Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics

项目摘要(摘要) 人脑连接组学和成像基因组学是近年来出现的两个新兴研究领域 多模式神经成像和高通量组学技术的进展。整合脑部成像 基因组学和连接学为人类大脑的系统表征带来了巨大的希望 连通性及基于连通性的神经生物学途径--从遗传结构到其影响 关于认知和行为。丰富的多模式神经成像数据与高密度组学数据相结合 可从大型里程碑式研究中获得，例如NIH人类连接组项目(HCP)和 阿尔茨海默病神经成像倡议(ADNI)。这些数据前所未有的规模和复杂性 然而，集合带来了严重的计算瓶颈，需要新的概念和使能工具。 为了弥补这一差距，本项目提出了开发和验证新的集成生物信息学 人类大脑基因组学和连接组学的方法，并有三个目标。目标1是开发一部小说 用于成像优化的结构连接体的系统表征的计算流水线 基因组学，其中将特别考虑解决重要问题，包括可靠的纤维束成像 和网络建设、网络属性的系统提取、重要网络的识别 组件(例如，枢纽、社区和富人俱乐部)，针对基因组的网络属性的优先顺序 分析和确定与成果相关的网络措施。目标2是开发新的生物信息学 确定结构连接体遗传基础的策略，包括新的分析方法 基于图形的表型数据和学习结果相关的关联，以及有效的 学习模块，以处理挖掘基因组-连接组关联的一组全面方案，请访问 全基因组连接全基因组的规模。目标3是开发一个交互式的可视化分析软件系统 纤维束和脑网络及其遗传决定因素和功能的可视化探索和挖掘 结果，将实施新的可视化和探索方法，以实现无缝结合 人类专业知识和机器智能，以实现具有背景意义的新发现。 该项目有望产生新的生物信息学算法和工具，用于全面联合 大规模基因组学和连接学数据的分析。这些强大的方法和工具的可用性 对于主要连接学和成像基因组学计划的全面知识发现和开发至关重要 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 对连接组和基因组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 对更好地理解基因、大脑连接和基因之间的相互作用具有相当大的潜力 因此，预计将对整个生物医学研究产生影响，并使公共卫生成果受益。

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.