Informatics Algorithms for Genomic Analysis of Brain Imaging Data

用于脑成像数据基因组分析的信息学算法

• 批准号: 10206271
• 负责人: Jason H. Moore
• 金额: $ 33.56万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10206271
• 关键词: Address; Algorithms; Alzheimer' s Disease; Atlases; Awareness; Beds; Biological; Brain; Brain Diseases; Brain imaging; Characteristics; Collection; Complex; Data; Data Set; Development; Diagnostic; Evaluation; Gene Expression; Genes; Genetic; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Human; Informatics; Knowledge; Link; Linkage Disequilibrium; Measures; Mental disorders; Methods; Mining; Modeling; Molecular; Multiomic Data; Nature; Neurobiology; Neurologic; Outcome; Pattern; Phenotype; Proteome; Public Health; Quantitative Trait Loci; Research; Software Tools; Source; Structure; System; Systems Biology; Testing; Therapeutic; Time; Tissues; Validation; cohort; data repository; design; epigenome; field study; gene expression database; genetic association; genome-wide; image guided; imaging genetics; in vivo; innovation; insight; knowledge repository; metabolome; multimodality; nervous system disorder; neurobiological mechanism; novel; open source; preference; quantitative imaging; simulation; tool; trait; transcriptome; transcriptomics

Project Summary Brain imaging genetics studies the relationship between genetic variations and brain imaging quantitative traits (QTs) and offers enormous potential to reveal the genetic underpinning of the neurobiological system that can impact the development of diagnostic, therapeutic and preventative approaches for complex brain disorders. Two critical gaps limiting the progress of brain imaging genetics include (1) the unprecedented scale and complexity of the imaging genetic data sets, and (2) lack of intermediate-level omics data to capture the molecular effects linking genetics to brain QTs. Our prior studies have contributed substantially to addressing the first gap. The proposed project will develop new informatics strategies to bridge the second gap, where valuable existing data in the omics domain will be leveraged to link brain imaging and genetics. In this project, we will focus on transcriptomics, and will make use of major transcriptomics data repositories including Genotype-Tissue Expression (GTEx) Project, UK Brain Expression Consortium (UKBEC), and Allen Human Brain Atlas (AHBA). Our overarching goal is to identify brain imaging genetic associations with evidence manifested in the human brain transcriptome. Our hypothesis is that, with additional source of evidence at the transcriptomic level, the identified brain imaging genetic associations are biologically more meaningful and less likely to be false positives. To achieve our goal, we propose four aims. Aim 1 is to develop novel bi-multivariate models incorporating regional tissue-specific expression quantitative trait locus (eQTL) knowledge for mining brain imaging genetic associations. Given that eQTL is a source of tissue-specific evidence to link genotype, gene expression, and brain QTs, we will develop novel eQTL-guided bi-multivariate models to identify imaging genetic associations potentially evidenced by regional tissue-specific eQTL knowledge. Aim 2 is to develop novel bi-multivariate models incorporating brain-wide genome-wide (BWGW) cross-domain co-expression patterns for mining brain imaging genetics associations. AHBA, a BWGW gene expression database, is a natural connection between genome and brain. We propose to develop novel biclustering and bi-multivariate methods to identify meaningful AHBA modules with cross-domain co-expression patterns, and use these patterns to guide the search for co-expression-aware associations between genetic variations and multimodal brain imaging measures. Aim 3 is to develop open source software tools for structure-aware mining of brain imaging genetic associations. Aim 4 is to perform evaluation and validation on both simulated data and real imaging genetics cohorts. Successful completion of the above aims will produce innovative informatics methods and tools for integrative analysis of imaging, genetics and transcriptomics data to address a critical barrier in brain imaging genetics. Using ADNI and related cohorts as test beds, these methods and tools will be shown to have considerable potential for understanding the molecular mechanism of Alzheimer’s disease, and be expected to impact neurological and psychiatric research in general and benefit public health outcomes.

项目摘要 脑成像遗传学研究遗传变异与脑成像定量的关系 它提供了巨大的潜力来揭示神经生物学系统的遗传基础， 可能会影响复杂大脑的诊断、治疗和预防方法的发展， 紊乱限制脑成像遗传学进展的两个关键差距包括：（1）前所未有的规模 和成像遗传数据集的复杂性，以及（2）缺乏中间水平的组学数据来捕获 将遗传学与大脑QT联系起来的分子效应。我们先前的研究为解决这一问题做出了重大贡献。 第一个差距。拟议的项目将制定新的信息学战略，以弥合第二个差距， 将利用组学领域现有的宝贵数据将脑成像和遗传学联系起来。在这个项目中， 我们将专注于转录组学，并将利用主要的转录组学数据库，包括 基因型-组织表达（GTEx）项目、英国脑表达联盟（UKBEC）和艾伦人类 脑图谱（AHBA）。我们的首要目标是确定大脑成像的遗传关联与证据 表现在人脑转录组中。我们的假设是，有了更多的证据来源， 在转录组水平上，所确定的脑成像遗传关联在生物学上更有意义， 很可能是假阳性。为了实现我们的目标，我们提出了四个目标。目的1是开发新的双多元 整合区域组织特异性表达数量性状基因座（eQTL）知识的模型用于挖掘 脑成像遗传关联。鉴于eQTL是连接基因型的组织特异性证据的来源， 基因表达和大脑QT，我们将开发新的eQTL指导的双多变量模型来识别成像 遗传关联可能由区域组织特异性eQTL知识证明。目标二：发展 结合全脑基因组（BWGW）跨域共表达的新型双多变量模型 用于挖掘大脑成像遗传学关联的模式。AHBA是一个BWGW基因表达数据库， 基因组和大脑之间的天然联系。我们建议开发新的双聚类和双多变量 方法来识别有意义的AHBA模块与跨域共表达模式，并使用这些 模式，以指导遗传变异和多模态之间的共表达意识的关联搜索 脑成像测量。目标3是开发用于大脑结构感知挖掘的开源软件工具 想象遗传关联。目的4是对模拟数据和真实的数据进行评估和验证 成像遗传学队列。成功地完成上述目标将产生创新的信息学 用于综合分析成像、遗传学和转录组学数据的方法和工具， 脑成像遗传学的障碍。使用ADNI和相关队列作为测试床，这些方法和工具将 显示出在了解阿尔茨海默病的分子机制方面具有相当大的潜力， 预计将影响神经学和精神病学研究，并有利于公共卫生成果。