Detecting Mitochondrial DNA Variants in Rare Disease Families
Author Information
Author(s): Stenton Sarah L., Laricchia Kristen, Lake Nicole J., Chaluvadi Sushma, Ganesh Vijay, DiTroia Stephanie, Osei-Owusu Ikeoluwa, Pais Lynn, O’Heir Emily, Austin-Tse Christina, O’Leary Melanie, Abu Shanap Mayada, Barrows Chelsea, Berger Seth, Bönnemann Carsten G., Bujakowska Kinga M., Campagna Dean R., Compton Alison G., Donkervoort Sandra, Fleming Mark D., Gallacher Lyndon, Gleeson Joseph G., Haliloglu Goknur, Pierce Eric A., Place Emily M., Sankaran Vijay G., Shimamura Akiko, Stark Zornitza, Tan Tiong Yang, Thorburn David R., White Susan M., Vilain Eric, Lek Monkol, Rehm Heidi L., O’Donnell-Luria Anne
Primary Institution: Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium
Hypothesis
Can mitochondrial DNA variant detection from exome and genome sequencing data help resolve undiagnosed cases in rare disease families?
Conclusion
The study found that mtDNA variant analysis provided a genetic diagnosis or candidate variant for 0.4% of undiagnosed families affected by rare diseases.
Supporting Evidence
- Diagnostic mtDNA variants were identified in 10 previously genetically undiagnosed families.
- One additional undiagnosed proband had >900 heteroplasmic variants providing evidence of pathogenicity.
- The study analyzed data from 6,660 rare disease families, with 5,625 being genetically undiagnosed.
Takeaway
Scientists looked at DNA from families with rare diseases to find hidden problems in their mitochondria, which are tiny parts of cells that help produce energy. They found some new clues that could help explain why some people are sick.
Methodology
The study used dedicated bioinformatic pipelines to analyze exome and genome sequencing data for mitochondrial DNA variants in a cohort of 6,660 rare disease families.
Limitations
The primary source of DNA was blood, which may not optimally reflect mtDNA variants present in affected tissues, potentially leading to missed diagnoses.
Participant Demographics
The majority of participants were genetically undiagnosed families with a broad range of rare diseases, primarily pediatric onset.
Digital Object Identifier (DOI)
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