NHGRI EpiGenVar Coordinating Center

描述（由申请人提供）：NHGRI正在开发一项研究计划，以识别和表征与全基因组关联（GWA）和其他遗传研究中复杂人类疾病因果相关的遗传变异。为了支持这样一项雄心勃勃的工作的复杂性，罗格斯大学遗传学系召集了一个由统计遗传学家、分子遗传学家、遗传流行病学家、计算机和信息科学家、生物统计学家和具有多年相关经验的项目管理人员组成的强大团队，作为协调中心（CC）。具体而言，CC将作为一个集中的遗传流行病学资源，以促进和支持该计划和研究调查员的活动，重点关注因果变异的复制和表征：(1)利用创新的计算机和信息科学方法，以可理解的形式检索和综合从关联、表型、协变量/暴露和群体祖先数据分析中获得的研究结果和描述性数据，包括特定干预对给定基因型-表型关联的影响以及与给定变异相关的特定性状的风险；(2)作为数据交流中心，以用户友好和易于解释的格式传播关于假定的与疾病有关的遗传变异的流行病学结构的结果和描述性数据，包括参与人口研究的详细和标准化特征，以便科学界最大限度地利用数据、人口影响和潜在的基因功能；(3)利用最先进的计算机和信息科学支持开发网络平台，以实现、激励和促进与外部研究人员的合作，开展未来的功能和转化研究；(4)按照指导委员会或项目办公室的要求，为项目的成功协调发挥中心作用，促进、支持和管理所需的项目活动和后勤。该计划的协调将本着合作精神，采用创造性和灵活的方法，同时在gwa的统计遗传方法和项目管理方法方面发挥领导作用。我们CC的最终目标将是促进基因型-表型关联的识别和表征，从而加速我们对常见疾病的遗传和环境原因的理解。

DESCRIPTION (provided by applicant): NHGRI is developing a research program to identify and characterize genetic variants causally associated with complex human diseases in genome-wide association (GWA) and other genetic studies. To support the complexities of such an ambitious effort, the Department of Genetics at Rutgers University has convened a strong team of statistical geneticists, molecular genetics, genetic epidemiologists, computer and information scientists, biostatisticians and project management staff with many years of related experience to serve as a Coordinating Center (CC). Specifically, the CC will serve as a centralized genetic epidemiologic resource to facilitate and support the activities of the program and Study Investigators focused on replication and characterization of causal variants by: (1) utilizing innovative computer and information science methodologies to retrieve and synthesize in a comprehensible form study results and descriptive data obtained from the analysis of association, phenotypic, covariate/exposure and population ancestry data, including the impact of particular interventions on a given genotype-phenotype association and the risk of a specific trait associated with a given variant; (2) serving as a data clearinghouse for disseminating results and descriptive data on the epidemiologic architecture of putative disease-associated genetic variants, including detailed and standardized characterizations of the participating population studies, in user-friendly and readily interpretable formats that will maximize the utilization of data population impact and potential gene function by the scientific community; (3) utilizing state-of-the-art computer and information science support to develop cyber platforms that will enable, stimulate and facilitate collaborations with outside investigators for future functional and translational research; and (4) serving as a centralized to facilitate, support and manage as needed program activities and logistics as requested by the Steering Committee or Project Office as needed for successful coordination of the program. Coordination of the program will be done in a spirit of collaboration using creative and flexible approaches, while providing leadership in statistical genetic methodologies for GWAs and approaches to project management. The ultimate goal of our CC will be to facilitate the identification and characterization of genotype-phenotype associations, thereby accelerating our understanding of the genetic and environmental causes of common diseases.