Identification of Long Noncoding RNA Candidate Disease Genes Associated With Clinically Reported Copy Number Variants in Congenital Heart Disease
Bibliographic record
Abstract
Background Copy number variants (CNVs) contribute to 3% to 10% of isolated congenital heart disease (CHD) cases, yet their pathogenic roles remain unclear. Diagnostic efforts have focused on protein‐coding genes, largely overlooking long noncoding RNAs (lncRNAs), which play key roles in development and disease. Methods AND RESULTS We systematically analyzed lncRNAs overlapping clinically validated CNVs in 743 patients with CHD from the Cytogenomics of Cardiovascular Malformations Consortium. We identified heart‐expressed lncRNAs, constructed a gene regulatory network using weighted gene coexpression network analysis, and identified gene modules associated with heart development. Functional enrichment and network analyses were used to identify lncRNAs that may be involved in heart development and potentially contribute to CHD. The code is stably archived at https://doi.org/10.5281/zenodo.13799779 . We identified 18 lncRNA candidate genes within modules significantly correlated with heart tissue, highlighting their potential involvement in CHD pathogenesis. Notably, lncRNAs such as lnc‐STK32C‐3, lnc‐TBX20‐1 , and CRMA demonstrated strong associations with known CHD genes. Strikingly, although only 7.6% of known CHD genes were affected by a CNV, 68.8% of the CNVs contained a lncRNA expressed in the heart. Conclusions Using weighted gene coexpression network analysis, we identified CNV‐associated lncRNAs with potential relevance to CHD, underscoring the complexities of noncoding regions in disease pathogenesis. These findings suggest that lncRNAs may play a greater role in CHD than previously recognized, highlighting the need for broader genomic analyses that extend beyond protein‐coding genes. This study provides a foundation for further exploration of lncRNAs in CHD, with potential implications for improved genetic characterization and diagnosis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".