A novel mutation in <i>KIAA0196</i>: identification of a gene involved in Ritscher–Schinzel/3C syndrome in a First Nations cohort
Bibliographic record
Abstract
BACKGROUND: Ritscher-Schinzel syndrome (RSS) is a clinically heterogeneous disorder characterised by distinctive craniofacial features in addition to cerebellar and cardiac anomalies. It has been described in different populations and is presumed to follow autosomal recessive inheritance. In an effort to identify the underlying genetic cause of RSS, affected individuals from a First Nations (FN) community in northern Manitoba, Canada, were enrolled in this study. METHODS: Homozygosity mapping by SNP array and Sanger sequencing of the candidate genes in a 1Mb interval on chromosome 8q24.13 were performed on genomic DNA from eight FN RSS patients, eight of their parents and five unaffected individuals (control subjects) from this geographic isolate. RESULTS: All eight patients were homozygous for a novel splice site mutation in KIAA0196. RNA analysis revealed an approximate eightfold reduction in the relative amount of a KIAA0196 transcript lacking exon 27. A 60% reduction in the amount of strumpellin protein was observed on western blot. CONCLUSIONS: We have identified a mutation in KIAA0196 as the cause of the form of RSS characterised in our cohort. The ubiquitous expression and highly conserved nature of strumpellin, the product of KIAA0196, is consistent with the complex and multisystem nature of this disorder.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".