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Fair Phenotype Annotation and Genomic Reinterpretation

公平表型注释和基因组重新解释

基本信息

批准号：
10675315
负责人：
Wendy K Chung
金额：
$ 88.64万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10675315
关键词：
Address Age Algorithms Awareness Case Study Categories Child Clinical Code Data Databases Development Diagnosis Diagnostic Disease Disparity Documentation Effectiveness Electronic Health Record Ensure Equity Evolution Face Gene Frequency Genes Genetic Genetic Diseases Genomic medicine Genomics Genotype Goals Grain Healthcare Healthcare Systems Individual Informatics Institution KBG syndrome Knowledge Laboratories Learning Literature Manuals Medical Records Medical center Methodology Methods Natural Language Processing Natural Language Processing pipeline Patients Pediatric Hospitals Performance Phenotype Philadelphia Physicians Population Population Heterogeneity Public Health Publishing Rare Diseases Research Retrieval Severities Signal Transduction Source Specialist Structure Symptoms Syndrome TAF1 gene Testing Time Underrepresented Populations Underserved Population Universities Variant Walking Work World Health clinical phenotype clinical practice clinical sequencing computable phenotypes cost cost effectiveness design digital disease phenotype exome exome sequencing genetic disorder diagnosis genetic variant genome sequencing health disparity improved innovation insight interoperability knowledge base knowledge graph multimodality novel open source tool patient population phenome portability precision medicine routine screening tool tv watching

项目摘要

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.

项目总结