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酒精使用障碍的人类遗传学和表观遗传学。

Human genetics and epigenetics of alcohol use disorder.

机构信息

Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA.

Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA.

出版信息

J Clin Invest. 2024 Aug 15;134(16):e172885. doi: 10.1172/JCI172885.

Abstract

Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.

摘要

酒精使用障碍(AUD)是全球发病率和死亡率的主要原因。其复杂的病因涉及遗传、表观遗传和环境因素。我们回顾了 AUD 的遗传学和表观遗传学的研究进展,总结了关键发现。几十年来,技术的进步将研究从早期的候选基因研究提升到了当今的全基因组扫描,揭示了 AUD 的许多遗传和表观遗传风险因素。对超过 100 万名参与者的最新全基因组关联研究(GWAS)确定了 100 多个遗传变异,对血液和脑组织样本的最大表观基因组全基因组关联研究(EWAS)揭示了组织特异性的表观遗传变化。下游分析揭示了富集的途径、与其他特征的遗传相关性、脑组织中的转录组全基因组关联以及 AUD 的药物-基因相互作用。我们还讨论了局限性和未来的方向,包括提高 GWAS 和 EWAS 研究的效能,以及扩大这些分析中所包含的人群多样性。更大的样本、新型技术和分析方法至关重要;这些方法包括全基因组测序、多组学、单细胞测序、空间转录组学、变异功能的深度学习预测以及疾病风险预测的综合方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5703/11324314/8db34970211a/jci-134-172885-g168.jpg

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