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: 8523951
• 负责人: Fernando Pardo-Manuel de Villena
• 金额: $ 80.21万
• 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 2014-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8523951
• 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，我们提出了一个非常雄心勃勃但又切实可行的目标：将UNC-CH现有的专业知识整合为卓越中心，以作为一种资源进行开发，并展示小鼠协作杂交（CC）的效用，以描述精神病学中复杂表型的遗传和环境决定因素，这是所有生物医学中最棘手的问题。 我们提出了一个特别具有挑战性的成功定义-我们将确定高概率病因模型（实际上可能很复杂），然后通过产生新的小鼠品系来证明这些模型的预测能力，这些小鼠品系的表型风险非常低或非常高。 一旦得到验证，这些高置信度模型就可以在随后的人体研究中进行测试。 该中心收集的数据将成为更广泛的科学界的宝贵资源，并可用于调查任何数量的生物学问题。 该项目的一个关键组成部分是开发先进的用户交互式数据库，以访问所收集的大型复杂数据集。 要实现这一总体目标，就需要具备多种多样的科学专长--精神病学、人类遗传学、小鼠表型分析、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工作，并致力于这里描述的项目。 此外，这些不同领域的成功整合是不平凡的;然而，我们可以证明，在过去五年中，该应用程序的所有科学家都有广泛的互动历史，现在知道如何一起工作，并了解同事的专业知识。 北卡罗来纳大学教堂山分校一直致力于促进跨学科的基因组学研究，是美国最具学院特色的生物医学研究机构之一，为这里提出的“科学2.0”项目提供了肥沃的背景。 公共卫生相关性精神疾病是一个悖论-相关的发病率、死亡率和社会成本是巨大的，然而，尽管经过了世纪的科学研究，关于核心疾病的病因学几乎没有确凿的事实。 虽然GWAS的荟萃分析正在进行中，但早期的结果表明，强有力的和可复制的发现是难以捉摸的。 我们的建议提供了一个替代模型的方法，以补充基本的精神病表型的研究。

项目成果

A novel statistical approach for jointly analyzing RNA-Seq data from F1 reciprocal crosses and inbred lines.

一种新的统计方法，用于联合分析 F1 互交和自交系的 RNA-Seq 数据。

• DOI: 10.1534/genetics.113.160119
• 发表时间: 2014
• 期刊: Genetics
• 影响因子: 3.3
• 作者: Zou,Fei;Sun,Wei;Crowley,JamesJ;Zhabotynsky,Vasyl;Sullivan,PatrickF;Pardo-ManueldeVillena,Fernando
• 通讯作者: Pardo-ManueldeVillena,Fernando