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, Yang Tan Tiong, 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 systematic analysis of exome and genome sequencing data identify mitochondrial DNA variants in undiagnosed rare disease cases?
Conclusion
The study found that analyzing mitochondrial DNA variants led to a genetic diagnosis in 0.4% of previously undiagnosed rare disease families.
Supporting Evidence
- The study analyzed data from 6,660 rare disease families.
- Diagnostic mtDNA variants were found in 10 previously undiagnosed families.
- One family had over 900 heteroplasmic variants linked to a novel pathogenic variant.
Takeaway
Researchers looked at DNA from families with rare diseases to find hidden problems in their mitochondria, helping some families get answers about their conditions.
Methodology
The study used specialized pipelines to analyze mitochondrial DNA from exome and genome sequencing data, focusing on single nucleotide variants, small indels, and large deletions.
Limitations
The study only identified diagnostic variants in a small percentage of undiagnosed families.
Participant Demographics
The cohort included 6,660 rare disease families, with 5,625 being genetically undiagnosed.
Digital Object Identifier (DOI)
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