The contribution of de novo coding mutations to meningomyelocele
Bibliographic record
Abstract
Abstract Meningomyelocele (MM) is considered a genetically complex disease resulting from failure of neural tube closure (NTD). Patients display neuromotor disability and frequent hydrocephalus requiring ventricular shunting. A few proposed genes contribute to disease susceptibility, but most risk remains unexplained 1 . We postulated that de novo mutations (DNMs) under purifying selection contribute to MM risk 2 . Here we recruited a cohort of 851 MM trios requiring shunting at birth, compared with 732 control trios, and found that de novo likely gene disrupting or damaging missense mutations occur in approximately 22.3% of subjects, 28% of which are estimated to contribute to disease risk. The 187 genes with damaging DNMs collectively define networks including actin cytoskeleton and microtubule-based processes, axon guidance, and histone modification. Gene validation demonstrates partial or complete loss of function, impaired signaling and defective neural tube closure in Xenopus embryos. Our results suggest DNMs make key contributions to MM risk, and highlight critical pathways required for neural tube closure in human embryogenesis.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".