Abstract
Many psychiatric disorders are heritable, but the molecular consequences of genetic risk remain difficult to resolve, in part due to environmental confounds and the complexity of transcriptomic data. This challenge impedes therapeutic development, which relies on integrating genetic and genomic insights. Here, we integrate diagnosis, toxicological exposure, and gene expression to clarify disease-associated transcriptomic patterns in the subgenual anterior cingulate cortex (sgACC), a brain region implicated in affective regulation and psychiatric illness. We applied group regularized canonical correlation analysis (GRCCA)-a multivariate regression method that models interdependent features-to deeply sequenced bulk RNA-seq data from individuals with bipolar disorder (BD; N = 35), major depression (MDD; N = 51), schizophrenia (SCZ; N = 44), and controls (N = 55). Toxicology data from 17 known compounds were included to assess the relative contribution of known environmental exposures. Case-control expression changes were also analyzed using traditional differential gene expression (DGE) analysis to compare biological interpretability across methods. Gene set enrichment analyses evaluated enrichments for neuropsychiatric risk genes, gene ontology pathways, and cell type markers. GRCCA identified a latent variable significantly associated with schizophrenia (p(perm) = 0.001). This expression pattern was enriched for upregulated neuronal pathways, downregulated immune processes, and genes within loci associated with schizophrenia by GWAS. While DGE results were correlated (r = 0.43; p(perm) = 1.0 × 10(-4)) and enriched for similar functional pathways, GRCCA showed stronger alignment with schizophrenia risk genes implicated by genome-wide association studies. Together, these findings define a schizophrenia-associated expression gradient in the sgACC and illustrate how multivariate integration can refine transcriptomic signals in the context of complex psychiatric disease.