Evidence for enhancer noncoding RNAs (enhancer-ncRNAs) with gene regulatory functions relevant to neurodevelopmental disorders
Bibliographic record
Abstract
Abstract Noncoding RNAs (ncRNAs) comprise a significant proportion of the mammalian genome, but their biological significance in neurodevelopment disorders is poorly understood. In this study, we identified 908 brain-enriched noncoding RNAs comprising at least one nervous system-related eQTL polymorphism that is associated with protein coding genes and also overlap with chromatin states characterised as enhancers. We referred to such noncoding RNAs with putative enhancer activity as brain ‘enhancer-ncRNAs’. By integrating GWAS SNPs and Copy Number Variation (CNV) data from neurodevelopment disorders, we found that 265 enhancer-ncRNAs were either mutated (CNV deletion or duplication) or contain at least one GWAS SNPs in the context of such conditions. Of these, the eQTL-associated gene for 82 enhancer-ncRNAs did not overlap with either GWAS SNPs or CNVs suggesting in such contexts that mutations to neurodevelopment gene enhancers disrupt ncRNA interaction. Taken together, we identified 49 novel NDD-associated ncRNAs that influence genomic enhancers during neurodevelopment, suggesting enhancer mutations may be relevant to the functions for such ncRNAs in neurodevelopmental disorders.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".