The Heritable Transcriptome and Alcoholism

本应用程序的目标是建立一个适合于系统的动物模型和伴随数据库 复杂性状的遗传分析，特别是代表饮酒遗传易感因素的性状 精神障碍(澳元)。系统遗传学方法需要对基因表达、 生物体多个组织中的蛋白质和代谢物水平，以及对基因-基因和 基因-环境相互作用，以及这些因素在促成复杂性方面的相互依赖 特征/障碍。因此，动物模型的一个关键要求是有一个基因稳定的种群，可以 经过多代人反复研究，以提供累积数据，最终可以允许 完整的系统遗传分析。在过去的授权期内，我们在 开发符合这些标准的杂交大鼠多样性小组(HRDP)。我们选择将重点放在 老鼠，而不是小鼠，用于研究与AUD相关的复杂特征，因为 大鼠的大脑，在操作任务中训练的容易，以及大鼠更高的认知能力。我们已经产生了DNA 四个组织(脑、肝[整体])的序列数据、RNA序列数据和全基因组外显子阵列数据 器官和细胞特异性数据]、心脏和棕色脂肪组织)来自HXB/BXH重组大鼠株 近交系(RI)小组和经典近交系大鼠品系。我们已经定位了行为/生理性状的QTL (酒精消费、酒精剥夺效应、酒精代谢，包括酒精后的醋酸盐水平 管理)，以及使用转录组数据来定位表达QTL，以生成转录本 共表达模块和图谱模块特征基因(第一主成分)的QTL。这些数据已经被 用于识别候选基因和转录网络，这些基因和转录网络对所测量的生物化学和 行为。我们所有的原始数据、处理数据和分析数据都已提供给研究界 在我们的PhenoGen网站(http://phenogen.ucdenver.edu).我们开发的这个网站还包括 在系统遗传学框架中探索这些数据的几个可视化工具，并允许用户 观察感兴趣的复杂表型和基因产物网络之间的遗传关系 影响表型。我们现在提议完成96份转录数据的主要核心- 增加了来自另一个大鼠RI小组(FXLE/LEXF)和更多近交系大鼠品系的数据。我们将获得 从所有品系的雄性和雌性大鼠的脑和肝脏的全转录组信息，量化表达 转录异构体，包括3‘非编码区异构体，并分析3’非编码区，以供替代使用 多聚腺苷酸化位点和miRNA结合位点。我们将利用我们已建立的和新开发的管道来 传播综合的系统级数据(PhenoGen和大鼠基因组数据库)。我们还将扩大我们的 将收集的信息应用于识别与基因相关的遗传因素的演示 通过在HXB/BXH RI小组中获得关于“抑郁”的易感性的信息来发展AUD，以及 我们将继续将动物数据与人类GWAS数据整合在一起。

The goal of this application is to establish an animal model and accompanying database suitable for a systems genetic analysis of complex traits, specifically traits that represent genetic predisposing factors for alcohol use disorder (AUD). Systems genetic approaches require a global analysis of factors such as gene expression, protein and metabolite levels in multiple tissues of an organism, as well as an understanding of gene-gene and gene-environment interactions, and the interdependency of these factors in contributing to complex traits/disorders. As a result, a key requirement for an animal model is a genetically stable population that can be studied repeatedly, over many generations, to provide cumulative data that can eventually allow for a complete systems genetic analysis. During the past grant periods, we have progressed well with the development of a Hybrid Rat Diversity Panel (HRDP) that meets these criteria. We have chosen to focus on the rat, rather than the mouse, for studies of complex traits related to AUD, because of the greater size of the rat brain, the ease of training in operant tasks, and the rat’s higher cognitive ability. We have generated DNA sequence data, RNA sequence data and whole genome exon array data on four tissues (brain, liver [whole organ and cell-specific data], heart and brown adipose tissue) from rat strains of the HXB/BXH recombinant inbred (RI) panel and from classic inbred rat strains. We have mapped QTLs for behavioral/physiological traits (alcohol consumption, alcohol deprivation effect, alcohol metabolism including acetate levels after alcohol administration), as well as used transcriptome data to map expression QTLs, to generate transcript coexpression modules and map module eigengene (first principal component) QTLs. These data have been used to identify candidate genes and transcriptional networks that contribute to the measured biochemistry and behaviors. All of our raw, processed and analyzed data have been made available to the research community on our PhenoGen website (http://phenogen.ucdenver.edu). This website, that we developed, also includes several visualization tools to explore these data in a systems genetics framework and allows the user to observe genetic relationships between a complex phenotype of interest and networks of gene products that influence the phenotype. We are now proposing to complete the main core of transcriptional data for the 96- strain HRDP, adding data from another rat RI panel (FXLE/LEXF) and more inbred rat strains. We will obtain full transcriptome information of brain and liver of male and female rats from all strains, quantify the expression of transcript isoforms, including 3’UTR isoforms, and analyze the 3’UTR regions for alternative use of polyadenylation sites and miRNA binding sites. We will use our established and newly developed pipelines to disseminate integrated, systems level data (PhenoGen and Rat Genome Database). We will also expand our demonstration for applying the gathered information to the identification of genetic factors that are linked to the development of AUD by obtaining information on predisposition to “depression” in the HXB/BXH RI panel, and we will continue to integrate the animal data with human GWAS data.