Clinical genetic risk variants inform a functional protein interaction network for tetralogy of Fallot
Bibliographic record
Abstract
Abstract Background Tetralogy of Fallot (TOF), the most common cyanotic heart defect in newborns, has evidence of multiple genetic contributing factors. Identifying variants that are clinically relevant is essential to understand patient-specific disease susceptibility and outcomes, and could contribute to delineating pathomechanisms. Methods and Results We used a clinically-driven strategy and current guidelines to re-analyze exome sequencing data from 811 probands with TOF, focused on identifying rare loss-of-function and other likely pathogenic variants in congenital heart disease (CHD) genes. In addition to confirming a major contribution of likely pathogenic variants in FLT4 (VEGFR3; n=14) and NOTCH1 (n=11), we identified 1-3 such variants in each of 21 other CHD genes, including ATRX, DLL4, EP300, GATA6, JAG1, NF1, PIK3CA, RAF1, RASA1, SMAD2 , and TBX1 . There were also three emerging CHD/TOF candidate genes with multiple loss-of-function variants in this cohort: KDR (n=4), IQGAP1 (n=3), and GDF1 (n=8). In total, these variants were identified in 64 probands (7.9%). Using the 26 composite genes in a STRING protein interaction enrichment analysis revealed a biologically relevant network (p-value 3.3e-16), with VEGFR2 ( KDR ) and NOTCH1 representing central nodes. Variants associated with arrhythmias/sudden death and/or heart failure indicated factors that could influence long-term outcomes. Conclusions The results are relevant to precision medicine for TOF. They suggest considerable clinical yield from genome-wide sequencing, and further evidence for KDR as a CHD/TOF gene and VEGF and Notch signaling as mechanisms in human disease. Harnessing genetic heterogeneity of single gene defects could inform etiopathogenesis and help prioritize novel candidate genes for 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".