NHGRI EpiGenVar Coordinating Center

描述(由申请人提供)：NHGRI正在开发一项研究计划，以在全基因组关联(GWA)和其他基因研究中识别和表征与复杂人类疾病相关的遗传变异。为了支持这一雄心勃勃的努力的复杂性，罗格斯大学遗传学系召集了一支强大的团队，由统计遗传学家、分子遗传学、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，作为协调中心(CC)。具体地说，CC将作为集中的遗传流行病学资源，通过以下方式促进和支持项目和研究调查人员的活动：(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/暴露和人群血统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的基因-表型关联的影响以及与给定的变体相关的特定性状的风险；(2)作为数据交换中心，传播关于可能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的特征，以用户友好和易于解释的格式，使科学界最大限度地利用数据对种群的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助，开发网络平台，以便能够、促进和促进与外部研究人员的合作，以便将来进行功能和翻译研究；以及(4)根据指导委员会或项目办公室的要求，作为一个中央协调机构，协助、支持和管理必要的计划活动和后勤，以确保计划的成功协调。将本着协作精神，使用创造性和灵活的方法协调该方案，同时在全球气候变化网络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-表型关联的识别和表征，从而促进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.