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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HG006388-03
Application #
8523193
Study Section
Special Emphasis Panel (ZHG1-HGR-N (M1))
Program Officer
Li, Rongling
Project Start
2011-08-15
Project End
2015-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$958,491
Indirect Cost
$331,927
Name
Northwestern University at Chicago
Department
Anatomy/Cell Biology
Type
Schools of Medicine
DUNS #
005436803
City
Chicago
State
IL
Country
United States
Zip Code
60611
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
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
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
Heatherly, Raymond; Rasmussen, Luke V; Peissig, Peggy L et al. (2016) A multi-institution evaluation of clinical profile anonymization. J Am Med Inform Assoc 23:e131-7
Simonti, Corinne N; Vernot, Benjamin; Bastarache, Lisa et al. (2016) The phenotypic legacy of admixture between modern humans and Neandertals. Science 351:737-41
Van Driest, Sara L; Wells, Quinn S; Stallings, Sarah et al. (2016) Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records. JAMA 315:47-57
Smith, Maureen E; Sanderson, Saskia C; Brothers, Kyle B et al. (2016) Conducting a large, multi-site survey about patients' views on broad consent: challenges and solutions. BMC Med Res Methodol 16:162
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
Bush, W S; Crosslin, D R; Owusu-Obeng, A et al. (2016) Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network. Clin Pharmacol Ther 100:160-9
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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