Center for Systems Neurogenetics of Addiction

项目概要 成瘾系统神经遗传学中心（CSNA）利用行为遗传学的方法和专业知识， 神经科学、计算科学、基因组生物学、小鼠和人类遗传学以及遗传工程， 可卡因生物行为风险的共同和独特生物机制的确定、背景化和建模 自我管理。药物成瘾是一种具有破坏性和复杂性的疾病，受多种病因的影响， 因素大量证据表明遗传变异对一系列成瘾行为的作用，从 实验和开始吸毒到强迫性吸毒行为。然而，发现诱发 人类群体中的基因和变异受到高样本量要求、表型分析能力和 药物暴露和其他环境因素的变化。先进的小鼠遗传种群显示 成瘾相关行为的变化，并提供一个实验平台，以发现神经生物学 易感特质预测自我施用可卡因倾向的机制。CSNA雇用 协作杂交遗传参考群体和多样性远交作图群体 综合研究项目集中在成瘾易感性的三个相互关联的方面：冲动，可卡因 致敏和自我给药。这些性状使用多维表型分析平台进行评估 在表现出极端遗传和表型变异的小鼠群体中， 评估成瘾脆弱性的共同和独特的生物学机制。这些互补 群体来自相同的创始人菌株，允许跨研究进行广泛的数据整合 无论是在CSNA内部还是外部。CSNA开发了数据集成方法，并产生了多个功能 基因组学和表型组学数据集，存放在广泛访问和高度功能的信息资源， 全球研究界。该中心还将其成果扩展到基础神经生物学和临床前研究领域。 通过将研究结果与人类遗传和基因组研究相结合， 小鼠突变体，并确定脆弱和耐药菌株的机制研究。三研究支持 核心为CSNA研究项目和更大的研究社区提供了最先进的方法， 包括一个复杂的，大容量的行为表型核心，一个综合遗传学和基因组学 统计遗传学、分子特征分析、生物库、数据整合和数据传播的核心，以及 小鼠资源和验证核心，用于创建和提供用于系统遗传学的新型小鼠资源， 验证和疾病建模。行政核心协调、整合和传播研究， 教育和外联活动。最后，一个试点核心可以促进协作工作，以促进创新， 成瘾科学的多样性。通过其共同努力，CSNA将对研究产生持久的影响， 成瘾遗传学通过对生物行为风险的整体检查，社区数据的生成， 我们和其他人将在未来的研究中，通过教育和培训，扩大和利用这些资源。

PROJECT SUMMARY OVERALL The Center for Systems Neurogenetics of Addiction (CSNA) leverages approaches and expertise in behavioral neuroscience, computational science, genome biology, mouse and human genetics, and genetic engineering to identify, contextualize and model shared and distinct biological mechanisms of biobehavioral risk for cocaine self-administration. Drug addiction is a devastating and complex disorder influenced by multiple etiological factors. Extensive evidence demonstrates the role of genetic variation on a range of addiction behaviors, from experimentation and initiation of drug use to compulsive drug-taking behavior. Yet discovery of predisposing genes and variants in human populations is limited by high sample size requirements, phenotyping capacity, and variability in drug exposure and other environmental factors. Advanced mouse genetic populations exhibit variation in addiction-relevant behaviors and offer an experimental platform to discover the neurobiological mechanisms by which predisposing traits predict the tendency to self-administer cocaine. The CSNA employs the Collaborative Cross genetic reference population and Diversity Outbred mapping population across three integrated research projects focused on three interrelated aspects of addiction susceptibility: impulsivity, cocaine sensitization and self-administration. These traits are evaluated using a multidimensional phenotyping platform in a mouse population exhibiting extreme genetic and phenotypic variation, enabling a replicable and extensible assessment of the shared and distinct biological mechanisms of addiction vulnerability. These complementary populations are derived from the same founder strains, allowing for extensive data integration across studies within and outside the CSNA. The CSNA develops data integration methods and produces multiple functional genomics and phenomics datasets, deposited in widely accessed and highly functional informatics resources for the global research community. The Center also extends its results into basic neurobiological and preclinical therapeutic research by integrating findings with human genetic and genomic studies, generating novel, validated mouse mutants and identifying vulnerable and resistant strains for mechanistic studies. Three research support cores provide state-of-the-art approaches to the CSNA research projects and the larger research community, including a sophisticated, large-capacity Behavioral Phenotyping Core, an Integrative Genetics and Genomics Core for statistical genetics, molecular profiling, biobanking, data integration and data dissemination, and a Mouse Resource and Validation Core for creating and delivering novel mouse resources for systems genetics, validation, and disease modeling. The Administrative Core coordinates, integrates, and disseminates research, education and outreach activities. Finally, a Pilot Core enables collaborative work to promote innovation and diversity in addiction science. Through its combined efforts, the CSNA will have a lasting impact on the study of addiction genetics through the holistic examination of biobehavioral risk, the generation of community data resources that we and others will expand and exploit in future studies, and through education and training.