Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics

人脑基因组学和连接组学的综合生物信息学方法

• 批准号: 9482419
• 负责人: LI SHEN
• 金额: $ 45.58万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9482419
• 关键词: Address; Algorithmic Software; Alzheimer' s Disease; Area; Artificial Intelligence; Beds; Behavior; Behavior Disorders; Bioinformatics; Biological; Biomedical Research; Brain; Brain Diseases; Brain imaging; Cognition; Communities; Complex; Coupled; Data; Data Set; Diagnostic; Disease; Evaluation; Fiber; Functional disorder; Genes; Genetic; Genetic Determinism; Genome; Genomics; Graph; Human; Image; Imagery; Individual; Joints; Knowledge Discovery; Learning; Learning Module; Machine Learning; Measures; Methods; Mining; Modality; Modeling; Nervous system structure; Neurobiology; Outcome; Pathway interactions; Pattern; Phenotype; Property; Public Health; Research; Sample Size; Single Nucleotide Polymorphism; Software Tools; Structure; Systems Biology; Technology; Testing; Therapeutic; United States National Institutes of Health; Visual; base; connectome; cost; data acquisition; density; functional outcomes; genetic architecture; genome-wide; genomic data; high dimensionality; improved; innovation; insight; interest; learning outcome; learning strategy; multimodality; neuroimaging; novel; novel strategies; phenotypic data; public health relevance; software systems; tool; tractography; trait; white matter; whole genome

Project Summary (Abstract) Human brain connectomics and imaging genomics are two emerging research fields enabled by recent advances in multi-modal neuroimaging and high throughput omics technologies. Integrating brain imaging genomics and connectomics holds great promise for a systematic characterization of both the human brain connectivity and the connectivity-based neurobiological pathway from its genetic architecture to its influences on cognition and behavior. Rich multi-modal neuroimaging data coupled with high density omics data are available from large-scale landmark studies such as the NIH Human Connectome Project (HCP) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The unprecedented scale and complexity of these data sets, however, have presented critical computational bottlenecks requiring new concepts and enabling tools. To bridge the gap, this project is proposed to develop and validate novel integrative bioinformatics approaches to human brain genomics and connectomics, and has three aims. Aim 1 is to develop a novel computational pipeline for a systematic characterization of structural connectome optimized for imaging genomics, where special consideration will be taken to address important issues including reliable tractography and network construction, systematic extraction of network attributes, identification of important network components (e.g., hubs, communities and rich clubs), prioritization of network attributes towards genomic analysis, and identification of outcome-relevant network measures. Aim 2 is to develop novel bioinformatics strategies to determining genetic basis of structural connectome, including novel approaches for analyzing graph-based phenotype data and learning outcome-relevant associations, and an ensemble of effective learning modules to handle a comprehensive set of scenarios on mining genome-connectome associations at the genome-wide connectome-wide scale. Aim 3 is to develop a visual analytic software system for interactive visual exploration and mining of fiber-tracts and brain networks with their genetic determinants and functional outcomes, where new visualization and exploration methods will be implemented for seamlessly combining human expertise and machine intelligence to enable novel contextually meaningful discoveries. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive joint analysis of large scale genomics and connectomics data. The availability of these powerful methods and tools is critical for full knowledge discovery and exploitation of major connectomics and imaging genomics initiatives such as HCP and ADNI. In addition, they can also help enable new computational applications in many other biomedical research areas where integrative analysis of connectomics and genomics data are of interest. Via thorough test and evaluation on HCP and ADNI data, these methods and tools will be demonstrated to have considerable potential for a better understanding of the interplay between genes, brain connectivity and function, and thus be expected to impact biomedical research in general and benefit public health outcomes.

项目概要（摘要） 人脑连接组学和成像基因组学是最近的两个新兴研究领域 多模式神经影像和高通量组学技术的进步。整合脑成像 基因组学和连接组学为人类大脑的系统表征带来了巨大希望 连接性和基于连接性的神经生物学途径从其遗传结构到其影响 关于认知和行为。丰富的多模态神经影像数据与高密度组学数据相结合 可从大规模的里程碑式研究中获得，例如 NIH 人类连接组计划 (HCP) 和 阿尔茨海默病神经影像倡议 (ADNI)。这些数据的规模和复杂性前所未有 然而，集合已经出现了关键的计算瓶颈，需要新的概念和支持工具。 为了弥补这一差距，该项目旨在开发和验证新型综合生物信息学 人类大脑基因组学和连接组学的方法，具有三个目标。目标 1 是开发一部小说 用于对成像优化的结构连接组进行系统表征的计算管道 基因组学，将特别考虑解决重要问题，包括可靠的纤维束成像 和网络构建，系统提取网络属性，识别重要网络 组件（例如，中心、社区和丰富的俱乐部）、网络属性对基因组的优先顺序 分析和确定与结果相关的网络措施。目标 2 是开发新型生物信息学 确定结构连接组遗传基础的策略，包括新的分析方法 基于图表的表型数据和学习成果相关的关联，以及有效的集合 用于处理挖掘基因组-连接组关联的一组全面场景的学习模块 全基因组连接组范围的规模。目标3是开发一个用于交互的视觉分析软件系统 纤维束和大脑网络及其遗传决定因素和功能的视觉探索和挖掘 结果，将实施新的可视化和探索方法以无缝结合 人类的专业知识和机器智能能够实现新颖的、有意义的发现。 该项目预计将产生用于综合联合的新型生物信息学算法和工具 大规模基因组学和连接组学数据分析。这些强大的方法和工具的可用性 对于主要连接组学和成像基因组学计划的全面知识发现和利用至关重要 例如 HCP 和 ADNI。此外，它们还可以帮助在许多其他领域实现新的计算应用程序 对连接组学和基因组学数据进行综合分析的生物医学研究领域。通过 对 HCP 和 ADNI 数据进行彻底的测试和评估，这些方法和工具将被证明具有 更好地理解基因、大脑连接和神经元之间的相互作用具有巨大的潜力 功能，因此有望影响一般生物医学研究并有益于公共卫生结果。