Polygenic risk score analysis for major depressive disorder in a Chinese cohort: single-phenotype and pathway-based approaches

Abstract Background Major depressive disorder (MDD) is a global mental health challenge characterized by high prevalence, disability, and recurrence, with its susceptibility showing substantial polygenic inheritance. Polygenic risk scores (PRS) provide a novel method to explore cumulative polygenic effects on MDD pathogenesis. Methods Initial study inclusion comprised 721 patients meeting Diagnostic and Statistical Manual of the American Psychiatric Association, fourth edition (DSM-IV) diagnostic criteria for MDD and 960 healthy controls. Using summary statistics from the Psychiatric Genomics Consortium (PGC) East Asian cohort for MDD and schizophrenia (SCZ) phenotypes, as well as from the BioBank Japan (BBJ) for insomnia and body mass index (BMI) phenotypes as the base dataset, we performed stringent quality control (QC) on the data using PLINK v1.90. Subsequently, PRSice v2.3.5 with its PRSet function was employed to generate optimal PRSs for the four phenotypes in QC-passed patients and controls, and pathway-specific PRSs based on Gene Ontology (GO) gene sets. Statistical comparisons of PRS differences between MDD patients and healthy controls were conducted using independent two-sample t-tests or Mann-Whitney U tests in R v4.3.0. Furthermore, logistic regression analyses, adjusted for age and sex as covariates, were performed to assess associations between phenotypic PRSs, pathway PRSs, and MDD status. Results A significant difference is demonstrated in MDD-PRS between patients and controls (p < 0.001), with a significant logistic prediction model (empirical p = 9.999×10⁻⁵, pseudo-R² = 2.3%). In contrast, cross-phenotype PRS models were not significant. Pathway analysis identified 18, 14, and 13 significant pathway PRSs in the Biological Process, Molecular Function, and Cellular Component GO subsets, respectively. Notably, most pathway PRS models exhibited greater explanatory power than the single-phenotype model. Conclusions These findings support the predictive value of MDD-PRS and highlight how pathway-specific PRS enhances understanding of MDD’s genetic basis, offering insights for future precision medicine approaches.