Instrumenting the Delivery System for a Genomics Research Information Commons

项目摘要 患者的遗传变异必须根据基于人群的参考和详细的表型进行背景分析 评估其致病性和对预后的影响，基于其他治疗的治疗轨迹和结果， 患者的变异，或类似变异的特定基因。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， 如有必要，再联系患者。在初步工作中，CTSA计划中的三家主要儿童医院 形成了基因组学研究和创新网络（GRIN），利用联合的，种族多样的 在儿科疾病谱中具有无与伦比的代表性。GRIN研究中心广泛同意 病人的生物样本库下一个合乎逻辑的步骤是一个真正的联合CTSA范围内的生物库 该倡议与支持基因组学信息共享（GIC）的信息学。有了表型数据 作为护理的副产品，我们开发了GIC技术，监管和政策支柱，认识到 跨CTSA医院的IT系统的异质性和成功联合的本地控制要求 网络首先，遵循完善的通用数据模型，每个站点将数据公开给跨 安全的PIC-SURE Meta应用程序编程接口（API），促进了多个 异构临床、组学和环境数据集。我们证明了自缩放性质的GIC 因为两个额外的CTSA以模块化方式连接。第二，我们为研究人员开发了两个门户网站：（A）准备- 研究门户。研究者可以执行基因型、表型或基因型/表型组合查询， 真实的接收汇总结果;以及（B）研究门户。通过适当的批准，患者级别的数据很容易 转移到云托管环境中，使用数据科学工具（Microsoft Notebooks，R Studio）， FHIR应用程序和资源，以及对外部数据源的API访问（例如，gnomAD，NHANES）。第三，我们发展 一个GIC工具包，包含广泛同意的生物库入组、研究者访问、材料转移和 协作，以使新的站点能够参与和/或自组织到协作网络中。最后我们 利用GIC建立并公开提供基因调整、精确性、 不同人口统计学人群的实验室参考范围。

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.