ArticleFrontiers in genetics2026
GWAS analysis of a depression cohort defined by an EHR-phenotyping algorithm reveals the role of immune regulations in depression risk.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
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 ( 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.
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