Development and validation of a computable knowledge framework for genomic medicine

项目总结/摘要 基因组医学是一门解释个体基因组信息的学科， 临床护理，用于诊断、预后或治疗决策。基因组实践的组成部分 解释是从知识库中收集多条证据，以支持或反驳 评价变异的临床意义。现代临床变异知识库维护文献和 变量覆盖范围大多不重叠。这种内容的多样性导致了基因组中的一个已知问题 解释：负责编写临床变异报告的分析人员选择花费大量时间 浏览多个资源和整理证据，或者有选择地错过关键信息， 评估更少的资源。分析员在临床上解释变体列表所需的工作量是 由于其在患者基因组的临床评估中的限速作用，被称为解释瓶颈。 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： 开发供基因组分析师使用的综合临床解释应用程序。作为新的知识基础 创建后，这些公共和私有数据集成器中的每一个都将承担设计和维护 每个新资源都有另一个接口，导致跨部门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、可互操作和联合基因组数据生态系统的过渡 通过开发和验证， 基因组医学的可计算知识框架。这一目标正在通过以下方式实现： 协调与癌症变异解释联盟、ClinGen和 全球基因组学与健康联盟。这一行政补充扩展了家长的活动 R35通过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）是一个对临床决策支持系统和用于支持临床决策的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团队的新合作来实现的， gnomAD数据集框架的开发。gnomAD数据集在应用AI/ML工具中的实用性 将在增强智能变体分类系统中演示。 由于这种管理补充，新的AI/ML应用程序依赖于人口频率 无需数据统一工作即可提供证据。这将提供一个基础， 在基因组医学管道中对变体进行可扩展的AI辅助分类。

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.