Mitochondrial DNA disease discovery through evaluation of genotype and phenotype data: The Solve-RD experience.

Abstract

The diagnosis of mitochondrial DNA (mtDNA) diseases remains challenging with next-generation sequencing, where bioinformatic analysis is usually more focused on the nuclear genome. We developed a workflow for the evaluation of mtDNA diseases and applied it in a large European rare disease cohort (Solve-RD). A semi-automated bioinformatic pipeline with MToolBox was used to filter the unsolved Solve-RD cohort for rare mtDNA variants after validating this pipeline on exome datasets of 42 individuals previously diagnosed with mtDNA variants. Variants were filtered based on blood heteroplasmy levels (≥1%) and reported association with disease. Overall, 10,157 exome and genome datasets from 9,923 affected individuals from 9,483 families within Solve-RD met the quality inclusion criteria. 136 mtDNA variants in 135 undiagnosed individuals were prioritized using the filtering approach. A focused MitoPhen-based phenotype similarity scoring method was tested in a separate genetically diagnosed "phenotype test cohort" consisting of nuclear gene and mtDNA diseases using a receiving operator characteristic evaluation. We applied the MitoPhen-based phenotype similarity score of >0.3, which was highly sensitive for detecting mtDNA diseases in the phenotype test cohort, to the filtered cohort of 135 undiagnosed individuals. This aided the prioritization of 34 out of 37 (92%) individuals who received confirmed and likely causative mtDNA disease diagnoses. The phenotypic evaluation was limited by the quality of input data in some individuals. The overall pipeline led to an additional diagnostic yield of 0.4% in a cohort where mitochondrial disease was not initially suspected. This highlights the value of our mtDNA analysis pipeline in diverse datasets.