T7. PLASMA PROTEOMIC SIGNATURES UNDERLYING THE POLYGENIC SCORES OF MAJOR PSYCHIATRIC DISORDERS

Background Large-scale genetic studies have substantially advanced our understanding of the genetic architecture of major psychiatric disorders by identifying multiple genetic variants, demonstrating complex polygenicity and revealing strong genetic overlap across these disorders. Despite these efforts, the functional interpretation of polygenic scores (PGSs), including their downstream molecular and clinical implications, remains poorly characterised. In this study, we conducted proteome-wide association studies (PWASs) to evaluate the associations between PGSs for major psychiatric disorders and the level of plasma proteins. Methods Using the UK Biobank study (N=45,902) as the target dataset, we developed PGSs for 13 psychiatric disorders and examined their associations with each of the 2,920 circulating proteins assayed. The 13 psychiatric disorders include major depressive disorder (MDD), bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), panic disorder, obsessive–compulsive disorder, Tourette’s syndrome, PTSD, anorexia nervosa, nicotine dependence, alcohol-use disorder, and cannabis-use disorder. The PGSs were computed using standard methods using GWAS summaries obtained from the GWAS Catalogue and Psychiatric Genomics Consortium by excluding UKB participants. The associations analysis was conducted using linear regression models, adjusting for age, sex, age², age × sex, age² × sex, batch effect, time gap between blood collection and protein measurement, assessment centre and the first 20 genetic principal components (PCs). Normalised protein expression values were inverse rank-transformed before statistical analysis. Significant PGS-protein associations were defined using a Bonferroni correction accounting for 2920 proteins (P-value < 1.71 × 10^(-5)). Then, we evaluated proteomics overlap across these disorders and performed functional analysis to characterise the role of these proteins in biological processes. Finally, a protein-to-drug target analysis was conducted. Results We identified a set of plasma proteins significantly associated with PGS for each of the psychiatric disorders, with substantial proteomics overlap across different disorders. The strongest and most consistent signals included CXCL17, FABP4, ADM, FSTL3, RNASE6, RBFOX3, and TNFRSF1A. These top proteins were enriched for immune, inflammatory, and metabolic pathways. A number of these proteins have shared associations across disorders. Of which, proteins in the HLA region (e.g., HLA-A, MICB/MICA, BTN3A2, GGT1, and GLA) were significantly associated with the PGSs for nine psychiatric disorders. Most shared associations were directionally consistent, although there were some inverse associations, suggesting partially divergent biological mechanisms. Discussion Our findings identify a set of top plasma proteins that capture the biological imprint of polygenic risk for major psychiatric disorders. These proteins implicate immune and metabolic dysregulation as central mechanisms and reveal a pattern of shared liability between psychiatric disorders. The identified proteins provide promising targets for mechanistic studies and biomarker development.