Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder

Area of Science:

• Genetics and Molecular Biology
• Neuroscience
• Metabolomics

Background:

• Age at depressive onset (AAO) influences major depressive disorder (MDD) symptomatology and outcomes.
• Integrating genome-wide association study (GWAS) with other omics data for AAO is underexplored.
• Novel susceptibility and resistance markers for MDD may be uncovered through multi-omics integration.

Purpose of the Study:

• To integrate genomics and metabolomics data using network analysis to characterize and differentiate MDD based on AAO.
• To identify novel genetic and metabolic biomarkers associated with early-onset versus adult-onset MDD.

Main Methods:

• Performed two GWAS for AAO as a continuous trait in adult cohorts (PGRN-AMPS and CO-MED).
• Integrated top GWAS variants with plasma metabolite data (153 metabolites).
• Utilized data-driven network analysis to characterize multi-omics signatures of early (<18 years) and adult-onset MDD.

Main Results:

• Identified a significant variant (p = 8.77 × 10−8) in an intron of SAMD3.
• In silico analysis showed top GWAS signals enriched for gene expression in the brain and during embryonic development.
• Network analysis revealed differential associations between variants (near INTU, FAT1, CNTN6, TM9SF2) and metabolites (phosphatidylcholines, carnitines, biogenic amines, amino acids) in early-onset versus adult-onset MDD.

Conclusions:

• Multi-omics integration successfully identified distinct biosignatures for early- and adult-onset MDD.
• These findings highlight potential biomarkers for MDD susceptibility and resistance.
• Future longitudinal studies with repeated omics and neuroimaging are needed to validate and characterize these biomarkers.