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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。成瘾易感性与其他行为障碍共病，这表明相同的遗传影响可能会影响多种表型，这种现象称为基因多效性。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。本申请利用Rl菌株的独特作图性质、一组新的高倍扩展的Rl系、先进的生物信息学工具、WebQTL中存在的广泛数据库以及TMGC高通量表型资源的专业知识来系统地鉴定最终调节下游多效性药物和醇表型的上游基因和分子网络。QTL定位和微阵列转录谱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应用于这些系统水平的表型。作为该应用程序的一部分，我们已经组建了一支强大的调查团队，他们在几个领域具有互补的专业知识，最值得注意的是复杂的性状分析和基因图谱，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先进的生物信息学和处理大型数据集的多元统计方法。这个强大的团队将利用能源部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发现任何新基因特异性突变的影响所需的表型分析量。候选基因将使用ORNL的新库存ENU资源以及公开可用的小鼠突变体资源进行验证。这对于开发现实的复杂疾病模型将是非常宝贵的，并将提供数据资源，以建议大规模单基因突变工作的成本效益有针对性的表型分析策略，如综合敲除小鼠项目联盟提出的那些。通过检查BXD Rl系中基因表达量度和已知表型量度的协方差，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、成瘾以及大脑和行为的个体差异的多效和多基因效应的特定遗传基础。