PDF(Pubmed)
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重要因素。其复杂的病因涉及遗传学，表观遗传学,和环境因素。我们回顾了理解AUD的遗传学和表观遗传学的进展，总结关键发现。几十年来，技术的进步已经将研究从早期的候选基因研究提升到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。最新的GWAS对100多万参与者进行了鉴定，发现了100多种遗传变异，血液和大脑样本中最大的全表观基因组关联研究（EWAS）揭示了组织特异性表观遗传变化。下游分析揭示了丰富的途径，与其他性状的遗传相关性，脑组织中的全转录组关联，和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，包括增加GWAS和EWAS研究的能力，以及扩大这些分析中包括的种群多样性。更大的样品，新技术,分析方法是必不可少的；这些方法包括全基因组测序，Multiomics,单细胞测序，空间转录组学，变量函数的深度学习预测，和疾病风险预测的综合方法。
