Bioinformatics Strategies for Multidimensional Brain Imaging Genetics

描述（由申请人提供）： 当今一代的多模态成像系统产生了大量的高维数据集，当这些数据集与高通量基因分型数据（例如单核苷酸多态性（SNP））相结合时，为增强我们对人类疾病的表型特征和遗传结构的理解提供了令人兴奋的机会。然而，这些数据集前所未有的规模和复杂性已经提出了关键的计算瓶颈，需要新的概念和使能工具。为了应对这些挑战，利用阿尔茨海默病（AD）的研究作为试验平台，该项目将开发和验证多维脑成像遗传学的新生物信息学策略。目的一是建立一种新的双变量分析策略S3 K-CCA，用于研究影像学遗传关联。现有的成像遗传学方法通常被设计为发现单SNP-单QT、单SNP-多QT或多SNP-单QT关联，并且在揭示互连的遗传标志物和相关的脑表型之间的复杂关系方面具有有限的能力。为了克服这一限制，S3 K-CCA被设计为稀疏双多变量学习模型，其同时使用具有多个预测因子的多个响应变量来分析大规模多模态神经基因组数据。目的2是开发HD-BIG，一个可视化和系统生物学框架，用于高维脑成像遗传学数据的综合分析。无缝整合有价值的领域知识以产生具有生物学意义的结果的机器学习策略仍然是成像遗传学中一个未充分探索的领域。在这个目标中，我们将开发一个用户友好的热图界面来可视化高维结果， 调整学习参数和策略，与现有的生物信息学资源和工具进行交互，并促进可视化探索和系统生物学分析。将开发一种新的成像遗传富集分析（IGEA）方法，以识别相关的遗传通路和相关的脑回路，并揭示它们之间的复杂关系。目的3是使用模拟和真实的成像遗传学数据评估所提出的S3 K-CCA和IGEA方法以及HD-BIG框架。该项目有望产生新的生物信息学算法和工具，用于大规模异质成像遗传学数据的综合联合分析。这些强大方法的可用性对于许多成像遗传学计划的成功至关重要。此外，它们还可以帮助在生物医学研究的其他领域实现新的计算应用，在这些领域中，对大规模多模态数据进行系统和综合分析至关重要。以AD为例，所提出的方法将展示增强对复杂疾病的机制理解的潜力，这可以通过促进诊断和治疗进展来有益于公共卫生结果。

DESCRIPTION (provided by applicant): Today's generation of multi-modal imaging systems produces massive high dimensional data sets, which when coupled with high throughput genotyping data such as single nucleotide polymorphisms (SNPs), provide exciting opportunities to enhance our understanding of phenotypic characteristics and the genetic architecture of human diseases. However, the unprecedented scale and complexity of these data sets have presented critical computational bottlenecks requiring new concepts and enabling tools. To address these challenges, using the study of Alzheimer's disease (AD) as a test bed, this project will develop and validate novel bioinformatics strategies for multidimensional brain imaging genetics. Aim 1 is to develop a novel bi- multivariate analysis strategy, S3K-CCA, for studying imaging genetic associations. Existing imaging genetics methods are typically designed to discover single-SNP-single-QT, single-SNP-multi-QT or multi-SNP-single- QT associations, and have limited power in revealing complex relationships between interlinked genetic markers and correlated brain phenotypes. To overcome this limitation, S3K-CCA is designed to be a sparse bi- multivariate learning model that simultaneously uses multiple response variables with multiple predictors for analyzing large-scale multi-modal neurogenomic data. Aim 2 is to develop HD-BIG, a visualization and systems biology framework for integrative analysis of High-Dimensional Brain Imaging Genetics data. Machine learning strategies to seamlessly incorporate valuable domain knowledge to produce biologically meaningful results is still an under-explored area in imaging genetics. In this aim, we will develop a user-friendly heat map interface to visualize high-dimensional results, adjust learning parameters and strategies, interact with existing bioinformatics resources and tools, and facilitate visual exploratory and systems biology analysis. A novel imaging genetic enrichment analysis (IGEA) method will be developed to identify relevant genetic pathways and associated brain circuits, and to reveal complex relationships among them. Aim 3 is to evaluate the proposed S3K-CCA and IGEA methods and the HD-BIG framework using both simulated and real imaging genetics data. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive joint analysis of large scale heterogeneous imaging genetics data. The availability of these powerful methods is critical to the success of many imaging genetics initiatives. In addition, they can also help enable new computational applications in other areas of biomedical research where systematic and integrative analysis of large-scale multi-modal data is critical. Using AD as an exemplar, the proposed methods will demonstrate the potential for enhancing mechanistic understanding of complex disorders, which can benefit public health outcomes by facilitating diagnostic and therapeutic progress.