Rare Copy Number Variations in Adults with Tetralogy of Fallot Implicate Novel Risk Gene Pathways
Bibliographic record
Abstract
Structural genetic changes, especially copy number variants (CNVs), represent a major source of genetic variation contributing to human disease. Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease, but to date little is known about the role of CNVs in the etiology of TOF. Using high-resolution genome-wide microarrays and stringent calling methods, we investigated rare CNVs in a prospectively recruited cohort of 433 unrelated adults with TOF and/or pulmonary atresia at a single centre. We excluded those with recognized syndromes, including 22q11.2 deletion syndrome. We identified candidate genes for TOF based on converging evidence between rare CNVs that overlapped the same gene in unrelated individuals and from pathway analyses comparing rare CNVs in TOF cases to those in epidemiologic controls. Even after excluding the 53 (10.7%) subjects with 22q11.2 deletions, we found that adults with TOF had a greater burden of large rare genic CNVs compared to controls (8.82% vs. 4.33%, p = 0.0117). Six loci showed evidence for recurrence in TOF or related congenital heart disease, including typical 1q21.1 duplications in four (1.18%) of 340 Caucasian probands. The rare CNVs implicated novel candidate genes of interest for TOF, including PLXNA2, a gene involved in semaphorin signaling. Independent pathway analyses highlighted developmental processes as potential contributors to the pathogenesis of TOF. These results indicate that individually rare CNVs are collectively significant contributors to the genetic burden of TOF. Further, the data provide new evidence for dosage sensitive genes in PLXNA2-semaphorin signaling and related developmental processes in human cardiovascular development, consistent with previous animal models.
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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) | 0.001 | 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".