Reported race-associated differences in control and schizophrenia post-mortem brain transcriptomes implicate stress-related and neuroimmune pathways
Bibliographic record
Abstract
Background: The molecular mechanisms underlying racial disparities in schizophrenia (SCZ) illness courses and outcomes are poorly understood. While these differences are thought to arise partly through stressful social gradients, little is known about how these differences are reflected in the brain, nor how they might underlie disparate psychiatric outcomes. Methods: To better understand the neuro-molecular correlates of social gradients, SCZ, and their overlap, we analyzed post-mortem dorsolateral prefrontal cortex (DLPFC) RNAseq data from two racially diverse cohorts in the CommonMind Consortium (235 reported Black and 546 White, 322 SCZ cases and 459 controls) using differential expression and gene set variation analyses. Results: We observed differences in brain gene expression that were consistent across cohorts and reported race. A combined mega-analysis identified 1,514 genes with differential expression (DE) between reported race groups after accounting for diagnosis and other covariates. Functional enrichment analyses identified upregulation of genes involved in stress and immune response, highlighting the potential role of environmental differences between reported race groups. In a race-by-diagnosis interaction analysis, no individual genes passed statistical significance. However, 109 gene sets showed statistically significant differences, implicating metabolic and immune pathways. Conclusion: Our results suggest molecular mechanisms uniquely perturbed across reported race groups and identify several candidate pathways associated with SCZ in a reported race-dependent manner. Our results underscore the importance of diverse cohort ascertainment to better capture population-level differences in SCZ pathogenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".