Fair Phenotype Annotation and Genomic Reinterpretation

项目总结 鉴于基因组知识的快速进化，对基因组重新解释的需求一直在增加。 然而，还没有标准的方法来确定应该向谁、何时以及如何重新解释 为确保准确性、成本效益和公平性而提供。获得基因组测试和遗传专家的机会 扩大了健康差距，有限的针对祖先的基因数据可能会进一步加剧这种差距。我们的 总体目标是设计一个可扩展和可持续的信息学框架，以支持连续基因组 对不同人群中具有非诊断外显子组或基因组测序的有症状患者的再分析。 扩展我们之前发表的关于Doc2HPO、Criteria2Query、Phen2基因、PhenCard、Phenominal和 ，我们将首先开发一个自然语言处理(NLP)管道来 使用最新的Phenopacket模式从临床记录创建多模式表现组。通过比较变化 在纵向EHR表型随时间的变化并分析在新证据背景下的变化 我们将确定从基因组再分析中受益最大的个体。然后我们就会把 从纵向电子健康记录(EHR)数据中提取不断演变的临床表型以自动触发 使用祖先感知和年龄敏感的知识图谱(PhenoKG)进行变体重新解释。与典型的不同 基于表型的基因优先排序工具，如Phen2gene，这里我们将通过 继续我们之前的工作，从EHR以及EHR中提取表型-基因型关系 文学。该知识图谱将实现祖先意识的查询、提取和推理，以及 年龄敏感、表型-基因型关系。通过利用多层随机游走一体化网络 方法，我们将把这种不同的知识图谱整合到一个表型驱动的基因和变体中 跨不同种群的连续基因组重分析的优先算法。用这些方法论 ，我们将在哥伦比亚大学的两个学术机构实施常规的再分析信息学管道 欧文大学医学中心(CUIMC)和费城儿童医院(CHOP)。我们将评估 在3年的时间里，通过不同的临床外显子组/基因组测序数据提高诊断效率 句号。我们将评估我们的公平表型和连续变异重新解释的方法如何减少 服务不足和代表性不足人群的基因组健康差异。最终，这些方法将 通过以下方式实现信息学驱动的、高效、可扩展、连续和公平的基因组医学诊断 持续的基因组变异重新解释。

PROJECT SUMMARY Given the rapid evolution of genomic knowledge, the need for genomic reinterpretation has been increasing. However, there is no standard approach yet to identifying to whom, when, and how reinterpretation should be provided to ensure accuracy, cost-effectiveness and fairness. Access to genomic tests and genetic specialists has widened health disparities, which could be further exacerbated by limited ancestry-specific genetic data. Our overarching goal is to design a scalable and sustainable informatics framework to support continuous genomic reanalysis for symptomatic patients with non-diagnostic exome or genome sequencing in diverse populations. Extending our prior published work on Doc2HPO, Criteria2Query, Phen2Gene, PhenCards, Phenominal, and phenotype-disease knowledge graphs, we will first develop a natural language processing (NLP) pipeline to create a multimodal phenome from clinical notes using the latest Phenopacket schema. By comparing changes in longitudinal EHR phenotypes over time and analyzing the changes in the context of the new evidence for variants, we will identify individuals who can benefit most from genomic reanalysis. Then we will incorporate evolving clinical phenotypes extracted from longitudinal electronic health record (EHR) data to trigger automatic variant reinterpretation using an ancestry-aware and age-sensitive knowledge graph (PhenoKG). Unlike typical phenotype-based gene prioritization tools such as Phen2Gene, here we will build the knowledge graph by extending our previous efforts and extracting phenotype-genotype relations from the EHR as well as the literature. This knowledge graph will enable the query, extraction and inference of ancestry-aware, as well as age-sensitive, phenotype-genotype relationships. By leveraging a multi-layer random-walk integrative network approach, we will incorporate this heterogeneous knowledge graph into a phenotype-driven gene and variant prioritization algorithm for continuous genomic reanalysis across diverse populations. With these methodological developments, we will implement a routine reanalysis informatics pipeline at two academic institutions, Columbia University Irving Medical Center (CUIMC) and Children’s Hospital of Philadelphia (CHOP). We will evaluate the improvements in diagnostic yield across a diverse set of clinical exome/genome sequencing data over a 3-year period. We will evaluate how our approach to fair phenotyping and continuous variant reinterpretation can reduce genomic health disparities for underserved and underrepresented populations. Ultimately, these methods will enable informatics-driven, efficient, scalable, continuous and fair genomic diagnostics for genomic medicine via continuous genomic variant reinterpretation.