Instrumenting the Delivery System for a Genomics Research Information Commons

检测基因组学研究信息共享的交付系统

• 批准号: 10212473
• 负责人: KENNETH D MANDL
• 金额: $ 170.55万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-31 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10212473
• 关键词: Address; African; Age; Big Data to Knowledge; Biological; Caring; Childhood; Clinical; Collaborations; Complex; Confidence Intervals; Consent; Data; Data Aggregation; Data Science; Data Set; Data Sources; Databases; Diagnosis; Diagnostic; Disease; Electronic Health Record; Enrollment; Environment; Fast Healthcare Interoperability Resources; Fostering; Frequencies; Gender; Genes; Genetic; Genetic Databases; Genome; Genomics; Genotype; Goals; Heterogeneity; Hospitals; Hypertrophic Cardiomyopathy; Individual; Industry Collaboration; Informatics; Information Resources; Information Systems; Infrastructure; Institution; Institutional Review Boards; Journals; Laboratories; Licensing; Link; Medicine; National Health and Nutrition Examination Survey; Nature; New England; Outcome; Pathogenicity; Patients; Pediatric Hospitals; Phenotype; Policies; Population; Population Heterogeneity; Process; Prognosis; Protocols documentation; Publishing; Recontacts; Reference Values; Reporting; Research; Research Personnel; Resources; Sampling; Secure; Site; System; Technology; Testing; Time; United States National Institutes of Health; Update; Variant; Vertebral column; Work; application programming interface; base; biobank; black patient; case control; cohort; comorbidity; data modeling; data sharing; ethnic diversity; exome; genetic pedigree; genetic variant; genomic data; improved; innovation; instrument; open source; patient population; phenotypic data; population based; programs; tool; treatment response; whole genome

Project Summary A patient’s genetic variant must be contextualized against a population-based reference and detailed phenotype to assess its pathogenicity and impact on prognosis, based on the care trajectories and outcomes of other patients with the variant, or similar variants of a particular gene. However, CTSA researchers do not have ready access to a definitive and representative reference dataset linking the genome to diagnosis, clinical progression, therapeutic response, and precision-adjusted laboratory reference ranges with the appropriate consents to recontact patients if needed. In preliminary work, three of the leading children’s hospitals in the CTSA program formed the Genomics Research and Innovation Network (GRIN) leveraging a combined, ethnically diverse population with unparalleled representation across the pediatric disease spectrum. GRIN sites broadly consent patients into compatible biobanking protocols. The next logical step is a truly federated CTSA-wide biobanking initiative, with the informatics supporting a Genomics Information Commons (GIC). With phenotype data produced as a byproduct of care, we develop the GIC technology, regulatory, and policy backbone, recognizing both heterogeneity of IT systems across CTSA hospitals and local control imperatives for a successful federated network. First, adhering to well-established common data models, each site exposes data to investigators across the secure PIC-SURE meta application programming interface (API), fostering incorporation of multiple heterogeneous clinical, omics, and environmental datasets. We demonstrate the self-scaling nature of the GIC as two additional CTSAs join in a modular fashion. Second, we develop two portals for researchers: (A) Prep-to- research portal. Investigators can execute genotype, phenotype, or combined genotype/phenotype queries, and receive aggregate results in real time; and (B) Study portal. With proper approvals, patient-level data are readily transferred to a cloud-hosted environment with data science tools (Jupyter Notebooks, R Studio), SMART on FHIR apps and resources, and API access to external data sources (e.g., gnomAD, NHANES). Third, we develop a GIC toolkit with policies for broadly consented biobank enrollment, investigator access, material transfer, and collaboration to enable new sites to participate and/or self-organize into collaboration networks. Finally, we leverage the GIC to build, and make publicly available, a knowledge resource of genetically-adjusted, precision laboratory reference ranges across demographically diverse populations.

项目概要 患者的遗传变异必须与基于人群的参考和详细表型结合起来 根据其他患者的护理轨迹和结果评估其致病性和对预后的影响 然而，CTSA 研究人员还没有准备好。 访问将基因组与诊断、临床进展联系起来的明确且具有代表性的参考数据集， 治疗反应和精确调整的实验室参考范围，并获得适当的同意 在前期工作中，CTSA 项目中的三家儿童医院负责重新联系患者。 建立了基因组学研究和创新网络（GRIN），利用联合的、种族多元化的 在儿科疾病谱系中具有无与伦比代表性的人群广泛同意。 下一个合乎逻辑的步骤是建立真正联合的 CTSA 范围内的生物样本库。 倡议，信息学支持基因组信息共享（GIC）和表型数据。 作为护理的副产品，我们开发 GIC 技术、监管和政策支柱，认识到 CTSA 医院之间 IT 系统的异构性以及成功联合的本地控制必要性 首先，每个站点都遵循完善的通用数据模型，向调查人员公开数据。 安全的 PIC-SURE 元应用程序编程接口 (API)，促进多种技术的结合 我们展示了 GIC 的自缩放性质。 随着另外两个 CTSA 以模块化方式加入，我们为研究人员开发了两个门户： (A) 准备。 研究人员可以执行基因型、表型或组合基因型/表型查询，以及 实时接收汇总结果；(B) 研究门户经过适当批准后，可以轻松获取患者级别的数据。 使用数据科学工具（Jupyter Notebooks、R Studio）转移到云托管环境，SMART on FHIR 应用程序和资源，以及对外部数据源（例如，gnomAD、NHANES）的 API 访问。第三，我们开发。 GIC 工具包，其中包含广泛同意的生物库注册、研究者访问、材料转移和 协作，使新站点能够参与和/或自组织成协作网络。 利用 GIC 构建并公开提供经过基因调整的精确知识资源 实验室参考范围涵盖人口统计不同的人群。