Fifteen years of research on oral–facial–digital syndromes: from 1 to 16 causal genes
Bibliographic record
Abstract
Oral–facial–digital syndromes (OFDS) gather rare genetic disorders characterised by facial, oral and digital abnormalities associated with a wide range of additional features (polycystic kidney disease, cerebral malformations and several others) to delineate a growing list of OFDS subtypes. The most frequent, OFD type I, is caused by a heterozygous mutation in the OFD1 gene encoding a centrosomal protein. The wide clinical heterogeneity of OFDS suggests the involvement of other ciliary genes. For 15 years, we have aimed to identify the molecular bases of OFDS. This effort has been greatly helped by the recent development of whole-exome sequencing (WES). Here, we present all our published and unpublished results for WES in 24 cases with OFDS. We identified causal variants in five new genes ( C2CD3 , TMEM107 , INTU , KIAA0753 and IFT57 ) and related the clinical spectrum of four genes in other ciliopathies ( C5orf42 , TMEM138 , TMEM231 and WDPCP ) to OFDS. Mutations were also detected in two genes previously implicated in OFDS. Functional studies revealed the involvement of centriole elongation, transition zone and intraflagellar transport defects in OFDS, thus characterising three ciliary protein modules: the complex KIAA0753-FOPNL-OFD1, a regulator of centriole elongation; the Meckel-Gruber syndrome module, a major component of the transition zone; and the CPLANE complex necessary for IFT-A assembly. OFDS now appear to be a distinct subgroup of ciliopathies with wide heterogeneity, which makes the initial classification obsolete. A clinical classification restricted to the three frequent/well-delineated subtypes could be proposed, and for patients who do not fit one of these three main subtypes, a further classification could be based on the genotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".