GenomeDiver: a platform for phenotype-guided medical genomic diagnosis

Nathaniel M. Pearson, Christian Stolte, Kevin Shi, Faygel Beren, Noura S. Abul-Husn, Gabrielle Bertier, Kaitlyn Brown, George A. Diaz, Jacqueline A. Odgis, Sabrina A. Suckiel, Carol R. Horowitz, Melissa Wasserstein, Bruce D. Gelb, Eimear E. Kenny, Charles Gagnon, Vaidehi Jobanputra, Toby Bloom, John M. Greally

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Making a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient’s phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient’s disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process. Methods: GenomeDiver uses genomic data to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient’s presentation. Results: We show how GenomeDiver communicates the clinician’s informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and examples of the performance of GenomeDiver using genomic testing data. Conclusion: GenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory–clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.

Original languageEnglish (US)
JournalGenetics in Medicine
DOIs
StateAccepted/In press - 2021

ASJC Scopus subject areas

  • Genetics(clinical)

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