A systems approach to the genetic study of alcohol dependence

酒精依赖（Alcohol dependence，AD）是一种慢性、复发性疾病， 精神病双胞胎研究已经确定了AD易感性的重要遗传贡献。 数百个基因的变异可能有助于AD的病因学，每种遗传变异仅赋予AD 风险略有增加。虽然许多基因可能有助于复杂疾病的病因， 倾向于进入较少数量的生物学途径。此外，越来越多的证据表明， 复杂疾病的部分风险变异位于疾病相关基因的调控DNA序列中， 组织或细胞类型。利用现有数据的研究可能会增加基因发现的力量， 这些疾病，包括AD。该提案旨在采用基于系统生物学的方法来识别 基因网络和调节变异的基础AD。为此，我们将进行综合分析， AD的全基因组关联研究（GWAS）与脑特异性差异基因共表达网络 （DCN）和转录调控网络（TRN）。我们构建网络的方法将使用大脑 基因表达和调控功能的区域特异性数据。我们的具体目标是：1）识别基因 通过对GWAS与脑特异性DCN的综合分析，确定AD的潜在子网络; 2）识别 通过GWAS与脑特异性TRN的综合分析来评估监管风险变体集;以及3）评估 使用现有的成像遗传学数据鉴定基因子网络和调控变体的功能。我们有 组建了一个杰出的多学科团队，他们在AD遗传学、基因组学、计算 生物学和神经影像学。我们的目标是应用多学科和尖端的分析战略， 为推进AD遗传学领域的发展服务。风险基因的识别和表征， 调节变异将有助于提高我们对AD生物学机制的理解， 使我们更接近于设计有效的预防和治疗疾病的方法。

Alcohol dependence (AD), one of the leading causes of disability worldwide, is a chronic and recurrent psychiatric illness. Twin studies have established a significant genetic contribution to AD susceptibility. Variations in hundreds of genes likely contribute to the etiology of AD, with each genetic variant conferring only a small increase in risk. Although numerous genes may contribute to the etiology of complex diseases, they tend to fall into a smaller number of biological pathways. In addition, accumulating evidence suggests a large portion of the risk variants for complex diseases are located in regulatory DNA sequences in disease-related tissue or cell types. Studies leveraging already existing data may increase the power of gene discovery for these disorders, which include AD. This proposal aims to employ a systems biology-based approach to identify gene networks and regulatory variants underlying AD. To that end, we will perform integrated analysis of genome-wide association studies (GWAS) of AD with brain-specific differential gene co-expression networks (DCNs) and transcriptional regulatory networks (TRNs). Our approach to network construction will use brain region-specific data, on gene expression and regulatory function. Our specific aims are: 1) Identify gene subnetworks underlying AD through integrated analysis of GWAS with brain-specific DCNs; 2) Identify regulatory risk variant sets through integrated analysis of GWAS with brain-specific TRNs; and 3) Evaluate the function of identified gene subnetworks and regulatory variants using existing imaging genetics data. We have assembled an outstanding multidisciplinary team with expertise in AD genetics, genomics, computational biology, and neuroimaging. Our goal is to apply multidisciplinary and cutting-edge analytical strategies in the service of advancing the field of AD genetics. The identification and characterization of risk genes and regulatory variants would help improve our understanding of the biological mechanisms that underlie AD, moving us closer to designing effective prevention and treatment for the disorder.