Identifying and Characterising Common Genetic Differences in Schizophrenia and Bipolar Disorder

Genomic dimensions deconstruct the clinical heterogeneity of bipolar disorder

Bipolar disorder’s (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder—and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic–GABAergic gradient along the psychotic factor. BD’s genetic architecture appears hierarchical—a general liability resolving into dimensions of course and comorbidity, beyond subtypes.

755. Educational attainment–related genetic and structural divergences between schizophrenia and bipolar disorder

Abstract Background Schizophrenia (SCZ) and bipolar disorder (BD) share substantial genetic overlap and both involve cognitive impairments. Educational attainment (EDU), a key proxy for cognition, differs significantly between the two disorders. However, how the genetic architecture linking EDU to SCZ and BD diverges remains unclear. Aims & Objectives The aim of this study is to systematically investigate and compare the genetic architecture linking educational attainment (EDU) with schizophrenia (SCZ) and bipolar disorder (BD), and to elucidate the neurobiological mechanisms underlying their shared and distinct cognitive and psychiatric risk pathways. Method We utilized large-scale genome-wide association study (GWAS) summary statistics to characterize global, local, and functional genetic correlations among EDU, SCZ, and BD. Cross-trait meta-analyses, colocalization, and bidirectional Mendelian randomization (MR) were used to identify shared loci and causal relationships. Tissue- and cell-type enrichment and summary-data-based MR analyses highlighted functional convergence in brain regions. Guided by these findings, neuroimaging analysis including 50 SCZ patients, 35 BD patients, and 60 healthy controls was performed whether EDU–disorder associations were mediated by gray matter volume (GMV). Results EDU showed a stronger genetic correlation with SCZ than BD. Cross-trait meta-analysis identified 32 shared SNPs for EDU-SCZ and 8 for EDU-BD. Bidirectional MR indicated a causal association between EDU and SCZ. EDU, SCZ, and BD shared heritability enrichment in the brain cortex, prefrontal cortex, and anterior cingulate cortex, with distinct cell-type enrichments. WBP2NL and NAGA were identified as functional genes shared by EDU and SCZ across multiple brain regions. Neuroimaging analysis indicated that EDU positively associated with the GMV in the left parahippocampal gyrus which also mediated the EDU-BD relationship. Discussion & Conclusions EDU shows broader and more convergent genetic associations with SCZ, whereas in BD, its influence may operate through specific neurostructural pathways, highlighting distinct mechanisms linking cognitive potential to psychiatric vulnerability.

Common genetic variants are associated with increased likelihood of latent co-occurring neurodevelopmental and mental health factors among autistic individuals.

Autistic individuals show elevated rates of co-occurring neurodevelopmental and mental health conditions, yet the genetic architecture of those comorbidities remains unclear. Using phenotypic (N = 74,204) and genetic (N = 17,582) data from the SPARK study, we investigated the factor structure, heritability, genetic correlation with autism (pleiotropy) and corresponding conditions in the general population (additivity). First, confirmatory factor analysis identified three correlated factors mirroring general population patterns: behavioural (ADHD, disruptive behaviour disorders), cothymic (depression, anxiety), and thought disorder (schizophrenia, bipolar). Second, all three factors had significant SNP heritabilities whilst rare variants were not associated with the tested factors in our sample. Third, polygenic scores and genetic correlations revealed positive shared genetics between the three factors and corresponding conditions in the general population but not with autism, supporting the additivity hypothesis. Fourth, within-family analyses (N = 5236 trios) demonstrated direct but not indirect genetic effects for the behavioural and cothymic factors. In sum, we find evidence for additive effects of other genetic factors in contributing to some latent co-occurring neurodevelopmental and mental health conditions in autism.

Pleiotropic genetic architecture linking schizophrenia and substance use disorders

712. Comorbid mental and substance use disorders – the role of genetic susceptibility

Abstract Background Alcohol use disorder (AUD) and alcohol consumption (AC) are highly heritable, globally burdensome, and frequently comorbid with severe psychiatric disorders like schizophrenia (SCZ) and bipolar disorder (BD). While these comorbidities are often linked to greater illness severity, it remains unclear whether they arise solely as complications of substance use or from a shared underlying genetic architecture. Aims & Objectives The overall aim was to leverage massive, diverse datasets and novel statistical frameworks to: i) Identify novel genetic loci associated with a narrow AUD phenotype across multiple ancestries. ii) Characterize the shared genomic loci and polygenic overlap between alcohol traits (AUD/AC) and psychiatric phenotypes (SCZ/BD). iii) Map identified variants to biological pathways and brain regions to uncover potential drug targets. Method A multi-ancestry GWAS was conducted on 1,041,450 individuals (including European, African, Hispanic, and Asian ancestries) using novel statistical tools and cross-ancestry functional analyses. We also used European-ancestry summary statistics (AUD: 34,658 cases; AC: n=200,680; SCZ: 31,013 cases; BD: 20,352 cases), and applied conjunctional False Discovery Rate (conjFDR) analysis to increase the power to detect shared genomic loci. The identified loci were mapped to gene expression data in the brain and examined for enrichment in specific neuronal pathways (GABAergic, dopaminergic, serotonergic) and immune-related gene sets. Results The multi-ancestry analysis identified 37 genome-wide significant loci, including seven novel for AUD. The conjFDR analysis further identified 28 loci shared between SCZ and AUD, and 2 loci shared between BD and AUD, many of which were previously unknown for these phenotypes. Loci were mapped to genes with altered expression in the striatum, hypothalamus, and prefrontal cortex. While European and African samples showed distinct immune-related patterns, shared loci between AUD and psychiatric disorders exhibited a complex mixture of both same and opposite effect directions. Extensive positive genetic correlations and polygenic overlap were found between AUD and both mental and general medical phenotypes, confirming that AUD shares a significant genetic liability with these conditions. Discussion & Conclusions These findings underscore the value of multi-ancestry and cross-disorder genetic studies in SUD. By identifying shared and novel genomic loci, we demonstrates that the relationship between alcohol use and psychiatric disorders is driven by a complex, shared genetic architecture rather than environmental complications alone. This advances our understanding of AUD risk and highlights potential neuronal and immune pathways for future clinical intervention.

Rare variation illuminates the distinct and pleiotropic genetic architecture of autism across neuropsychiatric traits

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.