Exome Sequencing Identifies a Novel Splicing Variant in <i>COL9A3</i> Resulting in Multiple Epiphyseal Dysplasia: A Case Report
Bibliographic record
Abstract
BACKGROUND: Multiple epiphyseal dysplasia (MED, OMIM #600969) is sometimes a mild skeletal dysplasia with diverse clinical findings, including early-onset osteoarthritis and short stature. Radiographic surveys can identify delayed epiphyseal ossification and cartilaginous changes. Due to genetic heterogeneity in MED, with dominant or recessive inheritance, molecular testing is essential for its diagnosis. METHODS: The clinical manifestations, the results of laboratory examinations, and genetic analysis of a 14-year-old Pakistani male with MED are reported. CASE PRESENTATION: Here we present a male patient with type-1 diabetes and hypothyroidism with bilateral knee effusions, right knee flexion contracture, and chronic arthralgias in his elbows and wrists. Given his symptomatology, a diagnosis of juvenile idiopathic arthritis (JIA) was initially suspected. Radiographs revealed sclerotic changes and fragments in the femoral and tibial epiphyses, suggesting destructive arthropathy. Genetic testing identified a COL9A3 variant (c.148-1G>C), as well as CTLA4 deficiency. The COL9A3 gene produces type IX collagen, and mutations in this gene can disrupt collagen folding or its interaction with other cartilage components. Complications include joint damage and early osteoarthritis, possibly requiring surgery. DISCUSSION: To date, only three COL9A3 splice-site mutations have been linked to MED. Our patient's splicing variant (c.148-1G>C) is novel and is likely causative, based on similar pathogenic mutations. Our patient presented with symptoms suggestive of JIA, but radiographic findings were inconsistent with this diagnosis. Genetic testing revealed a new pathogenic splicing variant in the COL9A3 gene, confirming MED. CONCLUSION: This case highlights the importance of early molecular testing if radiographic sclerotic changes are seen in the epiphyses due to the clinical and genetic heterogeneity of MED.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".