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A phenomics-first resource for interpretation of variants

用于解释变异的表型组学优先资源

基本信息

批准号：
10448140
负责人：
MELISSA A HAENDEL
金额：
$ 220.75万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-09 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10448140
关键词：
Address Adoption Algorithms Animal Model Basic Science Biology Candidate Disease Gene Case Study Characteristics Clinical Clinical Data Communities Complex Conflict (Psychology)Data Data Sources Databases Development Diagnosis Diagnostic Disease Disease model Evaluation Face Fast Healthcare Interoperability Resources Frequencies Functional disorder Genes Genetic Diseases Genome Genomics Genotype Human Human Genome Information Resources Journals Knowledge Link Mainstreaming Manuals Medical Genetics Methods Modality Modeling Modernization Ontology Outcome Patient Care Patients Phenotype Process Rare Diseases Research Research Personnel Resources Software Engineering Source Structure System Techniques Technology Terminology Variant Work base clinical care clinically actionable community partnership computational platform data integration data modeling data standards database schema disease diagnosis disease phenotype gene function genetic variant genomic variation heterogenous data human disease improved interoperability novel patient registry phenomics phenotypic data precision medicine prototype tool variant of unknown significance

项目摘要

A Phenomics-First Resource (PFR) for interpretation of variants Genomics is key to precision medicine; however, despite the ease of sequencing, clinical interpretation is still thwarted because relevant data (disease, phenotype, and variant) is complex, heterogeneous, and disaggregated across sources. Moreover, this evidence is sometimes incomplete, conflicting, and erroneous. Consequently, clinicians face long lists of candidate diseases, genes, and countless variants of unknown significance. This situation will not improve without capturing and harmonizing the underlying phenotypic information; computability of this information is the bedrock for the emerging field of phenomics. From basic science to clinical care, communities need structured ways to represent and exchange phenotypes and disease definitions. Addressing these fundamental phenomics needs makes it possible to computationally assess and reveal links between diseases and variants. We have previously shown how the addition of phenotypic information using the Human Phenotype Ontology (HPO) can improve the diagnostic yield for hard-to-diagnose patients, and HPO is therefore now a global standard for “deep phenotyping”. We have demonstrated the applicability of deep phenotyping in the evaluation of rare diseases which have overlapping mechanistic underpinnings with common/complex diseases as well as evolutionarily conserved mechanisms in model organisms. Having coordinated the community and prototyped the underlying computational platforms, we will now align both phenotype ontologies and clinical terminologies, enabling better comparison and inference of phenotypes for improved diagnostic efficacy. We propose to develop a Phenomics-First Resource (PFR). Specifically we will: 1. Create a community-driven framework of interoperable phenotype definitions across species (uPheno) 2. Harmonize human disease definitions with the MONDO disease alignment resource 3. Create a community-wide exchange standard for clinical and model-organism phenotypes (Phenopackets) 4. Develop an integrated phenomics platform to provide the research (e.g. BioLink) and clinical (FHIR) communities with programmatic access to phenomics ontologies, data, and algorithms The dynamic suite of interlinked technologies will together leverage community-developed knowledge in order to make variant interpretation more reliable, better provenanced, and more clinically actionable.

表型优先资源（PFR）用于变异解释