Gene to Phenotype Networks for Alcohol & Drug Addiction

酒精的基因到表型网络

• 批准号: 7149871
• 负责人: Daniel Goldowitz
• 金额: $ 74.49万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 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 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7149871
• 关键词: alcohols; drug abuse; drug addiction; genes; phenotype

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(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，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是共病的，这是同样的基因影响可能影响多种表型的证据，这种现象被称为基因多效性。这个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、行为变异和对滥用的易感性的基因和基因网络。这一应用利用了RL菌株的独特图谱特性、一套新的、高功率扩展的RL品系、先进的生物信息学工具、WebQTL中存在的大量数据库以及TMGC高通量表型资源的专业知识，以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定位和微阵列转录图谱的强大组合将被应用于这些系统水平的表型。作为这一应用的一部分，我们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，最突出的是在复杂特征分析和基因图谱、行为和神经分析、精神药理学和药物遗传学、转录组图谱和分子遗传学、药物滥用、酒精中毒、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方面。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极大地减少发现任何新的基因特定突变的影响所需进行的表型分析的数量。候选基因将使用ORNL的新库ENU资源以及公开可用的小鼠突变资源进行验证。这将对开发现实的复杂疾病模型非常有价值，并将提供数据资源，为大规模单基因突变研究提供具有成本效益的有针对性的表型策略，如全面基因敲除小鼠项目联盟提出的策略。通过检查BXD RL系中基因表达指标和已知表型指标的协方差，我们可以合理地针对可能受到特定基因突变影响的表型。更广泛地说，我们将能够确定基因多态对药物滥用、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特定遗传基础。