TL;DR
A cross-disorder transcriptomic framework was applied to identify shared molecular dysregulation across ASD, BD, and SCZ and identified 20 high-confidence recurrent genes, with pronounced transcriptional convergence observed between BD and SCZ.
OBJECTIVE Understanding the molecular mechanisms of autism spectrum disorder (ASD) and its psychiatric comorbidities, including bipolar disorder (BD), is pivotal for uncovering pathways that shape neurodevelopmental trajectories and clinical heterogeneity. We aimed to identify ASD-specific gene-expression signatures and disrupted biological processes in prefrontal cortex, contrasting them with those observed in BD. METHODS We performed a comparative transcriptomic analysis of RNA-seq datasets from postmortem prefrontal cortex samples of individuals with ASD or BD and controls. Differential expression was assessed with DESeq2, including batch as a covariate in the BD model. Functional interpretation used Gene Ontology over-representation analysis, KEGG Gene Set Enrichment Analysis and gene-concept network visualization. RESULTS ASD samples showed 45 differentially expressed genes (DEGs), mainly downregulated non-coding RNAs, particularly small nuclear RNAs and small nucleolar RNAs. Enrichment analysis indicated a convergent profile related to RNA processing, spliceosome assembly and spliceosomal activity. In contrast, BD showed 12 candidate DEGs, mostly upregulated protein-coding genes. BD enrichment involved metal ion response and detoxification, amine and peptide hormone responses, vascular regulation and hydrolase activity, with genes associated with neuroinflammation such as SERPINA3 and CHI3L1 contributing to this profile. No shared DEGs or enriched GO Biological Process terms were observed between ASD and BD. CONCLUSION These results support transcriptomic divergence in the prefrontal cortex, with ASD characterized by spliceosomal dysregulation, contrasting with metal ion response, vascular regulation and inflammation-associated signals in BD. Our findings provide a transcriptomic framework for future studies investigating disorder-specific molecular mechanisms and candidate signatures in ASD and BD.
This study finds that rare variants across hundreds of genes contribute to autism with variable phenotypic outcomes, and clusters them based on association evidence from large-scale studies of developmental disorders, schizophrenia, bipolar disorder, and epilepsy.
The study provides an integrated framework linking genetic variation to molecular dysfunction and clinical outcomes, offering valuable insights for future research and therapeutic development in pediatric neurology.
Schizophrenia and bipolar disorder are diagnostically distinct categories that overlap substantially in clinical features and genetic aetiology. Understanding genetic variants that contribute liability specifically to each disorder can offer insights into biological processes that differentiate them. Here we used Case-Case GWAS (CC-GWAS) to identify common genetic variants differentially associated with schizophrenia and bipolar disorder, analysing 67,390 schizophrenia cases and 41,917 bipolar disorder cases. We identified 19 genome-wide significant loci, of which 16 (84%) demonstrated divergent genetic effects with risk alleles showing opposite directions of association between disorders. The CC-GWAS summary statistics had detectable disorder-differentiating heritability (10.27%, SE=0.01) and showed genetic correlations indicating that SCZ-differentiating alleles were associated with lower educational attainment, lower cognitive performance, and increased risk of ADHD, anorexia, autism, BD1 (though not BD2), cannabis use disorder, and OCD. Four loci showed divergent effects despite not reaching genome-wide significance in either individual disorder GWAS, demonstrating enhanced power to detect opposite-direction effects. Functional annotation identified 102 mapped genes significantly enriched for expression across all 13 tested brain regions, with no significant enrichment in peripheral tissues, and gene set enrichment analysis implicated neuronal projection and synaptic compartments as the strongest biological themes differentiating the two disorders. Polygenic risk scores derived from these disorder-differentiating variants were associated with earlier age at onset and more severe negative symptoms in schizophrenia, consistent with these variants marking neurodevelopmental dimensions of illness. Our findings provide targets for understanding pathogenic differences between schizophrenia and bipolar disorder and demonstrate that genuine divergent genetic effects exist beyond the substantial shared liability.
Integrative pathogenetic mechanisms of ASD are explored, encompassing prenatal viral exposure, alterations in serotonin and oxytocin signaling pathways, regulation of N-methyl-D-aspartate receptors, SHANK proteins, and members of the Solute Carrier protein family.
Schizophrenia (SCZ) and bipolar disorder (BIP) share substantial common-variant liability but differ in cognition, medical comorbidity, and treatment response. Here we decomposed this overlap into schizophrenia-predominant, bipolar-predominant, and shared psychosis dimensions to test whether these components show distinct pleiotropic and biological profiles. Using the largest available SCZ and BIP GWAS, we applied bidirectional mtCOJO and Genomic SEM to derive SCZcondBIP, BIPcondSCZ, and PSY-shared and validated them using inter-component genetic correlations, FinnGen psychiatric endpoints, and Genomic SEM latent factors. We then characterized each component across cognitive, cardiometabolic, and immune traits, followed by genomic risk-locus discovery, pathway analysis, developmental expression profiling, and drug-target enrichment. The three components showed marked divergence. SCZcondBIP was negatively genetically correlated with cognition, education, metabolic syndrome, C-reactive protein, and neutrophil percentage, whereas BIPcondSCZ showed the opposite cognitive profile and shifted toward positive cardiometabolic and immune correlations. PSY-shared retained the mixed cognitive pattern seen at the disorder level and intermediate peripheral correlations, indicating that shared psychosis liability masks stronger disorder-specific differences. Between-component contrasts were approximately twice the magnitude of the corresponding SCZ-versus-BIP contrasts. We identified 248 consensus genomic risk loci, including 81 not detected in the input disorder GWAS. Biologically, PSY-shared was enriched for synaptic signalling, ion-channel, and neurodevelopmental pathways; SCZcondBIP primarily implicated synaptic-signalling and cellular-homeostasis pathways; and BIPcondSCZ showed weaker but distinct enrichment for synaptic-vesicular biology. Drug-target enrichment further separated the components, with strong antipsychotic enrichment for PSY-shared and distinct non-antipsychotic signals for the conditional factors. These findings show that SCZ and BIP genetic risk is best understood as biologically distinguishable shared and disorder-predominant dimensions that differentially map onto cognitive, cardiometabolic, immune, and molecular architecture. These findings provide a framework for evaluating whether component-specific polygenic scores improve stratification of cognitive, cardiometabolic, and inflammatory heterogeneity across severe psychiatric illness.
