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（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，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同时存在，这证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。该应用利用 Rl 菌株的独特作图特性、一组新的、高功率扩展的 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 作图和微阵列转录本分析的强大组合将应用于这些系统级表型。作为该应用的一部分，我们组建了一支强大的研究人员团队，他们在多个领域具有互补的专业知识，尤其是在复杂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。该项目生成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执行的表型分析量。候选基因将使用 ORNL 的新型 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非常有价值，并将提供数据资源，为大规模单基因突变工作提出具有成本效益的靶向表型策略，例如综合敲除小鼠项目联盟提出的策略。通过检查 BXD R1 系中基因表达测量值和已知表型测量值的协方差，我们可以合理地瞄准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。