One promise of the human genome project was to enable genome-informed personalized medicine. During the past four years Northwestern has been a site in the eMERGE network. This consortium of biobanks linked to electronic health records (EHR) has developed portable algorithms to identify cases and controls from EHR data and then performed genome-wide association studies (GWAS) to correlate genetic variation with disease and normal physiological variation in widely measured laboratory values. In response to RFA-HG-10-009, we propose to contribute to the network development of additional phenotype algorithms and the analysis of the genotype data from the Northwestern eMERGE cohort supplemented by approximately 3,000 additional EHR-linked samples, each associated with 660k GWAS genotypes. We will develop a range of phenotypes that will allow us to assess patient and physician attitudes to the utility of genetic information in predicting disease susceptibility, drug response and therapeutic outcomes. Based on these consultations, we propose to develop a modified quality improvement model for determining, in a pilot study, which genotypes might be most valuable to present in a clinical care setting. We will develop a consent model and associated educational methods in support of providing experimental subjects with genotype information in a clinical encounter, including CLIA certified re-genotyping of participants who were previously genotyped for research purposes. At Northwestern, we utilize a widely-deployed, commercial EHR, EPIC, and propose to develop technical approaches for integrating genetic variation data into the health record and to effectively present these results using point-of-care, decision support tools to physicians. A goal of this effort is to develop best practices collaboratively within the network, for reporting of genetic variation data and developing local practice guidelines for using genetic data in primary care clinical encounters. Finally, we propose a rigorous assessment of the impact of these approaches on primary care physicians and their patients, defining the regulatory issues and then disseminating lessons learned and best practice recommendations. Together, the work proposed should provide an assessment of key elements of genome-informed personalized medicine.
This project begins to answer questions about using genomic analysis and applying it to real world clinical situations. We propose to study the clinical and personal utility of genomic variation in a diverse primary care patient and physician population.
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|Jackson, Kathryn L; Mbagwu, Michael; Pacheco, Jennifer A et al. (2016) Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies. BMC Infect Dis 16:684|
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|Mo, Huan; Jiang, Guoqian; Pacheco, Jennifer A et al. (2016) A Decompositional Approach to Executing Quality Data Model Algorithms on the i2b2 Platform. AMIA Jt Summits Transl Sci Proc 2016:167-75|
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