3/3-Networks from Multidimensional Data for Schizophrenia and Related Disorders

3/3-来自精神分裂症和相关疾病多维数据的网络

• 批准号: 8305310
• 负责人: Hakon Hakonarson
• 金额: $ 34.02万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8305310
• 关键词: Algorithms; Autistic Disorder; Binding; Binding Proteins; Biological; Biology; Bipolar Disorder; Brain; Brain region; Budgets; Clinical Data; Cognitive; Collaborations; Communities; Computer software; DNA; DNA Methylation; Data; Data Analyses; Data Set; Data Sources; Databases; Development; Dimensions; Disease; Disease Association; Etiology; Functional Imaging; Functional Magnetic Resonance Imaging; Gene Expression; Generations; Genes; Genetic; Genetic Transcription; Genomics; Genotype; Goals; Gur; Human; Image; Individual; Information Networks; Institutes; Knowledge; Language; Lead; Link; Literature; Magnetic Resonance Imaging; Measures; Mental disorders; Methods; Methylation; Modeling; Molecular; Online Mendelian Inheritance In Man; Pathway Analysis; Pathway interactions; Pediatric Hospitals; Phenotype; Philadelphia; Population; Predisposition; Process; Proteins; PubMed; Quality Control; Quantitative Trait Loci; Research; Research Personnel; Sampling; Schizophrenia; Site; Solutions; Synapses; System; Test Result; Testing; United States; Universities; Validation; Variant; Weight; Work; autism spectrum disorder; base; case control; clinical phenotype; cohort; comparative; computer based statistical methods; cost; crosslink; data mining; flexibility; gene discovery; genome wide association study; medical schools; method development; neuroimaging; novel; population based; predictive modeling; protein metabolite; protein protein interaction; reconstruction; response; skills; small molecule; tool; user friendly software; web services

DESCRIPTION (provided by applicant): In this collaborative R01, "Networks from multidimensional data for schizophrenia and related disorders" submitted in response to RFA-MH-12-020, we propose to develop methods for integrating a broad range of genomic, imaging, and clinical data, hosting all data, methods, and results on a novel, flexible and extensible computing platform. Subsequently, these data and methods will be used to establish workflows available to the research community to integrate and mine the data for discovery. As proof-of-concept, multiple datasets for schizophrenia (SCZ) will be used and then extended to additional mental disorders. Specifically, in AIM 1 we will adapt the Synapse platform at Sage Bionetworks to host, QC, normalize, and transform data in an analysis ready format. Synapse will also enable computation, storage, sharing, and integration of SCZ specific data with pre-existing public data. The Sage platform will be hosted by the data center in the Institute of Genomics and Multiscale Biology at the Mount Sinai School of Medicine consisting of a data warehouse (organized file systems and databases), a web service tier and applications tier adapted to facilitate network reconstruction and more generally model building with SCZ data. In AIM 2, we will develop a pipeline of analytic methods that include new and existing tools for the primary processing of multiple types of data. Using direct experimental findings we will generate primary analysis datasets (e.g., expression QTLs, imaging QTLs, GWAS with SNP/CNV genotypes, RNASeq signatures, and DNA methylation and RNAseq associations), construct interaction networks with population-based expression and imaging datasets (e.g. gene expression, functional MRI and structural MRI), transform all data and results into analysis ready formats, and construct a standard set of queries to facilitate SCZ gene discovery. In AIM 3 following platform development, generation of primary analysis datasets, and basic network constructions, we will develop and apply methods to construct integrated, higher-order molecular networks and more generalized models to enhance our understanding of the genetic loci and gene networks underlying schizophrenia. Using a Bayesian framework, methods will be developed that identify network modules and the underlying genetic variance component (including epistatic interactions), incorporate prior disease information and extensive prior biological knowledge to construct more detailed probabilistic causal models, and identify causal regulators of networks associated with SCZ. In AIM 4, we will assess the extent to which the models validate in independent SCZ data and in bipolar disorder and autism. This proposal should have a major impact on the field as it proposes to create a solution, in the form of new platforms and analytic methods, for the bottleneck in gene discovery that results from our limited ability to fully analyze the data currently available on large samples of individuals suffering fro mental illness. This proposal will make possible the efficient use of this wealth of multi-dimensional data. PUBLIC HEALTH RELEVANCE: In the United States, over a million people have schizophrenia. The costs are staggering in human and financial terms. We propose to develop methods for integrating a broad range of genomic data into a novel, flexible and extensible computing platform. Subsequently, these data will be used to develop a pipeline of algorithms for integrating and mining the data. We will use as a proof-of-concept multiple datasets for schizophrenia, and then extend this to additional mental disorders.

描述(由申请人提供)：在响应RFA-MH-12-020提交的这份合作R01《精神分裂症和相关疾病的多维数据网络》中，我们建议开发方法来集成广泛的基因组、成像和临床数据，将所有数据、方法和结果托管在一个新的、灵活和可扩展的计算平台上。随后，这些数据和方法将被用于建立可供研究界使用的工作流程，以整合和挖掘数据以进行发现。作为概念验证，将使用精神分裂症(SCZ)的多个数据集，然后将其扩展到其他精神障碍。具体地说，在目标1中，我们将调整Sage Bionetworks的Synapse平台，以便以分析就绪的格式托管、QC、标准化和转换数据。Synapse还将实现SCZ特定数据与预先存在的公共数据的计算、存储、共享和集成。SAGE平台将由西奈山医学院基因组和多尺度生物学研究所的数据中心托管，数据中心由数据仓库(有组织的文件系统和数据库)、网络服务层和应用层组成，适用于促进网络重建和更广泛地使用SCZ数据建立模型。在AIM 2中，我们将开发一个分析方法管道，其中包括用于初级处理多种类型数据的新的和现有的工具。利用直接的实验结果，我们将生成初步分析数据集(例如，表达QTL、成像QTL、具有SNP/CNV基因类型的GWA、RNAseq签名、DNA甲基化和RNAseq关联)，构建与基于群体的表达和成像数据集(例如基因表达、功能MRI和结构MRI)的交互网络，将所有数据和结果转换为便于分析的格式，并构建标准查询集以促进SCZ基因的发现。在AIM 3中，随着平台的开发、初步分析数据集的生成和基本网络的构建，我们将开发和应用各种方法来构建集成的、更高阶的分子网络和更通用的模型，以增强我们对精神分裂症遗传座位和基因网络的理解。利用贝叶斯框架，将开发确定网络模块和潜在遗传差异分量(包括上位性相互作用)的方法，纳入先前的疾病信息和广泛的先前生物学知识，以构建更详细的概率因果模型，并确定与SCZ相关的网络的因果调节因素。在目标4中，我们将评估模型在独立的SCZ数据以及双相情感障碍和自闭症中的有效性。这项建议应该会对该领域产生重大影响，因为它建议以新平台和分析方法的形式为基因发现中的瓶颈创造一个解决方案，该瓶颈是由于我们有限的能力来充分分析目前患有精神疾病的个人的大样本数据造成的。这一提议将使有效利用这些丰富的多维数据成为可能。 与公共健康相关：在美国，有100多万人患有精神分裂症。从人力和财力的角度来看，这些成本都是惊人的。我们建议开发将广泛的基因组数据集成到一个新的、灵活的和可扩展的计算平台中的方法。随后，这些数据将被用来开发一系列整合和挖掘数据的算法。我们将使用精神分裂症的多个数据集作为概念验证，然后将其扩展到其他精神障碍。