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Advancing the implementation of variant-level functional data into clinical databases and clinical practice

推进变异级功能数据在临床数据库和临床实践中的实施

基本信息

批准号：
10674373
负责人：
Lea Starita
金额：
$ 82.62万
依托单位：
UNIVERSITY OF WASHINGTON
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：
2023-06-01 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10674373
关键词：
Acute Address BRCA1 gene Biological Assay Case/Control Studies Classification ClinVar Clinical Clinical Data Code Communities Data Data Set Databases Deposition Development Disease Educational Curriculum Educational workshop Equity Exclusion Frequencies Genes Genetic Genomic medicine Genomics Guidelines Human Genetics Human Genome Individual Infrastructure International Knowledge Laboratories Leadership Link Measurement Medical Medical Genetics Modeling Notification Pathogenicity Patients Population Population Heterogeneity Process Provider Quality Control Recommendation Research Research Personnel Resources Single Nucleotide Polymorphism Standardization System TP53 gene Testing Training Untranslated RNA Update Variant Visualization clinical care clinical database clinical decision-making clinical encounter clinical practice clinically relevant dashboard data modeling data standards empowerment genetic variant genomic data health care disparity human disease improved insight interest knowledgebase metadata standards multiplex assay repository research clinical testing segregation statistics technology development uptake variant of unknown significance

项目摘要

SUMMARY One of the major challenges limiting the potential of genomic medicine to revolutionize clinical care is the current lack of knowledge about the function of most human genetic variants. Consequently, the majority of clinically encountered genetic variants are classiﬁed as variants of uncertain signiﬁcance (VUS), which cannot be used for clinical decision making given their unknown relationship to disease. The VUS problem is particularly acute for individuals from populations historically excluded from research, compounding existing healthcare inequities when implementing genomic medicine. Variant-level functional data has the potential to overcome many of these challenges by providing pathogenicity information for variants from diverse populations. For example, we and others have demonstrated that multiplexed assays of variant effect (MAVEs) can resolve a large fraction of VUS in clinically important genes (e.g., 49% of BRCA1 VUS, 69% of TP53 VUS, and 93% of DDX3X VUS), and several U.S. and international consortia are currently using MAVEs to produce functional data for all possible coding and non-coding variants associated with all genes that have been linked to human disease. Given the potential of functional information to augment the implementation of genomic medicine, clinical guidelines have recently been updated to recommend the use of variant-level functional data when interpreting variant pathogenicity. However, despite the potential clinical utility of large-scale functional datasets, how best to implement them such that clinicians can appropriately incorporate variant functional data into clinical practice remains unknown. In this proposal we aim to address this unmet need in genomic medicine using the following approach: ● First, we will generate a framework for standardizing and disseminating curated large-scale functional data into clinician-facing resources, and implement this framework into ClinVar (Aim 1) ● Second, we will perform a proof-of-concept integration of variant level functional data in ClinVar into two large clinical practices to evaluate the clinical uptake and impact of variant-level functional data (Aim 2). ● Finally, we will build and disseminate resources for training clinicians on best practices for integrating functional data into clinical practice (Aim 3). Overall, this proposal has the potential to significantly advance the implementation of genomic data into clinical practice by enabling clinicians to appropriately leverage emerging variant-level functional data to resolve VUS, thereby making genomic medicine more equitable and impactful.