Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

IF 6.8 1区 医学 Q1 SUBSTANCE ABUSE
Alcohol Research : Current Reviews Pub Date : 2012-01-01
Aaron R Wolen, Michael F Miles
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引用次数: 0

Abstract

For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

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识别乙醇和酒精中毒的神经生物学基础基因网络。
对于像酗酒这样的复杂疾病,确定与这些疾病相关的基因及其具体作用是困难的。传统的遗传方法,如遗传关联研究(包括全基因组关联研究)和对人类和实验动物的数量性状位点(qtl)的分析,已经帮助确定了一些候选基因。然而,由于技术障碍,例如任何单个基因的影响都很小,这些方法在确定导致复杂疾病的特定基因方面的效力有限。新兴的系统生物学领域允许对整个基因网络进行分析,这可能有助于研究人员更好地阐明人类和动物模型中酒精中毒的遗传基础。这种网络可以使用诸如高通量分子谱分析(例如,通过基于微阵列的基因表达分析)或称为遗传基因组学的策略来识别,例如表达qtl的定位(eQTLs)。基因网络的表征可以揭示复杂性状背后的生物学途径,并为识别那些导致疾病发展的基因提供功能背景。
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来源期刊
自引率
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期刊介绍: Alcohol Research: Current Reviews (ARCR) is an open-access, peer-reviewed journal published by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) at the National Institutes of Health. Starting from 2020, ARCR follows a continuous, rolling publication model, releasing one virtual issue per yearly volume. The journal offers free online access to its articles without subscription or pay-per-view fees. Readers can explore the content of the current volume, and past volumes are accessible in the journal's archive. ARCR's content, including previous titles, is indexed in PubMed, PsycINFO, Scopus, and Web of Science.
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