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: 8305366
• 负责人: Stephen Henry Friend
• 金额: $ 99.43万
• 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/8305366
• 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）：在此协作R01中，“来自精神分裂症和相关疾病的多维数据的网络”是针对RFA-MH-12-020提交的，我们建议开发用于整合一系列基因组，成像和临床数据的广泛数据，托管所有数据，托管所有数据，并将所有数据集成的方法，并启用了全新的平台，并将其flexsible flexsible和Extsmentys计算。随后，这些数据和方法将用于建立可用于研究社区的工作流程，以整合和挖掘数​​据以进行发现。作为概念验证，将使用多个精神分裂症（SCZ）的数据集，然后扩展到其他精神障碍。具体而言，在AIM 1中，我们将适应Sage Bionetworks的Synapse平台以主机，QC，归一化和转换分析准备格式的数据。 Synapse还将通过预先存在的公共数据来启用SCZ特定数据的计算，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，该学院由数据仓库（有组织的文件系统和数据库）组成，Web服务层和应用程序层，适合于促进网络重建，并通过SCZ数据建立更多的网络重建。在AIM 2中，我们将开发一条分析方法的管道，其中包括用于多种类型数据的主要处理的新工具和现有工具。使用直接的实验发现，我们将生成初级分析数据集（例如，具有SNP/CNV基因型的QTL，Imaging QTL，GWA，GWAS，RNASEQ特征，RNASEQ签名以及DNA甲基化和RNASEQ关联），构建基于种群的表达和成像数据集（例如，基于人群的表达和成像数据）的MRI和MRI MRI MRI MRI，MRI MRI，MRI MRI，MRI MRI构建相互作用网络（构建一组标准的查询，以促进SCZ基因发现。在平台开发，主要分析数据集和基本网络构建体的AIM 3中，我们将开发和应用方法来构建综合的，高阶的分子网络和更概括的模型，以增强我们对精神分裂症基础的遗传基因座和基因网络的理解。使用贝叶斯框架，将开发方法，以识别网络模块和潜在的遗传方差成分（包括上皮相互作用），结合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关系模型，并识别与SCZ相关的网络的因果监管机构。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验证的程度。该提案应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解决方案，以便基因发现中的瓶颈我们的瓶颈，这是由于我们有限的能力，无法完全分析目前可在精神疾病中患有大量个人的数据。该提案将使有效利用这些丰富的多维数据。 公共卫生相关性：在美国，超过一百万的人患有精神分裂症。成本在人类和财务方面令人震惊。我们建议开发将广泛基因组数据集成到新颖，灵活且可扩展的计算平台中的方法。随后，这些数据将用于开发用于集成和挖掘数据的算法管道。我们将用作精神分裂症的多个概念证明，然后将其扩展到其他精神障碍。