Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits

Dana C. Crawford, Robert Goodloe, Eric Farber-Eger, Jonathan Boston, Sarah A. Pendergrass, Jonathan L. Haines, Marylyn D. Ritchie, William S. Bush

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Background/Aims: Present-day limited resources demand DNA and phenotyping alternatives to the traditional prospective population-based epidemiologic collections. Methods: To accelerate genomic discovery with an emphasis on diverse populations, we - as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study - accessed all non-European American samples (n = 15,863) available in BioVU, the Vanderbilt University biorepository linked to de-identified electronic medical records, for genomic studies as part of the larger Population Architecture using Genomics and Epidemiology (PAGE) I study. Given previous studies have cautioned against the secondary use of clinically collected data compared with epidemiologically collected data, we present here a characterization of EAGLE BioVU, including the billing and diagnostic (ICD-9) code distributions for adult and pediatric patients as well as comparisons made for select health metrics (body mass index, glucose, HbA1c, HDL-C, LDL-C, and triglycerides) with the population-based National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples (NHANES III, n = 7,159; NHANES 1999-2002, n = 7,839). Results: Overall, the distributions of billing and diagnostic codes suggest this clinical sample is a mixture of healthy and sick patients like that expected for a contemporary American population. Conclusion: Little bias is observed among health metrics, suggesting this clinical collection is suitable for genomic studies along with traditional epidemiologic cohorts.

Original languageEnglish (US)
Pages (from-to)137-146
Number of pages10
JournalHuman heredity
Volume79
Issue number3-4
DOIs
StatePublished - Jul 25 2015

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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