Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.

(A) Model of psychiatric disease pathogenesis. (B) Flowchart of the cross-disorder transcriptome analysis pipeline (12). Cortical gene expression datasets were compiled from cases of ASD (n=50 samples), SCZ (n=159), BD (n=94), MDD (n=87), AAD (n=17), and matched non-psychiatric controls (n=293) (12) (see Table S1).

Cortical gene expression patterns overlap. (A) Rank order of microarray transcriptome similarity for all disease pairs, as measured by Spearman’s correlation of differential expression log2FC values. (B) Comparison of the slopes among significantly associated disease pairs indicates a gradient of transcriptomic severity, with ASD > SCZ ~ BD > MDD. (C) Overlapping gene expression patterns across diseases are correlated with shared common genetic variation, as measured by SNP co-heritability (22). The Y-axis shows transcriptome correlations using microarray-based (discovery, red) and RNAseq (replication, blue) datasets. (D) RNAseq across all cortical lobes in ASD replicates microarray results and demonstrates a consistent transcriptomic pattern. Spearman’s ρ is shown for comparison between microarray and region-specific RNAseq replication datasets (all P’s < 10−14). Plots show mean +/− SEM. *P < 0.05, **P < 0.01, ***P < 0.001.

Network analysis identifies modules of co-expressed genes across disease(A) Network dendrogram from co-expression topological overlap of genes across disorders. Color bars show correlation of gene expression with disease status, biological, and technical covariates. (B) Multidimensional scaling plot demonstrates relationship between modules and clustering by cell-type relationship. (C) Module-level differential expression is perturbed across disease states. Plots show beta values from linear mixed-effect model of module eigengene association with disease status (FDR-corrected #P<0.1, *P<0.05, **P<0.01, ***P<0.001). D) The top twenty hub genes are plotted for modules most disrupted in disease. See Data Table S2 for a complete list of genes’ module membership (kME). Edges are weighted by the strength of correlation between genes. Modules are characterized by (E) Gene Ontology enrichment (top two pathways shown for each module) and (F) cell-type specificity, on the basis of RNAseq of purified cell populations from healthy human brain samples (25).

Downregulated neuronal modules are enriched for common and rare genetic risk factors(A) Significant enrichment is observed for SCZ-, ASD-, and BD-associated common variants from GWAS among neuron/synapse & mitochondrial modules (12). GWAS datasets are listed in Table S3. (B) The CD1 neuronal module shows significant enrichment for ASD- and SCZ-associated non-synonymous de novo variants from whole exome sequencing. The number of genes affected by different classes of rare variants is shown in parentheses. Significance was calculated using logistic regression, correcting for gene length. P-values are FDR corrected. (C) Total SNP-based heritability (liability scale for psychiatric diagnoses) calculated from GWAS using LD-score regression. (D) Proportion of heritability for each disorder or trait that can be attributed to individual co-expression modules. Significance (FDR-corrected *P<0.05, **P<0.01, ***P<0.001) is from enrichment statistics comparing the proportion of SNP heritability within the module divided by the proportion of total SNPs represented. The CD1 module shows significant enrichment in SCZ, BD, and educational attainment.