Psychiatry Investig.  2019 Sep;16(9):654-661. 10.30773/pi.2019.07.17.2.

Epigenetics and Depression: An Update

Affiliations
  • 1Department of Biostatistics, University of Washington, Seattle, WA , USA.
  • 2Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
  • 3Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.
  • 4Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan. tsai610913@gmail.com
  • 5Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan.
  • 6Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.

Abstract


OBJECTIVE
Depression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics and gene-environment (GxE) interactions, have been widely leveraged to determine plausible markers, genes, and variants for the risk of developing depression.
METHODS
We focus on the most recent developments for genomic research in epigenetics and GxE interactions.
RESULTS
In this review, we first survey a variety of association studies regarding depression with consideration of GxE interactions. We then illustrate evidence of epigenetic mechanisms such as DNA methylation, microRNAs, and histone modifications to influence depression in terms of animal models and human studies. Finally, we highlight their limitations and future directions.
CONCLUSION
In light of emerging technologies in artificial intelligence and machine learning, future research in epigenetics and GxE interactions promises to achieve novel innovations that may lead to disease prevention and future potential therapeutic treatments for depression.

Keyword

Depression; Biomarkers; Epigenetics; Gene-environment interactions; Stress

MeSH Terms

Artificial Intelligence
Biomarkers
Depression*
DNA Methylation
Epigenomics*
Gene-Environment Interaction
Histone Code
Humans
Machine Learning
MicroRNAs
Models, Animal
Risk Factors
Biomarkers
MicroRNAs
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