Haploinsufficiency of vascular endothelial growth factor related signaling genes is associated with tetralogy of Fallot
Bibliographic record
Abstract
PURPOSE: To determine disease-associated single-gene variants in conotruncal defects, particularly tetralogy of Fallot (TOF). METHODS: We analyzed for rare loss-of-function and deleterious variants in FLT4 (VEGFR3) and other genes in the vascular endothelial growth factor (VEGF) pathway, as part of a genome sequencing study involving 175 adults with TOF from a single site. RESULTS: We identified nine (5.1%) probands with novel FLT4 variants: seven loss-of-function, including an 8-kb deletion, and two predicted damaging. In ten other probands we found likely disruptive variants in VEGF-related genes: KDR (VEGFR2; two stopgain and two nonsynonymous variants), VEGFA, FGD5, BCAR1, IQGAP1, FOXO1, and PRDM1. Detection of VEGF-related variants (19/175, 10.9%) was associated with an increased prevalence of absent pulmonary valve (26.3% vs. 3.4%, p < 0.0001) and right aortic arch (52.6% vs. 29.1%, p = 0.029). Extracardiac anomalies were rare. In an attempt to replicate findings, we identified three loss-of-function or damaging variants in FLT4, KDR, and IQGAP1 in ten independent families with TOF. CONCLUSION: Loss-of-function variants in FLT4 and KDR contribute substantially to the genetic basis of TOF. The findings support dysregulated VEGF signaling as a novel mechanism contributing to the pathogenesis of TOF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".