<scp>RNA</scp> sequencing resolves novel <i>DYNC2H1</i> variants causing short‐rib thoracic dysplasia type 3: Case report
Bibliographic record
Abstract
BACKGROUND: Intronic variants outside the canonical splice site are challenging to interpret and therefore likely represent an underreported cause of human disease. Autosomal recessive variants in DYNC2H1 are associated with short-rib thoracic dysplasia 3 with or without polydactyly (SRTD3), a clinically heterogeneous disease generally presenting with short ribs, shortened tubular bones, narrow thorax and acetabular roof anomalies. We describe a case of SRTD3 with compound heterozygous frameshift and intronic variants and highlight the essential role of RNA sequencing (RNA-Seq) in variant interpretation. METHODS: Following inconclusive clinical genetic testing identifying a likely pathogenic frameshift variant and an intronic variant of uncertain significance (VUS) in DYNC2H1 in trans, the family enrolled in the Care4Rare Canada research program, where RNA-Seq studies were performed. RESULTS: The proband presented with post-axial polydactyly of all four limbs, a significantly small chest with a pectus excavatum and anterior flaring of the ribs. RNA-Seq investigations revealed a novel splice junction as a result of the intronic VUS and significantly decreased DYNC2H1 gene expression in the proband. CONCLUSION: This case demonstrates the diagnostic utility of RNA-Seq for variant interpretation following inconclusive clinical testing, which can ultimately lead to diagnosis for patients with rare disease.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".