miRNA and miRNA target genes in copy number variations occurring in individuals with intellectual disability
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are a family of short, non-coding RNAs modulating expression of human protein coding genes (miRNA target genes). Their dysfunction is associated with many human diseases, including neurodevelopmental disorders. It has been recently shown that genomic copy number variations (CNVs) can cause aberrant expression of integral miRNAs and their target genes, and contribute to intellectual disability (ID). RESULTS: To better understand the CNV-miRNA relationship in ID, we investigated the prevalence and function of miRNAs and miRNA target genes in five groups of CNVs. Three groups of CNVs were from 213 probands with ID (24 de novo CNVs, 46 familial and 216 common CNVs), one group of CNVs was from a cohort of 32 cognitively normal subjects (67 CNVs) and one group of CNVs represented 40 ID related syndromic regions listed in DECIPHER (30 CNVs) which served as positive controls for CNVs causing or predisposing to ID. Our results show that 1). The number of miRNAs is significantly higher in de novo or DECIPHER CNVs than in familial or common CNV subgroups (P < 0.01). 2). miRNAs with brain related functions are more prevalent in de novo CNV groups compared to common CNV groups. 3). More miRNA target genes are found in de novo, familial and DECIPHER CNVs than in the common CNV subgroup (P < 0.05). 4). The MAPK signaling cascade is found to be enriched among the miRNA target genes from de novo and DECIPHER CNV subgroups. CONCLUSIONS: Our findings reveal an increase in miRNA and miRNA target gene content in de novo versus common CNVs in subjects with ID. Their expression profile and participation in pathways support a possible role of miRNA copy number change in cognition and/or CNV-mediated developmental delay. Systematic analysis of expression/function of miRNAs in addition to coding genes integral to CNVs could uncover new causes of ID.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".