The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies

By C. McCarty, R. Chisholm, Christopher G. Chute, I. Kullo, G. Jarvik, E. Larson, Rongling Li, D. Masys, Marylyn DeRiggi Ritchie, D. Roden and 2 more. BMC Medical Genomics, 2011

709 citations recorded by the source corpus.

IntroductionThe eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRI-supported consortium of five institutions to explore the utility of DNA repositories coupled to Electronic Medical Record (EMR) systems for advancing discovery in genome science. eMERGE also includes a special emphasis on the ethical, legal and social issues related to these endeavors.OrganizationThe five sites are supported by an Administrative Coordinating Center. Setting of network goals is initiated by working groups: (1) Genomics, (2) Informatics, and (3) Consent & Community Consultation, which also includes active participation by investigators outside the eMERGE funded sites, and (4) Return of Results Oversight Committee. The Steering Committee, comprised of site PIs and representatives and NHGRI staff, meet three times per year, once per year with the External Scientific Panel.Current progressThe primary site-specific phenotypes for which samples have undergone genome-wide association study (GWAS) genotyping are cataract and HDL, dementia, electrocardiographic QRS duration, peripheral arterial disease, and type 2 diabetes. A GWAS is also being undertaken for resistant hypertension in ≈2,000 additional samples identified across the network sites, to be added to data available for samples already genotyped. Funded by ARRA supplements, secondary phenotypes have been added at all sites to leverage the genotyping data, and hypothyroidism is being analyzed as a cross-network phenotype. Results are being posted in dbGaP. Other key eMERGE activities include evaluation of the issues associated with cross-site deployment of common algorithms to identify cases and controls in EMRs, data privacy of genomic and clinically-derived data, developing approaches for large-scale meta-analysis of GWAS data across five sites, and a community consultation and consent initiative at each site.Future activitiesPlans are underway to expand the network in diversity of populations and incorporation of GWAS findings into clinical care.SummaryBy combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.

Subfields: Genetics

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Citations in the corpus

Large-Scale Discovery of Disease-Disease and Disease-Gene Associations (2016, methodology): “However, current approaches for phenotyping and gene discovery using EHR data rely on highly supervised rule-based or heuristic-based meth-ods, which require manual labor and often a consensus of medical experts 22 .”
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications (2015, background): “These efforts have been summarized elsewhere (12, 49, 66, 81, 95, 120) and are not covered in this review.”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, methodology): “The primary goal of the eMERGE network is to develop methods and best practices for the utilization of EHRs for genetic research [54,55].”
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers (2016, methodology): “…used in various disciplines, such as in data management [Zhang and Zhao, 2005, Verykios et al., 2004, Aggarwal and Philip, 2008], biomedical and social sciences [McCarty et al., 2011, Zheng et al., 2017] where data are distributed in nature and single databases only have limited samples sizes.”
Identifying and Mitigating Biases in EHR Laboratory Tests (2014, positive): “…analysis of the variables in this vast dataset, researchers can conduct a variety of multifaceted studies such as prediction of future patient health state, evaluation of intervention effectiveness , computational disease modeling, and identification of dangerous drug–drug interactions [1] [2] [3].”
AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “Recent years have seen an increase in research and initiatives related to AI in health, integrating different aspects of clinical data [110], linking biorepositories with clinical data [111-113], and forging connections between pharmaceutical research and development with clinical data [84].”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, neutral): “Examples of such large-scale phenotyping efforts are typ-iﬁed by the Electronic Medical Records and Genomics (eMERGE) Network [34] and the Observational Medical Outcomes Partner-ship (OMOP) [35].”
A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases (2015, methodology): “Currently, 34 out of 47 published phenotyping algorithms on the eMERGE (McCarty et al., 2011) Phenotype KnowledgBase include NLP components (https:// phekb.org/ Accessed: February 13 th 2015) .”