WNT4 deficiency impacts heart, diaphragm, and palate development: Insights from human genetics, machine learning, and mouse models
Bibliographic record
Abstract
WNT4 is a secreted protein that plays a critical role in the regulation of cell fate and embryogenesis. Biallelic variants in WNT4 have been linked to SERKAL syndrome, an autosomal recessive disorder characterized by 46,XX sex reversal and dysgenesis of the kidneys, adrenals, and lungs. SERKAL syndrome has only been described in a single consanguineous kindred with four affected fetuses. Additional features seen in a subset of affected fetuses included ventricular septal defect (VSD), congenital diaphragmatic hernia (CDH), and orofacial clefting (OFC). To determine if these additional features were likely to be caused by WNT4 deficiency, we used machine learning to compare WNT4 to genes known to cause VSD, CDH, and OFC. When compared to all RefSeq genes, WNT4’s rank annotation scores for these congenital anomalies were 94%, 99%, and 98.5%, respectively, indicating a high level of similarity. We subsequently identified a second consanguineous family with SERKAL syndrome in which an affected fetus had CDH and an affected child had OFC. We then demonstrated that a subset of Wnt4 null embryos have perimembranous VSDs, anterior and posterior sac CDH, and soft palate clefts. These findings suggest that WNT4 deficiency can cause VSD, CDH, and palatal anomalies in mice and humans with SERKAL syndrome. These studies also suggest that our machine learning approach can be used as a candidate gene prioritization tool, and that targeted mouse phenotyping can serve as a means of confirming the roles of candidate genes in mammalian development. • We identify a second family with biallelic WNT4 variants causing SERKAL syndrome • WNT4 deficiency causes cardiac, diaphragmic, and palatal anomalies • Machine learning can be used to prioritize candidate genes for further studies • Targeted mouse phenotyping can confirm the roles of genes in mammalian development
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".