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

小鼠和人脑中成瘾相关基因的共表达网络

• 批准号: 8137249
• 负责人: Michael Hawrylycz
• 金额: $ 36.94万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8137249
• 关键词: Adult; Anatomy; Area; Atlases; Automobile Driving; Biochemical; Biological Neural Networks; Brain; Brain region; Communities; Complex; Computer Analysis; Computer software; Computing Methodologies; Consultations; Custom; Data; Data Set; Data Sources; Databases; Dependency; Development; Disease; Drug Addiction; Drug abuse; Etiology; Exhibits; Female; Functional disorder; Gender; Gene Cluster; Gene Expression; Generations; Genes; Genetic; Genome; Genomics; Grouping; Hand; Heritability; Human; Image; Imagery; In Situ Hybridization; Individual; Information Resources; Institutes; Internet; Laboratories; Libraries; Light; Link; Literature; Maps; Measurement; Metadata; Methods; Mining; Molecular Profiling; Multivariate Analysis; Mus; Nature; Nervous system structure; Network-based; Online Systems; Opioid; Orthologous Gene; Pain; Pathway interactions; Pattern; Phase; Phenotype; Principal Component Analysis; Property; Research; Research Personnel; Research Proposals; Resolution; Resources; Sampling Studies; Schedule; Series; Software Tools; Source; Spinal Cord; Staging; Structure; Substance Addiction; Substance abuse problem; Surveys; System; Testing; Text; Time; Weight; Work; addiction; base; computerized tools; critical period; genome wide association study; genome-wide; interest; knowledge base; male; public health relevance; software development; text searching; tool; user-friendly

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.

描述（由申请人提供）：基因组广泛的数据集的可用性增加有望将遗传复杂性疾病的病因和病理生物学介绍为包括药物滥用和依赖性。然而，仍然存在重大挑战：尽管有证据表明具有重大遗传力的证据，但基因组广泛的关联研究通常揭示了较小的影响大小，这可能是由于疾病的多基因性质。来自艾伦学院（Allen Institute）的脑部基因表达数据集提供了新的数据源，这些数据源可根据解剖空间中其表达谱的相似性将基因组合在一起，从而增强了全基因组研究中统计检验的能力。由于这些数据集的空间空间分辨率，在全基因组和脑范围内的覆盖范围内，还可以检查涉及细胞间生化网络的特定假设以及范围范围的神经网络。在艾伦学院（Allen Institute）和冷泉港实验室，我们一直在协作分析艾伦脑图集（ABA）成人小鼠脑数据集，初步结果表明，基因的空间共表达模式确实是丰富的信息来源。在此应用程序中，我们打算将这种分析重点放在与成瘾相关的基因集上，并与成瘾研究专家协商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）基于软件和基于Web的工具的开发和改进，用于分析基因集中的共表达模式，以及（2）对初始成瘾相关基因集的多变量分析。第一年将重点放在已经准备好的成年小鼠脑数据集上。在随后的几年（2 - 4年，R33阶段）中，我们将将共表达分析扩展到小鼠发育和脊髓数据集（AIM 1）以及计划在此期间可用的人脑数据集（AIM 2）（AIM 2）。此外，我们将挖掘现有的数据库和文献，以增强我们的初始基因列表，并在药物滥用表型和相应的大脑区域之间建立关联数据库（AIM 3）。这将使我们能够更充分地分析可能参与成瘾的内部和细胞间网络。最后，我们将以Web门户的形式公开获得研究的一部分计算工具和分析结果（AIM 4）。 公共卫生相关性：鉴定基因和基因网络驱动药物滥用和成瘾是成瘾遗传学的主要挑战。公开介绍用于理解空间映射基因组数据集（例如ABA）的工具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。拟议的工作将涵盖关键成瘾基因簇的识别以及成瘾研究人员有用的在线方法的生成。