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Gene to Phenotype Networks for Alcohol & Drug Addiction

酒精的基因到表型网络

基本信息

批准号：
7289214
负责人：
Daniel Goldowitz
金额：
$ 27.95万
依托单位：
UNIVERSITY OF TENNESSEE HEALTH SCI CTR
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-30 至 2007-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7289214
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

项目摘要

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。作为许多人类疾病的典型，它们受到多种遗传和环境因素的影响。对成瘾的易感性与其他行为障碍并存，这表明相同的遗传影响可能会影响多种表型，这种现象被称为基因多效性。该项目的主要目的是系统地识别调节对滥用物质的多效性反应、行为变异和滥用易感性的基因和基因网络。该应用程序利用Rl菌株的独特定位特性，一个新的，高功率扩展的Rl系，先进的生物信息学工具，WebQTL中广泛的数据库，以及TMGC高通量表型资源的专业知识，系统地识别最终调节下游多效性药物和酒精表型的上游基因和分子网络。通过利用WebQTL中现有的高通量分子数据资源，QTL定位和微阵列转录分析的强大组合将应用于这些系统级表型。作为这一应用的一部分，我们已经组建了一支强大的研究团队，他们在几个领域具有互补的专业知识，最引人注意的是复杂特征分析和基因定位，行为和神经分析，精神药理学和药物遗传学，转录组分析和分子遗传学，药物滥用，酗酒，小鼠群体管理和分布以及处理大型数据集的先进生物信息学和多元统计方法。这个强大的团队将充分利用能源部橡树岭国家实验室提供的慷慨支持。这个项目产生的数据资源将大大减少表现型的数量，人们需要执行发现任何新的基因特异性突变的影响。候选基因将使用ORNL的新银行ENU资源以及公开可用的小鼠突变资源进行验证。这对于现实复杂疾病模型的发展将是无价的，并将提供数据资源，为大规模单基因突变的努力提供具有成本效益的靶向表型策略，例如由综合敲除小鼠项目联盟提出的那些策略。通过检测BXD Rl系基因表达量和已知表型量的协方差，我们可以合理定位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、成瘾以及大脑和行为的个体差异的多效性和多基因效应的具体遗传基础。