Rare missense variants in <i>DVL1</i>, one of the human counterparts of the <i>Drosophila dishevelled</i> gene, do not confer increased risk for neural tube defects
Bibliographic record
Abstract
BACKGROUND: Neural tube defects (NTDs) are severe malformations that arise when the neural tube fails to close during embryogenesis. The planar cell polarity pathway is involved in neural tube closure and has been implicated in the pathogenesis of NTDs both in animal models and human cohorts. Dishevelled (Dvl/Dsh) is a multi-module protein and a key regulator of both the canonical Wnt and the PCP pathway. In mouse, all Dvl1(-/-) ; Dvl2(-/-) double mutants display craniorachischisis, a severe form of open NTDs. Recently, we have reported a possible role for rare variants of DVL2 as risk factors for NTDs. METHODS: In view of these data, we hypothesized that DVL1 mutations might increase the risk for NTDs in some cases. Resequencing of the DVL1 gene in a cohort of 473 NTDs patients and 150 ethnically matched controls was performed. Prediction of the downstream effects of the nonsynonymous variants was done using computational methods. RESULTS: We identified six missense variants that were absent in our ethnically matched controls group, and four of them (p.Arg153Cys; p.Glu544Arg; p.Arg568Trp; p.Val644Phe) were predicted to have a functional effect on protein structure by one or more bioinformatic programs. However, there was no difference in the overall rate of deleterious variants between the patients and controls (four in patients and three in controls; p=0.36). CONCLUSION: Our findings did not provide evidence for the implication of DVL1 in the pathogenesis of human NTDs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".