MitoPhen database: a human phenotype ontology-based approach to identify mitochondrial DNA diseases. Issue 17 (24th August 2021)

• Record Type: Journal Article
• Authors: Ratnaike, Thiloka E; Greene, Daniel; Wei, Wei; Sanchis-Juan, Alba; Schon, Katherine R; van den Ameele, Jelle; Raymond, Lucy; Horvath, Rita; Turro, Ernest; Chinnery, Patrick F
• Is Part Of: Nucleic acids research. Volume 49:Issue 17(2021)
• Page Start: 9686
• Page End: 9695
• Publication Date: 2021-08-24
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab726
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Diagnosing mitochondrial disorders remains challenging. This is partly because the clinical phenotypes of patients overlap with those of other sporadic and inherited disorders. Although the widespread availability of genetic testing has increased the rate of diagnosis, the combination of phenotypic and genetic heterogeneity still makes it difficult to reach a timely molecular diagnosis with confidence. An objective, systematic method for describing the phenotypic spectra for each variant provides a potential solution to this problem. We curated the clinical phenotypes of 6688 published individuals with 89 pathogenic mitochondrial DNA (mtDNA) mutations, collating 26 348 human phenotype ontology (HPO) terms to establish the MitoPhen database. This enabled a hypothesis-free definition of mtDNA clinical syndromes, an overview of heteroplasmy-phenotype relationships, the identification of under-recognized phenotypes, and provides a publicly available reference dataset for objective clinical comparison with new patients using the HPO. Studying 77 patients with independently confirmed positive mtDNA diagnoses and 1083 confirmed rare disease cases with a non-mitochondrial nuclear genetic diagnosis, we show that HPO-based phenotype similarity scores can distinguish these two classes of rare disease patients with a false discovery rate <10% at a sensitivity of 80%. Enriching the MitoPhen database with more patients will improve predictions for increasingly rare variants.