Expression profiles of schizophrenia susceptibility genes during human prefrontal cortical development
Bibliographic record
Abstract
Background: Disruption in normal development of the human prefrontal cortex (PFC) may lead to cognitive dysfunction that manifests in individuals with schizophrenia. We sought to identify genes associated with age that are implicated in schizophrenia. Methods: We generated genome-wide expression profiles for the PFCs of humans ranging in age from 1 month to 49 years using the Affymetrix HG-U133 plus 2.0 microarrays (54 675 transcripts). Based on the criteria of significance (false discovery rate [FDR]-adjusted q < 0.001 and r2 > 0.6), we identified the genes associated with age in the PFC. We then performed functional annotation analyses of age-associated genes using the Gene Ontology and the Genetic Association Database (GAD). Results: We found robust age-dependent changes in gene expression in the PFCs of humans (2281 transcripts). The GAD analysis revealed that schizophrenia was an over-represented disease class, with 42 susceptibility genes included ( p < 0.001, fold enrichment = 1.66, FDR = 1.5%). Among the 42 genes, glutamate receptor genes ( GRIA1, GRIK1, GRIK2, GRIN2D, GRIP1, GRM5, GRM7 and SLC1A6) were consistently downregulated across age. We confirmed microarray gene expression changes by the quantitative polymerase chain reaction experiment. Limitations: Although numerous genes undergo robust changes in expression during the PFC development, some of the changes may be confounded by known and unknown factors that are intrinsic to the postmortem brain studies. Conclusion: Multiple schizophrenia susceptibility genes undergo age-dependent expression changes in the human PFC, and any disruption in those genes during the critical period of development may predispose the individuals to schizophrenia.
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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 | 0.000 |
| 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) | 0.001 | 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".