An Interdisciplinary program for systems genomics of complex behaviors

复杂行为系统基因组学跨学科项目

• 批准号: 8511023
• 负责人: Fernando Pardo-Manuel de Villena
• 金额: $ 5.34万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8511023
• 关键词: Adverse reactions; Alleles; Antipsychotic Agents; Anxiety; Behavior; Biological; Biology; Biomedical Research; Breeding; Chromosome Mapping; Clozapine; Commit; Communities; Complement; Complex; Computational Biology; Copy Number Polymorphism; Data; Data Collection; Data Set; Databases; Development; Discipline; Disease; Etiology; Gene Expression Profile; Genetic; Genome; Genomics; Genotype; Goals; Haloperidol; Health; Human; Human Genetics; Imagery; Institution; Instruction; Joints; Knowledge; Mental Depression; Mental disorders; Meta-Analysis; Modeling; Morbidity - disease rate; Mouse Strains; Mus; Parents; Pharmacogenetics; Phenotype; Preparation; Probability; Production; Psychiatry; Recording of previous events; Regulation; Research; Resources; Science; Scientist; Social Behavior; System; Systems Biology; Testing; Work; X Inactivation; computerized data processing; cost; data management; genome wide association study; high risk; imprint; metabolomics; model development; mortality; novel; phenome; predictive modeling; programs; success; tool

DESCRIPTION (provided by applicant): In this application, we propose a highly ambitious yet realistically attainable goal: to align existing expertise at UNC-CH into a center of excellence in order to develop as a resource and demonstrate the utility of the murine Collaborative Cross (CC) to delineate genetic and environmental determinants of complex phenotypes drawn from psychiatry, the most intractable set of problems in all of biomedicine. We propose a particularly challenging definition of success - we will identify high probability etiological models (which can be realistically complex) and then prove the predictive capacity of these models by generating novel strains of mice bred to be at either very low or very high risk of the phenotype. Once validated, these high confidence models can then be tested in subsequent human studies. The data collected at the UNC center would be a valuable resource to the wider scientific community and could be used to interrogate any number of biological problems. The development of sophisticated, user-interactive databases to access the large, complex datasets collected represents a key component of the project. Accomplishing this overarching goal requires an exceptional diversity of scientific expertise - psychiatry, human genetics, mouse phenotyping, mouse genetics, statistical genetics, computational biology, and systems biology. Experts in all of these disciplines were deeply involved in the preparation of this application and are committed to the projects described here. Moreover, successful integration of these diverse fields is non-trivial; however, we can document that all scientists on this application have a history of extensive interactions over the past five years, know now how to work together and have a working knowledge of their colleagues' expertise. UNC-Chapel Hill has a shown an intense commitment to promoting inter-disciplinary genomics research and is one of the most collegial biomedical research institutions in the US which provides a fertile backdrop for "Science 2.0" projects such as that proposed here. PUBLIC HEALTH RELEVANCE Psychiatric disorders are a paradox-the associated morbidity, mortality, and societal costs are enormous and yet, despite over a century of scientific study, there are few hard facts about the etiology of the core diseases. Although GWAS meta-analyses are in progress, early results suggest that strong and replicable findings are elusive. Our proposal provides an alternative model approach to complement the study of fundamental psychiatric phenotypes.

描述（由申请人提供）：在本申请中，我们提出了一个高度雄心勃勃但现实可实现的目标：将UNC-CH的现有专业知识与卓越中心保持一致，以发展为资源，并证明鼠协作十字架（CC）的实用性，以划定属于精神上的复杂现象的遗传和环境决定性的范围，从而使精神上的遗传范围脱颖而出。 我们提出了对成功的特别挑战性的定义 - 我们将确定高概率病因模型（这可能是现实的复杂），然后通过产生新型的小鼠菌株的菌株的表征风险非常低或很高的菌株来证明这些模型的预测能力。 一旦得到验证，这些高置信度模型就可以在随后的人类研究中进行测试。 在UNC中心收集的数据将是更广泛的科学界的宝贵资源，可以用来询问任何数量的生物学问题。 访问收集的大型复杂数据集的复杂，用户相互交互的数据库的开发代表了项目的关键组成部分。 实现这一总体目标需要具有非凡的科学专业知识多样性 - 精神病学，人类遗传学，小鼠表型，小鼠遗传学，统计遗传学，计算生物学和系统生物学。 所有这些学科的专家都深入参与了本申请的准备，并致力于此处描述的项目。 此外，这些不同领域的成功整合是非平凡的。但是，我们可以记录到，该应用程序上的所有科学家在过去五年中都有广泛互动的历史，现在知道如何共同努力并了解同事的专业知识。 UNC-Chapel Hill对促进跨学科基因组学研究表现出了强烈的承诺，并且是美国最合作的生物医学研究机构之一，为“ Science 2.0”项目提供了肥沃的背景。 公共卫生相关性的精神疾病是一种悖论 - 相关的发病率，死亡率和社会成本是巨大的，但是，尽管有一个多世纪的科学研究，但关于核心疾病的病因的艰难事实很少。 尽管GWAS荟萃分析正在进行中，但早期的结果表明，强大而可复制的发现是难以捉摸的。 我们的建议提供了一种补充基本精神病表型研究的替代模型方法。