Use of Whole-Genome Sequencing for Mitochondrial Disease Diagnosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Mitochondrial diseases (MDs) are the commonest group of heritable metabolic disorders. Phenotypic diversity can make molecular diagnosis challenging, and causative genetic variants may reside in either mitochondrial or nuclear DNA. A single comprehensive genetic diagnostic test would be highly useful and transform the field. We applied whole-genome sequencing (WGS) to evaluate the variant detection rate and diagnostic capacity of this technology with a view to simplifying and improving the MD diagnostic pathway. METHODS: Adult patients presenting to a specialist MD clinic in Sydney, Australia, were recruited to the study if they satisfied clinical MD (Nijmegen) criteria. WGS was performed on blood DNA, followed by clinical genetic analysis for known pathogenic MD-associated variants and MD mimics. RESULTS: Of the 242 consecutive patients recruited, 62 participants had "definite," 108 had "probable," and 72 had "possible" MD classification by the Nijmegen criteria. Disease-causing variants were identified for 130 participants, regardless of the location of the causative genetic variants, giving an overall diagnostic rate of 53.7% (130 of 242). Identification of causative genetic variants informed precise treatment, restored reproductive confidence, and optimized clinical management of MD. DISCUSSION: Comprehensive bigenomic sequencing accurately detects causative genetic variants in affected MD patients, simplifying diagnosis, enabling early treatment, and informing the risk of genetic transmission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".