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.
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.
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 |
Peissig, Peggy; Schwei, Kelsey M; Kadolph, Christopher et al. (2017) Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application. JMIR Med Inform 5:e27 |
Sanderson, Saskia C; Brothers, Kyle B; Mercaldo, Nathaniel D et al. (2017) Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US. Am J Hum Genet 100:414-427 |
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 |
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