数据 预测患有疾病或处于患病高风险的个体的策略，并制定 新颖的多变量模型结合了先前的生物学知识来识别相关的基因组相互作用 具有临床结果；目标 3：使用 ADNI 的真实数据评估和验证所提出的方法 和 IMAS 队列；目标 4：传播和支持公开的用户友好型软件 有效地实施所提出的方法。与公众健康的相关性：阿尔茨海默病 (AD) 作为一个例子是一种越来越常见的进行性神经退行性疾病，没有经过验证的 疾病改变治疗。所提出的多变量方法可能有助于识别新的诊断方法 AD 的生物标志物和治疗靶点。识别 AD 新的易感位点/生物标志物 对于更深入地了解 AD 的分子机制具有重要意义。