Clinical spectrum of <i>SIX3</i>-associated mutations in holoprosencephaly: correlation between genotype, phenotype and function
Bibliographic record
Abstract
BACKGROUND: Holoprosencephaly (HPE) is the most common structural malformation of the human forebrain. There are several important HPE mutational target genes, including the transcription factor SIX3, which encodes an early regulator of Shh, Wnt, Bmp and Nodal signalling expressed in the developing forebrain and eyes of all vertebrates. OBJECTIVE: To characterise genetic and clinical findings in patients with SIX3 mutations. METHODS: Patients with HPE and their family members were tested for mutations in HPE-associated genes and the genetic and clinical findings, including those for additional cases found in the literature, were analysed. The results were correlated with a mutation-specific functional assay in zebrafish. RESULTS: In a cohort of patients (n = 800) with HPE, SIX3 mutations were found in 4.7% of probands and additional cases were found through testing of relatives. In total, 138 cases of HPE were identified, 59 of whom had not previously been clinically presented. Mutations in SIX3 result in more severe HPE than in other cases of non-chromosomal, non-syndromic HPE. An over-representation of severe HPE was found in patients whose mutations confer greater loss of function, as measured by the functional zebrafish assay. The gender ratio in this combined set of patients was 1.5:1 (F:M) and maternal inheritance was almost twice as common as paternal. About 14% of SIX3 mutations in probands occur de novo. There is a wide intrafamilial clinical range of features and classical penetrance is estimated to be at least 62%. CONCLUSIONS: Our data suggest that SIX3 mutations result in relatively severe HPE and that there is a genotype-phenotype correlation, as shown by functional studies using animal models.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".