Determining the genetic architecture of human traits has been a successful and rapidly advancing aspect of Human Genetics. Our ability to characterize individual genetic variation is rapidly approaching the whole genome sequence level. However, equally important is rapid and detailed characterization of the phenotypic variation in the traits themselves, such that meaningful correlations can be identified between genotype and phenotype. The initial phase of the eMERGE network explored the use of electronic medical records for rapid and large-scale characterization of phenotypes and the ability to use linked DNA repositories to generate and analyze genetic variation. The eMERGE network has already demonstrated the viability and utility of this approach in a number of """"""""proof-of-principle"""""""" studies. It is now important to determine the portability and expandability of these approaches in a second and expanded phase of the network. Vanderbiit provided the underlying support for the initial eMERGE network through a supplement to its current eMERGE grant (VGER). We propose to continue our support for an expanded network through a coordinating center (eMERGE-CC) that will provide a combination of scientific and logistical efforts through four specific aims: 1). Accelerate phenotype algorithm development and sharing across the eMERGE-ll network;2). Expand methods to integrate high quality genomic information within EMRs across the eMERGE-ll network and analyze the resulting data;3). Expand and accelerate methods to determine the reidentification risk and levels of privacy afforded by performing research on combined clinical and genetic data from the eMERGE-ll network;and 4). Continue to provide logistical support to the entire eMERGE-ll network.

Public Health Relevance

The goal of the eMERGE project is to develop methods for using data from electronic medical records and data from genetic studies to better understand the genetic underpinnings of clinical disease. A further goal is to integrate this information into clinical care. The role of the Coordinating Center is to support these activities.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HG006385-04
Application #
8712534
Study Section
Special Emphasis Panel ()
Program Officer
Li, Rongling
Project Start
2011-08-15
Project End
2015-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
4
Fiscal Year
2014
Total Cost
$1,133,258
Indirect Cost
$367,398
Name
Vanderbilt University Medical Center
Department
Physiology
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
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
Verma, Shefali S; Frase, Alex T; Verma, Anurag et al. (2016) PHENOME-WIDE INTERACTION STUDY (PheWIS) IN AIDS CLINICAL TRIALS GROUP DATA (ACTG). Pac Symp Biocomput 21:57-68
Hripcsak, George; Mirhaji, Parsa; Low, Alexander Fh et al. (2016) Preserving temporal relations in clinical data while maintaining privacy. J Am Med Inform Assoc :

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