Gene to Phenotype Networks for Alcohol & Drug Addiction

酒精的基因到表型网络

• 批准号: 7462342
• 负责人: Daniel Goldowitz
• 金额: $ 71.06万
• 依托单位: UNIVERSITY OF BRITISH COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7462342
• 关键词: Affect; Alcohol Phenotype; Alcoholism; Alcohols; Appendix; Area; Behavior; Behavior Disorders; Behavioral; Bioinformatics; Brain; Candidate Disease Gene; Chromosome Mapping; Communities; Complex; Data; Data Set; Databases; Department of Energy; Development; Disease model; Drug Addiction; Drug abuse; Environmental Risk Factor; Ethylnitrosourea; Gene Expression; Gene Expression Profile; Gene Mutation; Genes; Genetic; Genetic Polymorphism; Individual Differences; Informatics; Knockout Mice; Maps; Measures; Molecular; Molecular Genetics; Morbidity - disease rate; Mus; Mutant Strains Mice; Mutation; Nervous System Physiology; Neuraxis; Pharmaceutical Preparations; Pharmacogenetics; Phenotype; Population; Predisposition; Property; Psychopharmacology; Purpose; Quantitative Trait Loci; Recombinants; Research Methodology; Research Personnel; Resources; Statistical Methods; System; Systems Integration; Tennessee; Testing; Transcript; Variant; Work; addiction; alcohol behavior; alcohol research; base; brain behavior; cooking; cost; cost effective; drug of abuse; gene discovery; human disease; interest; mouse genome; mouse model; neurobehavioral; neurogenetics; novel; pleiotropism; relating to nervous system; repository; response; tool; trait

DESCRIPTION (provided by applicant): Drug abuse and addiction are complex phenotypes. Typical of many human diseases, they are influenced by multiple genetic and environmental factors. Susceptibility to addiction is co-morbid with other behavioral disorders, which is evidence that the same genetic influences may be acting to affect multiple phenotypes, a phenomenon known as gene pleiotropy. The main purpose of this project is to systematically identify genes and gene networks that modulate pleiotropic responses to abused substances, behavioral variation, and susceptibility to abuse. This application exploits the unique mapping properties of Rl strains, a new, high power expanded set of Rl lines, advanced bioinformatics tools, extensive databases present in WebQTL, and the expertise of the TMGC high-throughput phenotyping resource to systematically identify upstream genes and molecular networks that ultimately modulate downstream pleiotropic drug and alcohol phenotypes. The powerful combination of QTL mapping and microarray transcript profiling will be applied to these systems level phenotypes by exploiting existing high-throughput molecular data resources in WebQTL. As part of this application, we have assembled a strong team of investigators with complementary expertise in several areas, most notably in complex trait analysis and gene mapping, behavioral and neural analysis, psychopharmacology and pharmacogenetics, transcriptome profiling and molecular genetics, drug abuse, alcoholism, mouse colony management and distribution and advanced bioinformatics and multivariate statistical methods of handling large data sets. This strong team will capitalize on the generous support offered by the Department of Energy's Oak Ridge National Lab. The data resources generated by this project will dramatically reduce the amount of phenotyping one needs to perform to discover the effects of any novel gene specific mutation. Candidate genes will be validated using a novel banked ENU resource at ORNL as well as publicly available mouse mutant resources. This will be invaluable for the development of realistic complex disease models and will provide data resources to suggest cost effective targeted phenotyping strategies for large scale single gene mutation efforts such as those proposed by the Comprehensive Knockout Mouse Project Consortium. By examining covariance of gene expression measures and known phenotypic measures in BXD Rl lines, we can rationally target phenotypes that are likely to be affected by particular gene mutations. More broadly, we will be able to identify the specific genetic basis of the pleiotropic and polygenic effects of genetic polymorphisms on drug abuse, addiction, and individual differences in brain and behavior.

描述（由申请人提供）：药物滥用和成瘾是复杂的表型。它们是许多人类疾病的典型特征，它们受到多种遗传和环境因素的影响。对成瘾的敏感性与其他行为障碍合并，这证明相同的遗传影响可能会影响多种表型，这是一种称为基因多效性的现象。该项目的主要目的是系统地识别基因和基因网络，以调节对滥用物质，行为变化和滥用易感性的多效应反应。 This application exploits the unique mapping properties of Rl strains, a new, high power expanded set of Rl lines, advanced bioinformatics tools, extensive databases present in WebQTL, and the expertise of the TMGC high-throughput phenotyping resource to systematically identify upstream genes and molecular networks that ultimately modulate downstream pleiotropic drug and alcohol phenotypes. QTL映射和微阵列成绩单分析的强大组合将通过利用WebQTL中现有的高通量分子数据资源来应用于这些系统级表型。作为该应用程序的一部分，我们组建了一个在多个领域具有互补专业知识的强大研究人员，最著名的是在复杂的特征分析以及基因映射，行为和神经分析，心理药理学和药物遗传学，转录组谱学，转录组谱，分子遗传学，药物滥用，药物滥用，酒精中毒，酗酒，酗酒，小鼠分配以及小鼠的生物学和多个数据设置。这支强大的团队将利用能源部的橡树岭国家实验室提供的慷慨支持。该项目产生的数据资源将大大减少人们所需的表型量，以发现任何新型基因特异性突变的影响。候选基因将使用ORNL的新型ENU资源以及公开可用的鼠标突变资源来验证。这对于开发现实的复杂疾病模型将是无价的，并将提供数据资源，以建议针对大规模单基因突变工作（例如综合敲除小鼠项目联盟提出的）提出成本有效的目标表型策略。通过检查BXD RL线中基因表达度量和已知表型测量的协方差，我们可以合理地靶向可能受特定基因突变影响的表型。更广泛地说，我们将能够确定遗传多态性对药物滥用，成瘾和大脑和行为中个体差异的多基因和多基因作用的特定遗传基础。