electronic MEdical Records and GEnomics (eMERGE) Network

Graduate student Janina Jeff presenting her work in eMERGE at the American Society of Human Genetics in San Francisco (2012).

The electronic MEdical Records and GEnomics (eMERGE) Network is a consortium of biobanks linked to electronic health records (EHRs) funded by the National Human Genome Research Institute at the National Institutes of Health. The Crawford lab was affiliated with eMERGE I and II as part of the Vanderbilt Genome-Electronic Records (VGER) study site, which leveraged the Nashville, TN biobank BioVU developed by Vanderbilt University. The primary goals of early eMERGE Network work included validating computable phenotypes and their use in genomic research as well as using these data for genomic discovery for clinical outcomes using genome-wide association studies.  The first phases of eMERGE also included work in return of research results to participants and developing clinical decision support for clinically relevant variants such as pharmacogenomics.

eMERGE Network and related biobank-electronic health records (EHRs) publications

  1. Ferar, K, Hall, TO, Crawford, DC, Rowley, R, Satterfield, BA, Li, R, Gragert, L, Karlson, EW, de Andrade, M, Kullo, IJ et al.. Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection. Sci Rep 2023; 13 (1): 18532. PubMed PMID:37898691 PubMed Central PMC10613277.
  2. Kaur, H, Crawford, DC, Liang, J, Benchek, P, COGENT BP Consortium, Zhu, X, Kallianpur, AR, Bush, WS. Replication of European hypertension associations in a case-control study of 9,534 African Americans. PLoS One 2021; 16 (11): e0259962. PubMed PMID:34793544 PubMed Central PMC8601554.
  3. Hollister, BM, Farber-Eger, E, Aldrich, MC, Crawford, DC. A Social Determinant of Health May Modify Genetic Associations for Blood Pressure: Evidence From a SNP by Education Interaction in an African American Population. Front Genet 2019; 10 : 428. PubMed PMID:31134134 PubMed Central PMC6523518.
  4. Pendergrass, SA, Crawford, DC. Using Electronic Health Records To Generate Phenotypes For Research. Curr Protoc Hum Genet 2019; 100 (1): e80. PubMed PMID:30516347 PubMed Central PMC6318047.
  5. Restrepo, NA, Laper, SM, Farber-Eger, E, Crawford, DC. Local genetic ancestry in CDKN2B-AS1 is associated with primary open-angle glaucoma in an African American cohort extracted from de-identified electronic health records. BMC Med Genomics 2018; 11 (Suppl 3): 70. PubMed PMID:30255811 PubMed Central PMC6157155.
  6. Crawford, DC, Restrepo, NA, Diggins, KE, Farber-Eger, E, Wells, QS. Frequency and phenotype consequence of APOC3 rare variants in patients with very low triglyceride levels. BMC Med Genomics 2018; 11 (Suppl 3): 66. PubMed PMID:30255797 PubMed Central PMC6156840.
  7. El Rouby, N, McDonough, CW, Gong, Y, McClure, LA, Mitchell, BD, Horenstein, RB, Talbert, RL, Crawford, DC, eMERGE network, Gitzendanner, MA et al.. Genome-wide association analysis of common genetic variants of resistant hypertension. Pharmacogenomics J 2019; 19 (3): 295-304. PubMed PMID:30237584 PubMed Central PMC6426691.
  8. Goodloe, R, Farber-Eger, E, Boston, J, Crawford, DC, Bush, WS. Reducing Clinical Noise for Body Mass Index Measures Due to Unit and Transcription Errors in the Electronic Health Record. AMIA Jt Summits Transl Sci Proc 2017; 2017 : 102-111. PubMed PMID:28815116 PubMed Central PMC5543370.
  9. Dumitrescu, L, Ritchie, MD, Denny, JC, El Rouby, NM, McDonough, CW, Bradford, Y, Ramirez, AH, Bielinski, SJ, Basford, MA, Chai, HS et al.. Genome-wide study of resistant hypertension identified from electronic health records. PLoS One 2017; 12 (2): e0171745. PubMed PMID:28222112 PubMed Central PMC5319785.
  10. Hollister, BM, Restrepo, NA, Farber-Eger, E, Crawford, DC, Aldrich, MC, Non, A. DEVELOPMENT AND PERFORMANCE OF TEXT-MINING ALGORITHMS TO EXTRACT SOCIOECONOMIC STATUS FROM DE-IDENTIFIED ELECTRONIC HEALTH RECORDS. Pac Symp Biocomput 2017; 22 : 230-241. PubMed PMID:27896978 PubMed Central PMC5147499.
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