The Cardiac Genome Clinic: implementing genome sequencing in pediatric heart disease
Bibliographic record
Abstract
PURPOSE: This study investigated the diagnostic utility of nontargeted genomic testing in patients with pediatric heart disease. METHODS: We analyzed genome sequencing data of 111 families with cardiac lesions for rare, disease-associated variation. RESULTS: In 14 families (12.6%), we identified causative variants: seven were de novo (ANKRD11, KMT2D, NR2F2, POGZ, PTPN11, PURA, SALL1) and six were inherited from parents with no or subclinical heart phenotypes (FLT4, DNAH9, MYH11, NEXMIF, NIPBL, PTPN11). Outcome of the testing was associated with the presence of extracardiac features (p = 0.02), but not a positive family history for cardiac lesions (p = 0.67). We also report novel plausible gene-disease associations for tetralogy of Fallot/pulmonary stenosis (CDC42BPA, FGD5), hypoplastic left or right heart (SMARCC1, TLN2, TRPM4, VASP), congenitally corrected transposition of the great arteries (UBXN10), and early-onset cardiomyopathy (TPCN1). The identified candidate genes have critical functions in heart development, such as angiogenesis, mechanotransduction, regulation of heart size, chromatin remodeling, or ciliogenesis. CONCLUSION: This data set demonstrates the diagnostic and scientific value of genome sequencing in pediatric heart disease, anticipating its role as a first-tier diagnostic test. The genetic heterogeneity will necessitate large-scale genomic initiatives for delineating novel gene-disease associations.
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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 | 0.007 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".