Lin Eugene, Tsai Shih-Jen
Department of Biostatistics, University of Washington, Seattle, WA , USA.
Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
Psychiatry Investig. 2019 Sep;16(9):654-661. doi: 10.30773/pi.2019.07.17.2. Epub 2019 Aug 29.
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.
We focus on the most recent developments for genomic research in epigenetics and GxE interactions.
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.
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.
抑郁症与多种环境风险因素相关，如压力、童年虐待经历和应激性生活事件。目前评估抑郁症病理生理学的方法，如表观遗传学和基因-环境（GxE）相互作用，已被广泛用于确定抑郁症发病风险的合理标志物、基因和变体。
我们关注表观遗传学和GxE相互作用方面基因组研究的最新进展。
在本综述中，我们首先考虑GxE相互作用，调查了各种关于抑郁症的关联研究。然后，我们从动物模型和人体研究的角度，阐述了DNA甲基化、微小RNA和组蛋白修饰等表观遗传机制影响抑郁症的证据。最后，我们强调了它们的局限性和未来方向。
鉴于人工智能和机器学习领域的新兴技术，表观遗传学和GxE相互作用的未来研究有望实现新的创新，从而可能带来疾病预防以及抑郁症未来潜在的治疗方法。
