Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary...

Area of Science:

• Genetics
• Rare Diseases
• Bioinformatics

Background:

• Rare diseases (RD) present diagnostic challenges, with many having genetic origins but unidentified causative genes.
• Inconclusive exome/genome sequencing results are common for patients with suspected genetic RD.
• The Solve-RD project aims to diagnose undiagnosed RD by identifying molecular causes.

Purpose of the Study:

• To develop and evaluate a phenotypic similarity-based variant prioritization methodology for undiagnosed rare diseases.
• To improve the diagnostic yield of exome/genome sequencing for rare disease patients.
• To contribute to the International Rare Diseases Research Consortium (IRDiRC) goal of diagnosing RD within one year.

Main Methods:

• Developed three complementary phenotypic similarity calculation approaches using Human Phenotype Ontology (HPO), Orphanet Rare Diseases Ontology (ORDO), and HPO-ORDO Ontological Module (HOOM).
• Performed genomic data reanalysis using the RD-Connect Genome-Phenome Analysis Platform (GPAP).
• Compared submitted cases with other cases and known RD in Orphanet.

Main Results:

• Identified variants of interest (pathogenic/likely pathogenic) in 8.8% of 725 cases analyzed through similarity clustering.
• Validated diagnostic hypotheses in 42.1% of cases.
• Required further exploration for an additional 10.9% of cases.

Conclusions:

• The phenotypic similarity-based variant prioritization methodology shows promise for diagnosing undiagnosed rare diseases.
• The approach successfully identified potential causative variants and generated validated diagnostic hypotheses.
• An automated, standardized phenotypic-based re-analysis pipeline is being developed for broader application.