The completion of the human genome sequence and measurements of genetic variation have enabled the exploration of how genetic variation and gene-environment interactions contribute to health and disease. Associating natural genetic variation with health status requires large numbers of individual human genomes that are well annotated with respect to health status. The goal of this application is to test the hypothesis that biorepositories coupled to electronic medical records (EMRs) can provide these well annotated samples and that they can produce gene associations similar to purpose developed cohorts. NUgene is a biorepository linked to EMR developed phenotypes collected with a broad consent that allows assessment of individual genetic variation, mining of EMRs for phenotypes, and the use of this data for establishing correlations between phenotypes and genotypes. Specifically we propose to: (1) assess the extent, quality and utility of data obtained from medical records to identify cases of asthma and diabetes as well as controls for genome wide association studies (GWAS); (2) evaluate the appropriateness of our consent for GWAS and data sharing; (3) evaluate our data sharing plan and align it with standards as they are developed; (4) develop and disseminate best practices for collecting, formatting, and documenting of phenotypic and genotype data in consultation with participants, IRBs, investigators and the community; (5) characterize and evaluate the representativeness and diversity of NUgene participants in relationship to individuals receiving healthcare through Northwestern's health care affiliates, the Chicagoland area and the US population; (6) perform GWAS on NUgene DNA cases and controls to compare genetic variations associated with diabetes and asthma in our population with previous GWAS on more traditionally defined collections of diabetes and asthma cases. With the active engagement of stakeholders including the community, research investigators and regulatory and compliance experts we will evaluate and develop best practices for studying genetic contributions to health and disease using participants enrolled in the course of their health care. These studies will contribute to our understanding of diabetes and asthma and will produce recommendations for improvements in EMRs that will improve their utility for research and set the stage for integration of genomic information and EMRs with the goal of achieving the vision of personalized medicine. ? ? ? ?
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