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 #
3U01HG006385-03S1
Application #
8721691
Study Section
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
$190,234
Indirect Cost
$68,354
Name
Vanderbilt University Medical Center
Department
Physiology
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
El Rouby, Nihal; McDonough, Caitrin W; Gong, Yan et al. (2018) Genome-wide association analysis of common genetic variants of resistant hypertension. Pharmacogenomics J :
Mosley, Jonathan D; Feng, QiPing; Wells, Quinn S et al. (2018) A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nat Commun 9:3522
Antommaria, Armand H Matheny; Brothers, Kyle B; Myers, John A et al. (2018) Parents' attitudes toward consent and data sharing in biobanks: A multisite experimental survey. AJOB Empir Bioeth 9:128-142
Roden, Dan M; Van Driest, Sara L; Mosley, Jonathan D et al. (2018) Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome. Clin Pharmacol Ther 103:787-794
Xia, Weiyi; Wan, Zhiyu; Yin, Zhijun et al. (2018) It's all in the timing: calibrating temporal penalties for biomedical data sharing. J Am Med Inform Assoc 25:25-31
Hall, Molly A; Wallace, John; Lucas, Anastasia et al. (2017) PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies. Nat Commun 8:1167
Nadkarni, Girish N; Galarneau, Geneviève; Ellis, Stephen B et al. (2017) Apolipoprotein L1 Variants and Blood Pressure Traits in African Americans. J Am Coll Cardiol 69:1564-1574
Prasser, Fabian; Gaupp, James; Wan, Zhiyu et al. (2017) An Open Source Tool for Game Theoretic Health Data De-Identification. AMIA Annu Symp Proc 2017:1430-1439
Li, Bo; Vorobeychik, Yevgeniy; Li, Muqun et al. (2017) Scalable Iterative Classification for Sanitizing Large-Scale Datasets. IEEE Trans Knowl Data Eng 29:698-711
Sileshi, Bantayehu; Newton, Mark W; Kiptanui, Joash et al. (2017) Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool. Anesthesiology 127:250-271

Showing the most recent 10 out of 77 publications