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

描述（由申请人提供）：在此合作R 01中，“精神分裂症和相关疾病多维数据网络”提交响应RFA-MH-12-020，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，在一个新颖，灵活和可扩展的计算平台上托管所有数据，方法和结果。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疾病。具体来说，在AIM 1中，我们将调整Sage Bionetworks的Synapse平台，以分析就绪格式托管、QC、标准化和转换数据。Synapse还将实现SCZ特定数据与现有公共数据的计算、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，该数据中心包括一个数据仓库（有组织的文件系统和数据库）、一个网络服务层和应用程序层，适用于促进网络重建和更普遍的SCZ数据模型构建。在AIM 2中，我们将开发一个分析方法管道，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签名以及DNA甲基化和RNAseq关联），构建具有基于群体的表达和成像数据集（例如基因表达、功能性MRI和结构性MRI）的相互作用网络，将所有数据和结果转换为分析就绪格式，并构建标准查询集以促进SCZ基因发现。在AIM 3平台开发，主要分析数据集的生成和基本的网络构建之后，我们将开发和应用方法来构建集成的，高阶的分子网络和更广义的模型，以提高我们对精神分裂症的遗传位点和基因网络的理解。使用贝叶斯框架，将开发的方法，确定网络模块和潜在的遗传方差组件（包括上位性相互作用），将以前的疾病信息和广泛的生物学知识，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。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。这一提议应该对该领域产生重大影响，因为它提出以新的平台和分析方法的形式为基因发现的瓶颈创造一个解决方案，因为我们有限的能力充分分析目前可获得的大样本患有精神疾病的个体的数据。这一建议将使有效利用这一丰富的多维数据成为可能。

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.