De novo large rare copy-number variations contribute to conotruncal heart disease in Chinese patients
Bibliographic record
Abstract
Abstract Conotruncal heart anomalies (CTDs) are particularly prevalent congenital heart diseases (CHD) in Hong Kong. We surveyed large (>500 kb), rare (<1% frequency in controls) copy-number variations (CNVs) in Chinese patients with CTDs to identify potentially disease-causing variations. Adults who tested negative for 22q11.2 deletions were recruited from the adult CHD clinic in Hong Kong. Using a stringent calling criteria, high-confidence CNV calls were obtained, and a large control set comprising 3,987 Caucasian and 1,945 Singapore Chinese subjects was used to identify rare CNVs. Ten large rare CNVs were identified, and 3 in 108 individuals were confirmed to harbour de novo CNVs. All three patients were syndromic with a more complex phenotype, and each of these CNVs overlapped regions likely to be important in CHD. One was a 611 kb deletion at 17p13.3, telomeric to the Miller–Dieker syndrome (MDS) critical region, overlapping the NXN gene. Another was a 5 Mb deletion at 13q33.3, within a previously described critical region for CHD. A third CNV, previously unreported, was a large duplication at 2q22.3 overlapping the ZEB2 gene. The commonly reported 1q21.1 recurrent duplication was not observed in this Chinese cohort. We provide detailed phenotypic and genotypic descriptions of large rare genic CNVs that may represent CHD loci in the East Asian population. Larger samples of Chinese origin will be required to determine whether the genome-wide distribution differs from that found in predominantly European CHD cohorts.
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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 | 0.001 | 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) | 0.002 | 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".