1/3-Networks from Multidimensional Data for Schizophrenia and Related Disorders

精神分裂症及相关疾病多维数据的 1/3 网络

• 批准号: 8501690
• 负责人: ERIC E SCHADT
• 金额: $ 75.03万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 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/8501690
• 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.

描述（由申请人提供）：在这份针对 RFA-MH-12-020 提交的合作 R01“精神分裂症和相关疾病的多维数据网络”中，我们建议开发用于整合广泛的基因组、成像和临床数据的方法，将所有数据、方法和结果托管在新颖、灵活和可扩展的计算平台上。随后，这些数据和方法将用于建立研究界可用的工作流程，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，然后扩展到其他精神障碍。具体来说，在 AIM 1 中，我们将采用 Sage Bionetworks 的 Synapse 平台来以分析就绪格式托管、质量控制、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学和多尺度生物学研究所的数据中心托管，其中包括数据仓库（有组织的文件系统和数据库）、Web 服务层和应用程序层，用于促进网络重建和使用 SCZ 数据进行更普遍的模型构建。在 AIM 2 中，我们将开发一系列分析方法，其中包括用于初级处理多种类型数据的新工具和现有工具。利用直接的实验结果，我们将生成主要分析数据集（例如，表达 QTL、成像 QTL、具有 SNP/CNV 基因型的 GWAS、RNASeq 特征以及 DNA 甲基化和 RNAseq 关联），利用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）构建交互网络，将所有数据和结果转换为分析就绪格式，以及 构建一组标准查询以促进 SCZ 基因发现。在AIM 3中，继平台开发、主要分析数据集生成和基本网络构建之后，我们将开发和应用方法来构建集成的高阶分子网络和更通用的模型，以增强我们对精神分裂症背后的遗传位点和基因网络的理解。使用贝叶斯框架，将开发识别网络模块和潜在遗传方差成分（包括上位相互作用）的方法，结合先前的疾病信息和广泛的先前生物学知识来构建更详细的概率因果模型，并识别与 SCZ 相关的网络的因果调节因子。在 AIM 4 中，我们将评估模型在独立 SCZ 数据以及双相情感障碍和自闭症中的验证程度。该提案应该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创建一个解决方案，以解决基因发现的瓶颈，该瓶颈是由于我们全面分析目前患有精神疾病的个体的大样本数据的能力有限而造成的。该提案将使有效利用这些丰富的多维数据成为可能。