Genotypic and phenotypic analysis of 396 individuals with mutations in <i>Sonic Hedgehog</i>
Bibliographic record
Abstract
BACKGROUND: Holoprosencephaly (HPE), the most common malformation of the human forebrain, may result from mutations in over 12 genes. Sonic Hedgehog (SHH) was the first such gene discovered; mutations in SHH remain the most common cause of non-chromosomal HPE. The severity spectrum is wide, ranging from incompatibility with extrauterine life to isolated midline facial differences. OBJECTIVE: To characterise genetic and clinical findings in individuals with SHH mutations. METHODS: Through the National Institutes of Health and collaborating centres, DNA from approximately 2000 individuals with HPE spectrum disorders were analysed for SHH variations. Clinical details were examined and combined with published cases. RESULTS: This study describes 396 individuals, representing 157 unrelated kindreds, with SHH mutations; 141 (36%) have not been previously reported. SHH mutations more commonly resulted in non-HPE (64%) than frank HPE (36%), and non-HPE was significantly more common in patients with SHH than in those with mutations in the other common HPE related genes (p<0.0001 compared to ZIC2 or SIX3). Individuals with truncating mutations were significantly more likely to have frank HPE than those with non-truncating mutations (49% vs 35%, respectively; p=0.012). While mutations were significantly more common in the N-terminus than in the C-terminus (including accounting for the relative size of the coding regions, p=0.00010), no specific genotype-phenotype correlations could be established regarding mutation location. CONCLUSIONS: SHH mutations overall result in milder disease than mutations in other common HPE related genes. HPE is more frequent in individuals with truncating mutations, but clinical predictions at the individual level remain elusive.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".