MicroRNA Dysregulation, Gene Networks, and Risk for Schizophrenia in 22q11.2 Deletion Syndrome
Bibliographic record
Abstract
The role of microRNAs (miRNAs) in the etiology of schizophrenia is increasingly recognized. Microdeletions at chromosome 22q11.2 are recurrent structural variants that impart a high risk for schizophrenia and are found in up to 1% of all patients with schizophrenia. The 22q11.2 deletion region overlaps gene DGCR8, encoding a subunit of the miRNA microprocessor complex. We identified miRNAs overlapped by the 22q11.2 microdeletion and for the first time investigated their predicted target genes, and those implicated by DGCR8, to identify targets that may be involved in the risk for schizophrenia. The 22q11.2 region encompasses seven validated or putative miRNA genes. Employing two standard prediction tools, we generated sets of predicted target genes. Functional enrichment profiles of the 22q11.2 region miRNA target genes suggested a role in neuronal processes and broader developmental pathways. We then constructed a protein interaction network of schizophrenia candidate genes and interaction partners relevant to brain function, independent of the 22q11.2 region miRNA mechanisms. We found that the predicted gene targets of the 22q11.2 deletion miRNAs, and targets of the genome-wide miRNAs predicted to be dysregulated by DGCR8 hemizygosity, were significantly represented in this schizophrenia network. The findings provide new insights into the pathway from 22q11.2 deletion to expression of schizophrenia, and suggest that hemizygosity of the 22q11.2 region may have downstream effects implicating genes elsewhere in the genome that are relevant to the general schizophrenia population. These data also provide further support for the notion that robust genetic findings in schizophrenia may converge on a reasonable number of final pathways.
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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".