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

Abstract

Abstract Rare diseases (RD) have a prevalence of not more than 1/2000 in the European population, and are characterised by the difficulty of obtaining a correct and timely diagnosis. According to Orphanet, 72,5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)’s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases amongst them and with known RD in Orphanet. A 3-step programmatic cascade of phenotypic similarity calculations using The Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) was developed; genomics data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplar cases, discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity algorithms, formulating diagnostic hypotheses that were validated in 42.1% of them and need further explorations in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort in Solve-RD.