Dissecting the Shared Genetic Architecture Between Major Psychiatric Disorders and Metabolic Syndrome

Metabolic syndrome (MetS) and psychiatric disorders frequently co-occur, suggesting shared biological and genetic mechanisms. However, the genetic architecture underlying this comorbidity remains poorly understood. We performed a large-scale genome-wide investigation to explore the shared genetic architecture between MetS and ten major psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), alcohol dependence (ALC), anorexia nervosa (ANO), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive symptoms (OCS), post-traumatic stress disorder (PTSD), and schizophrenia (SCZ). Genetic correlations and genetic overlap were assessed using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and genetic analysis incorporating pleiotropy and annotation (GPA). For trait pairs with significant correlations and overlaps, pleiotropic variants were identified using the pleiotropic analysis under the composite null hypothesis (PLACO). Bayesian colocalization was applied to assess pleiotropic loci. Gene-level associations were assessed with the multi-marker analysis of genomic annotation (MAGMA). Tissue-specific heritability analysis (LDSC-SEG) was applied for tissue selection, followed by transcriptome-wide association analysis using joint-tissue imputation (TWAS-JTI) and fine-mapping with FOCUS to prioritize putative causal genes. Further integration analyses included gene set enrichment analysis (GSEA), single-cell RNA sequencing (scRNA-seq) analysis, protein-protein interaction (PPI) analysis, and druggability assessment to facilitate functional interpretation. We identified significant genetic correlations between MetS and seven psychiatric disorders. PLACO analysis revealed 7,622 pleiotropic SNPs, and Bayesian colocalization detected 114 pleiotropic loci. MAGMA identified 103 pleiotropic genes across five disorder pairs. Brain region-specific heritability was enriched for MDD-MetS and SCZ-MetS, particularly in seven regions. TWAS-JTI and FOCUS identified ASCC3 , VAMP2 , and FADS1 as putative causal genes for MDD-MetS, and gene SNX19 for SCZ-MetS. Functional annotations implicated these genes in synaptic transmission and lipid metabolism, with VAMP2 highlighted as a convergent molecular node. Our findings provide evidence of pleiotropic genetic effects contributing to MetS-psychiatric disorder comorbidity. This study advances understanding of shared neuro-metabolic mechanisms and highlights novel genetic targets for potential therapeutic interventions.