#4037 DEVELOPMENT OF AN EXPERT MODEL FOR THE DIAGNOSIS OF INHERITED KIDNEY DISEASES

Background and Aims Inherited kidney diseases (IKDs) account for 10-20% of cases of Chronic Kidney Disease (CKD). Mutations in over 400 genes are known as drivers of more than 150 monogenic kidney disorders. Clinical diagnosis is often challenging as these disorders are characterized by numerous clinical features often shared by many diseases as well as very variable expression. Over the years, rare-disease databases such as OMIM and Orphanet have complied information to facilitate diagnosis of rare diseases. Based on these, The Human Phenotype Ontology (HPO) developed a standardized directory for phenotypes with phenotype-disease associations. New phenotype-driven gene-priorization tools such as Phenomizer have been developed in the recent years using these resources, however structural inaccuracies prevent a proper uptake of these tools. To date, no specific renal tool has been developed. The aim of the study was to develop an expert model for the diagnosis of IKDs based on phenotype semantic similarity distances such over Human Phenotype Annotations. Method Using MONDO ontology which encompasses HPO phenotypes, OMIM and Orphanet, HPO annotations corresponding to 196 IKDs were exported an analyzed. Based on literature, original HPO annotations for each IKD were manually curated (inaccurate terms were eliminated, new known HPOs were added and new annotations were generated in case they were missing). Frequencies were added to each annotation. Specific kidney data such as CKD stage, age at need of kidney replacement therapy and degree of proteinuria were included. Age of onset, prevalence, and specific gender sub analysis in case of diseases with X-linked inheritance was also integrated. All curated phenotype-disease associations were reviewed by European experts. Using MONDO annotations for each IKD as synthetic patients a statistical model with a friendly user web-based interface was developed in Java. Results Analysis of current ontologies by experts in the filed revealed that HPO terms associated to IKDs were inaccurate and non-specific. A new phenotype-driven tool fed with new curated terms and specific kidney data was found to be superior to current tools. Future contributions to ontologies such as HPO, MONDO and open data-sources with curated terms and updated classifications concerning IKDs will help to improving the diagnosis and characterization of IKDs. Conclusion Current phenotype-based gene prioritization tools and rare disease databases have contributed significantly to improving the clinical diagnosis of rare diseases. However, these tools are nonspecific and imprecise when focusing on a specific field such as IKDs. Curation of current annotations by experts in the field will greatly improve the accuracy of these tools and facilitate clinical diagnosis of IKDs.