Instrumenting the Delivery System for a Genomics Research Information Commons

项目摘要 患者的基因变异必须与基于人群的参考和详细的表型联系起来 根据其他疾病的护理轨迹和结果，评估其致病性和对预后的影响 携带特定基因变种或类似变种的患者。然而，CTSA的研究人员还没有准备好 获得将基因组与诊断、临床进展、 治疗反应和经适当同意的精密度调整的实验室参考范围 如果需要，请重新联系患者。在前期工作中，CTSA项目中的三家领先的儿童医院 形成了基因组研究和创新网络(GRIN)，利用联合的、不同种族的 在整个儿科疾病谱中具有无与伦比的代表性的人口。GRIN网站普遍同意 患者进入兼容的生物库协议。下一个合乎逻辑的步骤是真正联合的CTSA范围内的生物库 倡议，信息学支持基因组学信息共享(GIC)。使用表型数据 作为CARE的副产品，我们开发GIC技术、监管和政策骨干，认识到 CTSA医院之间IT系统的异构性和成功联合医院的本地控制需求 网络。首先，遵循完善的公共数据模型，每个站点都向调查人员公开数据 安全PIC-SECURE元应用程序编程接口(API)，促进了多个 不同的临床、组学和环境数据集。我们演示了GIC的自我扩展特性 因为另外两个CTSA以模块化方式结合在一起。其次，我们为研究人员开发了两个门户：(A)准备-到- 研究门户网站。调查人员可以执行基因、表型或组合的基因/表型查询，以及 实时接收汇总结果；以及(B)研究门户。有了适当的批准，患者级别的数据很容易 使用数据科学工具(Jupyter笔记本、R Studio)、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，我们发展 GIC工具包，包含广泛同意的生物库注册、调查员访问、材料转让和 协作使新站点能够参与和/或自组织到协作网络中。最后，我们 利用GIC建立并公开提供经过基因调整的精确的知识资源 实验室参考范围涵盖人口统计上的不同人群。

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.