Co-expression Networks of Addiction-Related Genes in the Mouse and Human Brain

描述（由申请人提供）：全基因组数据集的日益可用性有望揭示遗传复杂疾病的病因学和病理生理学，包括药物滥用和依赖。然而，仍然存在重大挑战：尽管有证据表明具有显著的遗传性，但全基因组关联研究通常显示效应量较小，这可能是由于疾病的多基因性质。来自艾伦研究所的全脑基因表达数据集提供了新的数据来源，可用于根据解剖空间中基因表达谱的相似性将基因分组，从而增强全基因组研究中统计检验的能力。由于这些数据集具有前所未有的空间分辨率，具有全基因组和全脑覆盖范围，还可以检查涉及细胞间生化网络以及全脑神经网络的特定假设。在艾伦研究所和冷泉港实验室，我们一直在合作分析艾伦脑图谱（阿坝）成年小鼠大脑数据集，初步结果表明，基因的空间共表达模式确实是一个丰富的信息来源。在这个应用程序中，我们打算将这个分析集中在成瘾相关的基因集上，咨询成瘾研究专家并整合相关的在线信息资源。第一年（R21阶段）的具体目标包括（1）开发和完善用于分析基因组中共表达模式的软件和基于网络的工具，以及（2）对初始成瘾相关基因组进行多变量分析。第一年将专注于已经掌握的成年小鼠大脑数据集。在随后的几年（第2-4年，R33阶段），我们将把共表达分析扩展到小鼠发育和脊髓数据集（Aim 1）和人脑数据集（Aim 2），这些数据集计划在此期间可用。此外，我们将挖掘现有的数据库和文献，以增加我们的初始基因列表，以及开发药物滥用表型和相应的大脑区域之间的关联数据库（目标3）。这将使我们能够更全面地分析可能参与成瘾的细胞内和细胞间网络。最后，我们将以门户网站的形式公开研究中开发的计算工具和分析结果（目标4）。 公共卫生相关性：识别驱动药物滥用和成瘾的基因和基因网络是成瘾遗传学目前面临的主要挑战。公开介绍用于理解空间映射基因组数据集的工具，如阿坝，将对旨在了解这些遗传网络和途径的研究人员产生重大影响。拟议的工作将包括识别关键的成瘾基因簇，以及为成瘾研究人员提供有用的在线方法。

DESCRIPTION (provided by applicant): The increasing availability of genome wide data sets promise to shed light into the etiology and pathophysiology of genetically complex disorders, including substance abuse and dependence. There remain significant challenges, however: although there is evidence for significant heritability, genome wide association studies have typically revealed small effect sizes, possibly due to the polygenic nature of the disorders. The brain-wide gene expression data sets from the Allen Institute offers new data sources that could be used to group genes together based on similarities in their expression profiles in anatomic space, thus enhancing the power of statistical tests in genome-wide studies. Due to the unprecedented spatial resolution in these data sets, with genome-wide and brain-wide coverage, specific hypotheses involving intercellular biochemical networks as well as brain-wide neural networks can also be examined. At the Allen Institute and at Cold Spring Harbor Laboratory, we have been collaboratively analyzing the Allen Brain Atlas (ABA) adult mouse brain data set, and preliminary results demonstrate that the spatial co-expression patterns of genes are indeed a rich source of information. In this application, we intend to focus this analysis on addiction-related gene sets, in consultation with experts on addiction research and integrating relevant online information resources. Specific aims in the first year (R21 phase) include (1) development and refinement of software and web-based tools for analysis of co-expression patterns in gene sets and (2) multivariate analysis of an initial set of addiction related genes. The first year will focus on the adult mouse brain data set that is already at hand. In subsequent years (years 2-4, R33 phase), we will extend the co-expression analysis to mouse developmental and spinal cord data sets (Aim 1), and human brain data sets (Aim 2), that are scheduled to become available during this period. Additionally, we will mine existing databases and the literature to augment our initial gene lists as well as to develop a database of associations between substance abuse phenotypes and corresponding brain areas (Aim 3). This will allow us to more fully analyze the intra and intercellular networks that may be involved in addiction. Finally, we will make the computational tools and analysis results developed as part of our research publicly available in the form of a web portal (aim 4). PUBLIC HEALTH RELEVANCE: The identification of genes and gene networks driving drug abuse and addiction is a major current challenge in addiction genetics. The public presentation of tools for understanding spatially mapped genomic datasets such as the ABA will have major impact on researchers aiming to understand these genetic networks and pathways. The proposed work will encompass both the identification of key addiction gene clusters as well as the generation of useful online methods for addiction researchers.