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Updated: Mar 3, 2026
Genotyping Single Nucleotide Polymorphisms in the Mitochondrial Genome by Pyrosequencing
Published on: February 10, 2023
Charting the phenotypic landscape of mitochondrial diseases through a systematic evaluation of pathogenic
Thiloka Ratnaike1, Siddharth Ramanan2, Nour Elkhateeb3
1Department of Pediatric Neurology, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, United Kingdom; Department of Pediatrics, University of Cambridge, Cambridge, United Kingdom; Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Purpose:
Primary mitochondrial diseases (PMD) arise from variants in the mitochondrial or nuclear genomes. Phenotype-based recognition of specific PMD genotypes remains difficult, prolonging the diagnostic odyssey. We expanded the MitoPhen database to characterize phenotypic variation across PMD more systematically.
Methods:
Individual-level data on mitochondrial DNA disorders, nuclear-encoded mitochondrial diseases, and single large-scale mitochondrial DNA deletions were manually curated with Human Phenotype Ontology (HPO) terms to produce MitoPhen v2. Principal-component analysis summarized system-level abnormalities; HPO-level enrichment and mean phenotype-similarity scores were then used to distinguish common PMD genotypes.
Results:
MitoPhen v2 adds 3940 individuals to the original release, now encompassing 1597 publications, 10,626 individuals, and 117 genotypes. Among 7586 affected cases, 72,861 HPO terms were recorded. Principal-component analysis revealed 6 phenotype dimensions capturing most system-level variance. At the HPO level, we observed genotype-specific enrichments and identified 111 gene-phenotype links absent from the current HPO database. Using MT-TL1, single large-scale mitochondrial DNA deletions, and POLG as exemplars, phenotype-similarity scores reliably separated individuals with these genotypes from those without.
Conclusion:
MitoPhen v2 enabled systematic, genotype-aware analysis of heterogeneous PMD phenotypes and highlighted the diagnostic value of structured, individual-level data. Phenotype-similarity metrics from such data sets can refine variant interpretation in large rare-disease cohorts and provide a transferable framework for other phenotypically complex genetic disorders.
Insights
The expanded MitoPhen v2 database systematically analyzes primary mitochondrial disease (PMD) phenotypes, improving genotype recognition. Phenotype similarity scores help differentiate PMD genotypes, aiding diagnosis in rare genetic disorders.
Area of Science:
- Genetics
- Rare Diseases
- Bioinformatics
Background:
- Primary mitochondrial diseases (PMD) result from genetic variants, making genotype identification challenging.
- The diagnostic journey for PMD is often prolonged due to complex phenotype-genotype correlations.
- Systematic characterization of phenotypic variation is crucial for advancing PMD diagnosis.
Purpose of the Study:
- To expand the MitoPhen database for a more systematic analysis of phenotypic variation in PMD.
- To improve the recognition of specific PMD genotypes based on detailed phenotypic data.
- To develop methods for distinguishing PMD genotypes using phenotypic similarity.
Main Methods:
- MitoPhen v2 was created by curating individual-level data with Human Phenotype Ontology (HPO) terms for mitochondrial DNA and nuclear-encoded disorders.
- Principal-component analysis was used to summarize system-level abnormalities.
- HPO-level enrichment and phenotype-similarity scores were calculated to differentiate PMD genotypes.
Main Results:
- MitoPhen v2 includes 10,626 individuals and 117 genotypes from 1597 publications.
- Analysis revealed 6 key phenotype dimensions and identified 111 novel gene-phenotype links.
- Phenotype-similarity scores effectively distinguished individuals with specific genotypes (e.g., MT-TL1, POLG).
Conclusions:
- MitoPhen v2 facilitates genotype-aware analysis of complex PMD phenotypes.
- Structured, individual-level phenotypic data and similarity metrics enhance variant interpretation.
- This framework is transferable for diagnosing other phenotypically complex genetic disorders.
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