Co-expression of prepulse inhibition and Schizophrenia genes in the mouse and human brain
Bibliographic record
Abstract
Schizophrenia is a complex psychiatric disorder with genetic and phenotypic heterogeneity. Accumulating rare and genome-wide association study (GWAS) common risk variant information has yet to yield robust mechanistic insight. Leveraging large-scale gene deletion mouse phenomic data thus has potential to functionally interrogate and prioritize human disease genes. To this end, we applied a cross-species network-based approach to parse an extensive mouse gene set (188 genes) associated with disrupted prepulse inhibition (PPI), a Schizophrenia endophenotype. Integrating PPI genes with high-resolution mouse and human brain transcriptomic data, we identified functional and disease coherent co-expression modules through hierarchical clustering and weighted gene co-expression network analysis (WGCNA). In two modules, Schizophrenia risk and mouse PPI genes converged based on telencephalic patterning. The associated neuronal genes were highly expressed in cingulate cortex- and hippocampus; implicated in synaptic function and neurotransmission and overlapped with the greatest proportion of rare variants. Concordant neuroanatomical patterning revealed novel core Schizophrenia-relevant genes consistent with the Omnigenic hypothesis of complex traits. Among other genes discussed, the developmental and post-synaptic scaffold TANC2 (Tetratricopeptide repeat, ankyrin repeat and coiled-coil containing 2) emerged from both networks as a novel core genetic driver of Schizophrenia altering PPI. Aspects of psychiatric disease comorbidity and phenotypic heterogeneity are also explored. Overall, this study provides a framework and galvanizes the value of mouse preclinical genetics and PPI to prioritize both existing and novel human Schizophrenia candidate genes as druggable targets.
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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 | 0.001 | 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".