Overlapping pathogenic de novo CNVs in neurodevelopmental disorders and congenital anomalies impacting constraint genes regulating early development
Bibliographic record
Abstract
Abstract Neurodevelopmental disorders (NDDs) and congenital anomalies (CAs) are a collection of rare disorders with complex etiology. In this study, we investigated the less understood genomic overlap of copy number variants (CNVs) in two large cohorts of NDD and CA patients to identify de novo CNVs and candidate genes associated to both NDD and CA phenotypes. We analyzed clinical microarray CNV data from 10,620 NDD and 3,176 CA cases annotated using Horizon platform of GenomeArc Analytics and then applied rigorous downstream analysis to evaluate overlapping genes from NDD and CA CNVs. Out of 154 patients (NDD and CA cases), we extracted genes from overlapping 217 pathogenic de novo CNVs (47.3% (80/169) of NDD and 64.6% (31/48) CA cases) and performed constraint gene analysis. 79 constraint genes (10.1% non-OMIM entries) were found to have significantly enriched genomic overlap within rare de novo pathogenic deletions (P-value = 0.01, OR = 1.58) and 45 constraint genes (13.3% non-OMIM entries) within rare de novo pathogenic duplications (P-value = 0.01, OR = 1.97). Analysis of spatiotemporal transcriptome demonstrated both pathogenic deletion and duplication genes to be highly expressed during the prenatal stage in human developmental brain (P-value = 4.95 X 10− 6). From the list of overlapping genes, EHMT1, an interesting known NDD gene encompassed de novo pathogenic CNVs from both NDD and CA patients, whereas FAM189A1, and FSTL5 are new candidate genes from non-OMIM entries. In summary, we have identified constraint overlapping genes from CNVs in NDD and CA patients that have the potential to play a vital role in common disease etiology.
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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 | 0.002 | 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) | 0.002 | 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".