Data Resource and Administrative Coordination Center for the Scalable and Systematic Neurobiology of Psychiatric and Neurodevelopmental Disorder Risk Genes Consortium

精神科和神经发育障碍风险基因联盟的可扩展和系统神经生物学数据资源和行政协调中心

• 批准号: 10642251
• 负责人: DAVID H HAUSSLER
• 金额: $ 150万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-09 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10642251
• 关键词: Administrative Coordination; Algorithms; Alleles; Area; Atlases; BRAIN initiative; Biological Assay; Biotechnology; Brain; Brain Mapping; California; Cells; Childhood; ClinVar; Clinical; Collaborations; Communities; Consensus; Data; Data Protection; Data Set; Data Store; Databases; Disease; Effectiveness; Electrophysiology (science); Ensure; Environment; FAIR principles; Fast Healthcare Interoperability Resources; Funding; Generations; Genes; Genetic; Genomics; Goals; Health; Healthcare; Healthcare Systems; Human; Image; Information Resources; International; Lead; Leadership; Maps; Measurement; Medical Genetics; Medicare/Medicaid; Mental disorders; Metadata; Modeling; Modernization; Modification; Molecular; Monitor; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Neurobiology; Neurodevelopmental Disorder; Neurologic; Neurosciences; Ontology; Organoids; Patients; Persons; Phenotype; Process; Provider; Publications; Publishing; Quality of life; Records; Regenerative Medicine; Research Personnel; Research Priority; Resources; Services; Source; Structure; Support System; Techniques; Technology; The Cancer Genome Atlas; Tissues; Training; Trans-Omics for Precision Medicine; Trust; United States National Institutes of Health; Vision; Work; biomedical resource; cancer genome; cohort; complex data; computerized data processing; data format; data portal; data resource; data sharing; data standards; data submission; effectiveness research; empowerment; exome sequencing; experience; genome browser; genome wide association study; health record; innovation; insight; interoperability; knowledge base; loss of function; meetings; model organism; neural; neuropsychiatric disorder; novel; risk variant; success; virtual; web portal; whole genome; working group

ABSTRACT Our team proposes to lead the SSPsyGene consortium into the Data Biosphere. We will do this by adapting data biosphere technology and management techniques we have already deployed for other NIH institutes, NIH Common Fund, the NIH Office of the Director, the Chan Zuckerberg Initiative (CZI), and the California Institute for Regenerative Medicine (CIRM), making SSPsyGene interoperable across multiple disease areas. We also bring our expertise with neurological data through our involvement with BICCN, Psychiatric Cell Map Initiative, CZI’s Pediatric Brain Map, NHGRI's Center for Live Cell Genomics/Biotechnology, and our close relationship with PsychENCODE and the Allen Brain Institute. For SSPsyGene, we have 4 major tasks: (1) We will assemble all the information necessary to empower the consortium to choose between 100 and 250 genes to experimentally characterize (Aim 2). We have identified more than 20 different types of information to be integrated for this purpose, many of which are already in the UCSC Genome Browser. We will apply multiple ranking algorithms to this integrated information source to guide the SSPsyGene Consortium’s decision process. (2) We will work to establish an ontology structure that is sufficiently expressive yet fully maintainable, supporting FAIR data use by both researchers and machines (Aim 3). Our previous work with the UCSC Genome Browser and our close relationships with ontology organizations will help us to bridge the gaps between molecular, cellular, tissue/organoid, and model organism measurements, and to extend these resources when needed. Inspired by our experience with the clinical ontologies in OMOP and FHIR, we propose a novel service to allow researchers to query phenotype-phenotype associations in large clinical cohorts, such as All of Us and HEDIS, the database of records from Medicare and Medicaid. (3) We will create a state-of-the-art SSPsyGene Data Biosphere fully compatible with those we created for other NIH institutes (Aim 4). Our emphasis will be on standardization of the data submission process with extensive quality monitoring to ensure timely and effective data release. We will leverage our deep involvement with the Global Alliance for Genomics and Health to ensure all data and metadata will meet FAIR standards. We have experience with the complex data types that will be generated by the SSPsyGene consortium, including -omics, imaging, electrophysiology and other data types. (4) We have served as trusted third party organizers to many NIH consortia, developing a reputation for fairness and impartiality in data sharing and publication, and expertise in coordinating, generating consensus, publishing results, and creating a resource with maximal impact (Aim 5). Based on our strengths in biomedical data, metadata and ontologies, FAIR platforms, and consortium leadership, we are confident that we will achieve all the goals of the SSPsyGene Consortium.

抽象的 我们的团队建议带领 SSPsyGene 联盟进入数据生物圈，我们将通过适应来实现这一目标。 我们已经为其他 NIH 机构部署了数据生物圈技术和管理技术， NIH 共同基金、NIH 主任办公室、陈·扎克伯格倡议 (CZI) 和加州 再生医学研究所 (CIRM)，使 SSPsyGene 在多个疾病领域具有互操作性。 我们还通过参与 BICCN（精神病学细胞图谱）带来我们在神经学数据方面的专业知识 Initiative、CZI 儿科脑图、NHGRI 活细胞基因组学/生物技术中心以及我们的密切合作 与 PsychENCODE 和艾伦脑研究所的关系 对于 SSPsyGene，我们有 4 个主要任务：（1）我们 将收集所有必要的信息，使联盟能够在 100 到 250 个基因之间进行选择 进行实验表征（目标 2）。我们已经确定了 20 多种不同类型的信息。 为此目的进行了集成，其中许多已经在 UCSC 基因组浏览器中，我们将应用多个。 对这个综合信息源的算法进行排名，以指导 SSPsyGene 联盟的决策 (2)我们将努力建立一个具有足够表达性且完全可维护的本体结构， 支持研究人员和机器公平使用数据（目标 3）。 基因组浏览器以及我们与本体组织的密切关系将帮助我们弥合差距 分子、细胞、组织/类器官和模型生物测量之间，并扩展这些 受到 OMOP 和 FHIR 临床本体经验的启发，我们 提出一项新颖的服务，允许研究人员查询大型临床中的表型-表型关联 队列，例如 All of Us 和 HEDIS，即 Medicare 和 Medicaid 的记录数据库 (3) 我们将创建。 最先进的 SSPsyGene 数据生物圈与我们为其他 NIH 机构创建的数据生物圈完全兼容 （目标 4）。我们的重点是数据提交过程的标准化和广泛的质量。 我们将利用我们对全球的深入参与进行监控，以确保及时有效的数据发布。 基因组学与健康联盟确保所有数据和元数据都符合 FAIR 标准。 SSPsyGene 联盟将生成的复杂数据类型的经验，包括 -组学、影像、电生理学和其他数据类型 (4) 我们担任值得信赖的第三方组织者。 对于许多 NIH 联盟来说，在数据共享和出版方面建立了公平和公正的声誉， 以及协调、达成共识、发布结果和创建资源最大化方面的专业知识 影响（目标 5）。基于我们在生物医学数据、元数据和本体论、FAIR 平台等方面的优势。 在联盟的领导下，我们有信心实现 SSPsyGene 联盟的所有目标。