Tara Matise, Ph.D. (PI), Department of Genetics; Steve Buyske, Ph.D. (co-PI), Department of Statistics, Rutgers University

Jose Luis Ambite, Ph.D. (co-I); Ewa Deelman, Ph.D. (co-I), Information Sciences Institute, University of Southern California.

Carlos Bustamante, Ph.D. (PI); Eimear Kenny, Ph.D. (co-I), Stanford University Center for Computational, Human and Evolutionary Genomics

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 geneticists, 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 cyberplatforms 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.