Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record

Marylyn D. Ritchie, Joshua C. Denny, Dana C. Crawford, Andrea H. Ramirez, Justin B. Weiner, Jill M. Pulley, Melissa A. Basford, Kristin Brown-Gentry, Jeffrey R. Balser, Daniel R. Masys, Jonathan L. Haines, Dan M. Roden

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250 Scopus citations


Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (ORPR) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70-698) and controls (n = 808-3818) from deidentified health records. Each of the 21 tests of association yielded point estimates in the expected direction. Previous genotype-phenotype associations were replicated (p < 0.05) in 8/14 cases when the ORPR was > 1.25, and in 0/7 with lower ORPR. Statistically significant associations were detected in all analyses that were adequately powered. In each of the five diseases studied, at least one previously reported association was replicated. These data demonstrate that phenotypes representing clinical diagnoses can be extracted from EMR systems, and they support the use of DNA resources coupled to EMR systems as tools for rapid generation of large data sets required for replication of associations found in research cohorts and for discovery in genome science.

Original languageEnglish (US)
Pages (from-to)560-572
Number of pages13
JournalAmerican Journal of Human Genetics
Issue number4
StatePublished - Apr 9 2010

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


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