4.7 Article

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

期刊

JOURNAL OF PERSONALIZED MEDICINE
卷 12, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/jpm12030412

关键词

genomics; metabolomics; major depressive disorder; age at onset; network analysis

资金

  1. Harry C. and Debra A. Stonecipher Predoctoral Fellowship at the Mayo Clinic Graduate School of Biomedical Sciences, National Science Foundation (NSF) [2041339]
  2. National Institutes of Health (NIH) [U19 GM61388, R01 GM028157, R01 AA027486, R01 MH108348, R24 GM078233, RC2 GM092729, U19 AG063744, N01 MH90003, R01 AG04617, U01 AG061359, RF1 AG051550, R01 MH113700, R01 MH124655, K23AI143882]
  3. Hersh Foundation
  4. Duke Psychiatry Pharmacometabolomics Center
  5. Mayo Clinic Center for Individualized Medicine

向作者/读者索取更多资源

This study integrated genomics with metabolomics to analyze the differences between early and adult-onset depression, and identified potential biomarkers related to the development and treatment of depression.
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 x 10(-8)) localized to an intron of SAMD3. In silico functional annotation of top signals (p <1 x 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.

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