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: 8325662
• 负责人: Michael Hawrylycz
• 金额: $ 36.11万
• 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/8325662
• 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; Gene Expression Profile; 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; abstracting; addiction; base; computerized tools; critical period; genome wide association study; genome-wide; interest; knowledge base; male; software development; text searching; tool; user-friendly

Project Summary/Abstract 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 proposal, 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).

项目摘要/摘要 全基因组数据集的日益可获得性有望揭示病因学和 遗传性复杂疾病的病理生理学，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， 遗传性，全基因组关联研究通常揭示了小的效应大小，可能 由于疾病的多基因性质。全脑基因表达数据集来自 艾伦研究所提供了新的数据源，可用于根据以下条件将基因分组 它们在解剖空间表达谱的相似性，从而增强了统计学的力量 全基因组研究中的测试。由于这些数据集具有前所未有的空间分辨率， 全基因组和全脑覆盖，涉及细胞间生化的特定假设 网络以及全脑神经网络也可以被研究。 在艾伦研究所和冷泉港实验室，我们一直在合作 分析Allen Brain Atlas(ABA)成年小鼠的大脑数据集和初步结果 证明基因的空间共表达模式确实是一个丰富的来源 信息。在这项建议中，我们打算将这一分析集中在与成瘾相关的基因集上，在 与成瘾研究专家进行咨询，整合相关在线信息 资源。第一年(R21阶段)的具体目标包括(1)开发和完善 用于分析基因集中共表达模式的软件和基于网络的工具和(2) 成瘾相关基因初始集合的多变量分析。第一年将重点放在 已经在手边的成年小鼠大脑数据集。在接下来的几年(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(目标1)， 和人脑数据集(目标2)，计划在此期间提供。 此外，我们还将挖掘现有的数据库和文献以充实我们最初的基因列表 以及开发药物滥用表型和药物滥用表型之间关联的数据库 相应的脑区(目标3)。这将使我们能够更全面地分析内部和 可能与成瘾有关的细胞间网络。最后，我们将使 作为我们研究的一部分开发的计算工具和分析结果可在 门户网站的形式(目标4)。

项目成果

Canonical genetic signatures of the adult human brain.

• DOI: 10.1038/nn.4171
• 发表时间: 2015-12
• 期刊: Nature neuroscience
• 影响因子: 25
• 作者: Hawrylycz M;Miller JA;Menon V;Feng D;Dolbeare T;Guillozet-Bongaarts AL;Jegga AG;Aronow BJ;Lee CK;Bernard A;Glasser MF;Dierker DL;Menche J;Szafer A;Collman F;Grange P;Berman KA;Mihalas S;Yao Z;Stewart L;Barabási AL;Schulkin J;Phillips J;Ng L;Dang C;Haynor DR;Jones A;Van Essen DC;Koch C;Lein E
• 通讯作者: Lein E