Informatics Algorithms for Genomic Analysis of Brain Imaging Data

项目摘要 脑成像遗传学研究基因变异与脑成像定量之间的关系 特征(QT)，并为揭示神经生物学系统的遗传基础提供了巨大的潜力 可能会影响复杂脑的诊断、治疗和预防方法的发展 精神错乱。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捉 将遗传学与大脑QTS联系起来的分子效应。我们之前的研究在很大程度上有助于解决 第一个缺口。拟议的项目将制定新的信息学战略，以弥合第二个差距，其中 组学领域中有价值的现有数据将被用来将大脑成像和遗传学联系起来。在这个项目中， 我们将专注于转录学，并将利用主要的转录学数据存储库，包括 基因-组织表达(GTEx)项目、英国脑表达联盟(UKBEC)和Allen Human 脑图谱(AHBA)。我们的首要目标是确定脑成像基因与证据之间的联系 在人脑转录组中表现出来。我们的假设是，有了额外的证据来源 转录水平，已确定的脑成像遗传关联在生物学上更有意义，而不是 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。目标1是开发新的双多变量 包含区域组织特异性表达数量性状基因座(EQTL)知识的挖掘模型 脑成像基因关联。鉴于eQTL是联系基因的组织特异性证据的来源， 基因表达和脑QTS，我们将开发新的eQTL引导的双多变量模型来识别成像 遗传关联可能被区域组织特有的eQTL知识所证明。目标2是发展 结合全脑全基因组(BWGW)跨域共表达的新型双多变量模型 挖掘脑成像遗传学关联的模式。AHBA是一个BWGW基因表达数据库，是一个 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元 使用跨域共表达模式识别有意义的AHBA模块的方法，并使用这些 指导在遗传变异和多模式之间搜索共同表达感知关联的模式 脑部成像测量。目标3是开发用于脑结构感知挖掘的开源软件工具 想象遗传关联。目标4是对模拟数据和真实数据进行评估和验证 成像遗传学队列。上述目标的成功实现将产生创新的信息学 用于综合分析成像、遗传学和转录组数据的方法和工具，以解决关键的 脑成像遗传学中的障碍。使用ADNI和相关队列作为试验台，这些方法和工具将被 被证明有相当大的潜力来理解阿尔茨海默病的分子机制，以及 预计将对神经学和精神病学研究产生普遍影响，并使公共卫生成果受益。

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.