GWAS analysis of a depression cohort defined by an EHR-phenotyping algorithm reveals the role of immune regulations in depression risk

Introduction Depression is a common psychiatric disorder and a leading cause of disability. Large-scale genomic studies have identified common variants associated with depression. However, researchers often rely on self-reported phenotypes, domain expertise-defined rules, and simple diagnostic codes (ICD9, ICD10) to identify depression participants, which suffers from inconsistent cohort definition and limited sample sizes. Thus, there is a lack of validated, efficient EHR phenotyping algorithms that precisely recognize depression cases. Methods We implemented a validated EHR phenotyping algorithm to construct a cohort of individuals with depression (11,532 cases and 39,631 controls, total n = 51,163) and conducted a genome-wide association study (GWAS) using this cohort. We validated the EHR-derived depression cohort using LDSC regression, comparing genetic similarities between our cohort and existing large meta-analyses. Top-ranked SNPs were selected and annotated to investigate downstream biological pathways and potential mechanisms that interfere with depression susceptibility. Results Our study reproduced previously identified genetic associations ( PHF5A, KCNG2 ) with depression susceptibility. We also identified novel SNPs within the HLA region and IGVH region, suggesting an association between immune function and depression phenotype. We also demonstrated the robustness of the phenotyping algorithm through genetic correlation analysis (LDSC), showing a highly genetic similarity (rg = 0.8317, P = 1.7758e-11) between our cohort and large meta-analysis cohorts of major depressive disorder. Conclusion Our results demonstrate a robust validation of the EHR-based depression phenotyping algorithm using genetic analysis while providing novel genetic associations between depression and immune functions.