Novel <scp>FGF9</scp> variant contributes to multiple synostoses syndrome 3
Bibliographic record
Abstract
Multiple synostoses syndromes (SYNS) are autosomal dominant syndromes characterized by multiple joint fusions commonly involving the carpal-tarsal, interphalangeal, humeroradial, and cervical spine joints. They display genetic heterogeneity with pathogenic variants reported in four separate genes (NOG, GDF5, FGF9, and GDF6) defining four different SYNS forms. FGF9 variants have been reported in SYNS3, a SYNS with multiple synostoses, normal cognition, normal hearing, and craniosynostosis. Here, we report a novel FGF9 c.569G > C p.(Arg190Thr) variant identified by whole-exome sequencing in a patient with multiple bony abnormalities. The patient initially presented with elbow instability and decreased range of motion. Imaging revealed bilateral radial head deformities, carpal-tarsal fusions, brachydactyly, and osteoarthritis of the sacroiliac joints. In silico protein modeling of the identified FGF9 variant predicts decreased stability of ligand-receptor binding supporting the pathogenicity of this finding. This finding expands the repertoire of FGF9 variants and phenotypic information reported for SYNS3 and suggest that genotype phenotype correlations due to localization seem less likely and more so due to the consequence of the pathogenic variant on the receptor. This is useful in the counseling in families as more de novo variants emerge.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".