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

Caroline W. Grant, Erin F. Barreto, Rakesh Kumar, Rima Kaddurah-Daouk, Michelle Skime, Taryn Mayes, Thomas Carmody, Joanna Biernacka, Liewei Wang, Richard Weinshilboum, Madhukar H Trivedi, William V. Bobo, Paul E. Croarkin, Arjun P. Athreya

Research output: Contribution to journalArticlepeer-review

Abstract

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early-compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early-and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated-omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Original languageEnglish (US)
Article number412
JournalJournal of Personalized Medicine
Volume12
Issue number3
DOIs
StatePublished - Mar 2022

Keywords

  • Age at onset
  • Genomics
  • Major depressive disorder
  • Metabolomics
  • Network analysis

ASJC Scopus subject areas

  • Medicine (miscellaneous)

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