A targeted sequencing panel identifies rare damaging variants in multiple genes in the cranial neural tube defect, anencephaly
Bibliographic record
Abstract
Neural tube defects (NTDs) affecting the brain (anencephaly) are lethal before or at birth, whereas lower spinal defects (spina bifida) may lead to lifelong neurological handicap. Collectively, NTDs rank among the most common birth defects worldwide. This study focuses on anencephaly, which despite having a similar frequency to spina bifida and being the most common type of NTD observed in mouse models, has had more limited inclusion in genetic studies. A genetic influence is strongly implicated in determining risk of NTDs and a molecular diagnosis is of fundamental importance to families both in terms of understanding the origin of the condition and for managing future pregnancies. Here we used a custom panel of 191 NTD candidate genes to screen 90 patients with cranial NTDs (n = 85 anencephaly and n = 5 craniorachischisis) with a targeted exome sequencing platform. After filtering and comparing to our in-house control exome database (N = 509), we identified 397 rare variants (minor allele frequency, MAF < 1%), 21 of which were previously unreported and predicted damaging. This included 1 frameshift (PDGFRA), 2 stop-gained (MAT1A; NOS2) and 18 missense variations. Together with evidence for oligogenic inheritance, this study provides new information on the possible genetic causation of anencephaly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".