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NIHOFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTHR24

Resource of zebrafish models of human diseases

Active
Institution
UNIVERSITY OF OREGON, OR
Principal investigator
Monte Westerfield
Funding
$744K
Duration
2024–2028
Open portfolio

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Abstract

PROJECT SUMMARY/ABSTRACT The diagnosis of rare diseases has seen enormous gains from the advent of whole-exome and whole-genome sequencing as clinical diagnostic tools. However, due to the large number of rare variants of unknown significance found in poorly characterized genes, there is a significant need for functional annotation of genes and variants. Model organism research has proven to be one of the most cost-effective methods to annotate genes and understand the biology underlying gene function and disease. This project will develop a resource of rigorously validated, translatable zebrafish models of previously undiagnosed human genetic diseases. Clinical and genetic information about new cases of undiagnosed rare human diseases will be obtained through collaboration with the clinical sites of the Diagnostic Centers of Excellence (DCoEs) and the Genomics Research to Elucidate the Genetics of Rare diseases (GREGoR) Consortium. A bioinformatics pipeline will be used to analyze and prioritize variants obtained from the patients for further study. Then humanized zebrafish genetic models of the selected gene variants will be generated using a new CRISPR/cas9 homologous recombination technology. The models will be validated by sequencing and phenotypic characterizations including molecular phenotypes. The resource of humanized animal models will be made available to the research community through the Zebrafish International Resource Center (ZIRC), and the accompanying genetic and phenotypic metadata will be deposited at the Zebrafish Information Network (ZFIN) and the Alliance of Genome Resources.

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R24OFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTHSpecial Emphasis Panel[ZRG1-BTC-Y(55)R]zebrafishresourcehumanizedvariantsrare diseaseshuman diseasesundiagnosedrare

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