Integrative analysis of genomics and imaging data from the BRAIN Initiative and other public data sources

对来自 BRAIN Initiative 和其他公共数据源的基因组学和成像数据进行综合分析

基本信息

批准号：
10190025
负责人：
Mark Bender Gerstein
金额：
$ 130.99万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10190025
关键词：
Address Adult Affective Archives Atlases Autopsy BRAIN initiative Behavioral Biological Brain Brain Diseases Brain region Cells Clinical Research Cognitive Collection Data Data Analyses Data Set Data Sources Deposition Discipline Exhibits Functional Imaging Future Genetic Genetic Epistasis Genetic Models Genetic Variation Genomics Genotype-Tissue Expression Project Goals Grain Heritability Human Image Joints Knowledge Lead Learning Length Link Magnetic Resonance Imaging Measures Mental disorders Metadata Methods Modeling Molecular Molecular Genetics Network-based Neurosciences Pattern Phenotype Polygenic Traits Process Regulator Genes Research Research Personnel Resolution Resources Risk Structural Models Structure Techniques Theoretical model Thick Time Tissues Twin Multiple Birth Variant Work base behavior influence biobank brain cell cell type connectome data harmonization data integration deep learning design functional genomics genetic predictors genetic variant genome wide association study genomic data high dimensionality human disease improved in vivo insight interest method development model building multilevel analysis neural circuit neuroimaging phenomics phenotypic data predictive modeling psychologic repository secondary analysis trait

项目摘要

Constructing an integrated picture of human brain function requires understanding how the effects of molecular and genetic factors propagate upwards, through many intervening layers of structure and interaction, to influence behavioral, psychiatric and cognitive traits. Projects such as the BRAIN Initiative (BI) recognize that building such a picture requires the convergent efforts of experts across genetics, genomics, neuroscience, and clinical studies, and have created resources to aid the integration of data from these disciplines. However, the challenge of combining experimental methods and theoretical models spanning vast length/time scales remains significant. One of the more promising avenues of addressing this challenge is the use of interpretable deep-learning approaches to learn high-dimensional structure inherent in data. By embedding constraints from known biological structure, investigators can relate the models’ internal representations to identifiable factors from neuroscience. This proposal will draw on the extensive resources in BI archives, along with other public resources, to integrate data from genetics, functional genomics, and neuroimaging. Through secondary analysis on this data we will build deep, multilevel polygenic models of high-level traits, such as cognitive, affective and psychiatric traits. We will trace the mechanisms underlying such traits to specific regions, cell types, functional connectivity patterns and structural imaging features. Additionally, by embedding biological structure at intermediate levels (tissue and cell-type gene regulatory networks; structural/functional constraints from MRI data), we will build models that improve on additive heritability measures of polygenic risk. In the process, we will harmonize BI data with other publicly available brain omics and imaging datasets. We will deposit all resources and models into relevant BI archives. The proposal is framed as follows. First, we will combine genetics with genomics-based networks from multiple brain regions and cell types, and develop predictive models of region- and cell-type-specific omics variation. These will be included in an interpretable deep model of cognitive and psychiatric traits (Aim 1). Second, we will learn predictive models of structural and functional imaging features from genetic predictors, which will likewise be embedded in interpretable deep models of high-level traits (Aim 2). Third, an integrated, polygenic model will be built by combining both functional-genomics- and neuroimaging-based features, allowing the impact of both subcomponents to be assessed. Furthermore, we will extend our previous work to develop compression-based interpretability methods, which allow a network to be coarse-grained and interpreted at varying levels of resolution. Such interpretation will include the exploration of subphenotypic structure in psychiatric disorders and interactions between traits (Aim 3). We expect the proposed approach to have wide-ranging implications, including insights into mechanistic underpinnings of brain function, new frameworks for integrative multilevel analysis, and the development of methods and resources for future research.

构建人类大脑功能的整体图景需要了解分子的影响如何遗传因素通过结构和相互作用的许多介入层向上传播， BRAIN Initiative (BI) 等项目认识到这一点。构建这样一幅图景需要遗传学、基因组学、神经科学、和临床研究，并创建了资源来帮助整合这些学科的数据。将跨越巨大长度/时间尺度的实验方法和理论模型相结合的挑战解决这一挑战的更有希望的途径之一是使用可解释的。通过嵌入约束来学习数据固有的高维结构的深度学习方法。已知的生物结构，研究人员可以将模型的内部表征与可识别的因素联系起来该提案将利用 BI 档案以及其他公共资源中的广泛资源。资源，通过二次整合来自遗传学、功能基因组学和神经影像学的数据。通过对这些数据的分析，我们将构建高级特征的深层、多级多基因模型，例如认知、我们将追踪这些特征背后的特定区域、细胞的机制。此外，还通过嵌入生物来识别类型、功能连接模式和结构成像特征。中间水平的结构（组织和细胞类型基因调控网络；结构/功能限制来自 MRI 数据），我们将建立模型来改进多基因风险的附加遗传性测量。在此过程中，我们将协调 BI 数据与其他公开可用的脑组学和成像数据集。将所有资源和模型存入相关 BI 档案。首先，我们将。将遗传学与来自多个大脑区域和细胞类型的基于基因组学的网络相结合，并开发区域和细胞类型特异性组学变异的预测模型将包含在可解释的模型中。认知和精神特征的深层模型（目标 1）其次，我们将学习结构和精神特征的预测模型。来自遗传预测因子的功能成像特征，同样将嵌入可解释的深层第三，将两者结合起来建立一个综合的多基因模型。基于功能基因组学和神经影像学的特征，允许两个子组件的影响此外，我们将扩展我们之前的工作来开发基于压缩的可解释性。方法，允许网络粗粒度并以不同的分辨率级别进行解释。解释将包括探索精神疾病的亚表型结构和相互作用我们期望所提出的方法具有广泛的影响，包括见解。大脑功能的机械基础、综合多层次分析的新框架以及开发未来研究的方法和资源。