Novel <i>FREM1</i> mutations expand the phenotypic spectrum associated with manitoba‐oculo‐tricho‐anal (MOTA) syndrome and bifid nose renal agenesis anorectal malformations (BNAR) syndrome
Bibliographic record
Abstract
Loss of function mutations in FREM1 have been demonstrated in Manitoba-oculo-tricho-anal (MOTA) syndrome and Bifid Nose Renal Agenesis and Anorectal malformations (BNAR) syndrome, but the wider phenotypic spectrum that is associated with FREM1 mutations remains to be defined. We screened three probands with phenotypic features of MOTA syndrome. In one severely affected infant who was diagnosed with MOTA syndrome because of bilateral eyelid colobomas, a bifid nasal tip, hydrometrocolpos and vaginal atresia, we found two nonsense mutations that likely result in complete loss of FREM1 function. This infant also had renal dysplasia, a finding more consistent with BNAR syndrome. Another male who was homozygous for a novel stop mutation had an extensive eyelid colobomas, corneopalpebral synechiae, and unilateral renal agenesis. A third male child diagnosed with MOTA syndrome because of corneopalpebral synechiae and eyelid colobomas had a homozygous splice site mutation in FREM1. These cases illustrate that disruption of the FREM1 gene can produce a spectrum of clinical manifestations encompassing the previously described MOTA and BNAR syndromes, and that features of both syndromes may be seen in the same individual. The phenotype of FREM1-related disorders is thus more pleiotropic than for MOTA and BNAR syndrome alone and more closely resembles the widespread clinical involvement seen with Fraser syndrome. Moreover, our first case demonstrates that vaginal atresia may be a feature of FREM1-related disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".