Development and validation of a computable knowledge framework for genomic medicine

项目摘要/摘要 基因组医学是将个体的基因组信息解释为其一部分的学科 临床护理，用于诊断、预后或治疗决策。基因组实践中不可或缺的一部分 口译是从知识库中收集多条证据来支持或驳斥 评估的变异的临床意义。现代临床变异知识库维护文献和 基本不重叠的变体覆盖范围。这种内容的多样性导致了基因组中的一个已知问题 解读：负责汇编临床变异报告的分析师选择花费大量时间 浏览多个资源并整理证据，或通过有选择地遗漏关键信息 评估更少的资源。分析员在临床上解释变量列表所需的工作量是 被称为解释瓶颈，因为它在患者基因组的临床评估中起到了限速作用。 来自公共和私人基因组医学组织的数据集成商通过以下方式努力缓解这一瓶颈 开发供基因组分析员使用的综合临床解释应用程序。作为新的知识库 创建后，这些公共和私有数据集成器中的每个都将面临设计和维护的任务 每个新资源的另一个接口，导致数据协调工作的组合增长 整个系统。这种方法是不可扩展的。 父R35正在实现向可扩展、可互操作和联合的基因组数据生态系统的过渡 通过开发和验证，从目前已有的数据集成商和知识库 基因组医学的可计算知识框架。这一目标正在通过 协调研究活动与癌症联合会、Clingen和 全球基因组学与健康联盟。这一行政补充扩展了母公司的活动 R35将正在开发的基因组知识框架应用于基因组聚合数据库 (GnomAD)对临床决策支持系统和用于支持临床的AI/ML工具具有重要价值的数据集 不同的解释。这是通过与gnomAD团队进行新的合作来实现的 开发gnomAD数据集的框架。GnomAD数据集在应用AI/ML工具中的应用 对于基因组医学，将在增强智能变体分类系统中进行演示。 作为这一管理补充的结果，新的AI/ML应用程序依赖于人口频率 无需进行数据协调工作即可提供证据。这将为 可扩展的、人工智能辅助的基因组药物管道中的变种分类。

Project Summary/Abstract Genomic medicine is the discipline of interpreting genomic information about an individual as part of their clinical care, for diagnosis, prognosis, or therapeutic decision-making. Integral to the practice of genome interpretation is the collection of multiple lines of evidence from knowledgebases to support or refute the clinical significance of evaluated variants. Modern clinical variant knowledgebases maintain literature and variant coverage that is mostly non-overlapping. This diversity of content causes a known problem in genome interpretation: analysts tasked with assembling a clinical variant report choose to spend considerable time navigating multiple resources and collating evidence, or risk missing critical information by selectively evaluating fewer resources. The resulting effort needed for an analyst to clinically interpret a variant list is known as the interpretation bottleneck, for its rate-limiting role in the clinical evaluation of patient genomes. Data integrators from public and private genomic medicine organizations work to alleviate this bottleneck by developing integrative clinical interpretation applications for use by genome analysts. As new knowledgebases are created, each of these public and private data integrators is left with the task of designing and maintaining another interface for each new resource, leading to combinatorial growth of data harmonization effort across the entire system. This approach is not scalable. The parent R35 is enabling a transition to a scalable, interoperable, and federated genomic data ecosystem from the data integrators and knowledgebases already in existence today through development and validation of a computable knowledge framework for genomic medicine. This objective is being carried out through coordination of research activities with the Variant Interpretation for Cancer Consortium, ClinGen, and the Global Alliance for Genomics and Health. This administrative supplement extends the activities of the parent R35 by applying the developing genomic knowledge framework to the Genome Aggregation Database (gnomAD) a dataset of great value to clinical decision support systems and AI/ML tools used to support clinical variant interpretation. This is achieved through a new collaboration with the gnomAD team to bring the developments of the framework to the gnomAD dataset. Utility of the gnomAD dataset in applied AI/ML tools for genomic medicine will be demonstrated in an augmented intelligence variant classification system. As a result of this administrative supplement, new AI/ML applications dependent upon Population Frequency Evidence will be made possible without need for data harmonization efforts. This will provide a foundation for scalable, AI-assisted classification of variants in genomic medicine pipelines.