The Seattle eMERGE project aims to bring personal genomics to practice settings by taking advantage of the extensive electronic medical record (EMR) and biorepository of Group Health Cooperative (GH), including a 33-year pharmacy database and longitudinal data on an aging population. Algorithms developed in eMERGE I will be used to combine genome-wide association studies with phenotypes mined from EMRs to discover new polymorphism-phenotype relationships. Target phenotypes are infectious disease susceptibility, specifically to Clostridium difficile diarrhea, shingles from varicella zoster virus, and fungal nail infection, responses to antihypertensive drugs, serotonin-specific reuptake inhibitors, and statins, including adverse events. A new algorithm will follow longitudinal glycemia and hematocrit trajectories, and a novel automated method will detect karyotype abnormalities for assessing correlation to myelodysplasia and leukemia. Data will also support phenotypes investigated at other eMERGE sites. To create a model for introducing genomics into clinical practice, successful needs assessment methods from eMERGE I will engage stakeholders in guiding development of prototype EMR user interfaces in a clinical decision support format. The test case will be human leukocyte antigen-typing for an adverse drug reaction and the setting will be the patient-centered medical home care model developed at GH. This proposal provides the eMERGE network and its collaborators with the Seattle team's unique expertise in using natural language processing (NLP) to extract information from EMRs, and assisting in adoption of NLP methods. To disseminate eMERGE results and foster collaborations, it takes advantage of leadership positions of the investigators, including partners within eMERGE, other consortia and the HMO Research network, especially the potential for developments supported by the NIH Director's Common Fund in biobanking and megaepidemiology. Completion of the aims will reveal new, medically useful markers, improve the linking of high-throughput genomic methods to EMR data, and develop policies and practices for bringing individualized evidence-based medicine to communities.

Public Health Relevance

To advance personalized medicine-treatment and preventive care based on individual traits;this project matches small differences in DNA to infectious disease susceptibility and response to statins, serotonin- specific reuptake inhibitors (SSRIs) and blood pressure medications. Methods to use these results in clinical care will be guided by focus groups of patients and caregivers in the patient-centered Group Health system.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZHG1-HGR-N (M1))
Program Officer
Li, Rongling
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Group Health Cooperative
United States
Zip Code
Zhang, Jinglan; Fedick, Anastasia; Wasserman, Stephanie et al. (2016) Analytical Validation of a Personalized Medicine APOL1 Genotyping Assay for Nondiabetic Chronic Kidney Disease Risk Assessment. J Mol Diagn 18:260-6
Verma, Anurag; Verma, Shefali S; Pendergrass, Sarah A et al. (2016) eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants. BMC Med Genomics 9 Suppl 1:32
Jun, G; Ibrahim-Verbaas, C A; Vronskaya, M et al. (2016) A novel Alzheimer disease locus located near the gene encoding tau protein. Mol Psychiatry 21:108-17
Leo, Michael C; McMullen, Carmit; Wilfond, Benjamin S et al. (2016) Patients' ratings of genetic conditions validate a taxonomy to simplify decisions about preconception carrier screening via genome sequencing. Am J Med Genet A 170:574-82
Rasmussen, Luke V; Overby, Casey L; Connolly, John et al. (2016) Practical considerations for implementing genomic information resources. Experiences from eMERGE and CSER. Appl Clin Inform 7:870-82
Hohman, Timothy J; Cooke-Bailey, Jessica N; Reitz, Christiane et al. (2016) Global and local ancestry in African-Americans: Implications for Alzheimer's disease risk. Alzheimers Dement 12:233-43
Garrison, Nanibaa' A; Sathe, Nila A; Antommaria, Armand H Matheny et al. (2016) A systematic literature review of individuals' perspectives on broad consent and data sharing in the United States. Genet Med 18:663-71
Mez, Jesse; Mukherjee, Shubhabrata; Thornton, Timothy et al. (2016) The executive prominent/memory prominent spectrum in Alzheimer's disease is highly heritable. Neurobiol Aging 41:115-21
Korngiebel, Diane M; McMullen, Carmit K; Amendola, Laura M et al. (2016) Generating a taxonomy for genetic conditions relevant to reproductive planning. Am J Med Genet A 170:565-73
Ridge, Perry G; Hoyt, Kaitlyn B; Boehme, Kevin et al. (2016) Assessment of the genetic variance of late-onset Alzheimer's disease. Neurobiol Aging 41:200.e13-20

Showing the most recent 10 out of 116 publications