title: The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.
abstract: BACKGROUND
  Biorepositories linked to de-identified electronic medical records (EMRs) have the potential to complement traditional epidemiologic studies in genotype-phenotype studies of complex human diseases and traits. A major challenge in meeting this potential is the use of EMR-derived data to extract phenotypes and covariates for genetic association studies. Unlike traditional epidemiologic data, EMR-derived data are collected for clinical care and are therefore highly variable across patients. The variability of clinical data coupled with the challenges associated with searching unstructured clinical notes requires the development of algorithms to extract phenotypes for analysis. Given the number of possible algorithms that could be developed for any one EMR-derived phenotype, we explored here the impact algorithm decision logic has on genetic association study results for a single quantitative trait, high density lipoprotein cholesterol (HDL-C).
  
  
  RESULTS
  We used five different algorithms to extract HDL-C from African American subjects genotyped on the Illumina Metabochip (n = 11,519) as part of Epidemiologic Architecture for Genes Linked to Environment (EAGLE). Tests of association between HDL-C and genetic risk scores for HDL-C associated variants suggest that the genetic effect size does not vary substantially across the five HDL-C definitions.
  
  
  CONCLUSIONS
  These data collectively suggest that, at least for this quantitative trait, algorithm decision logic and phenotyping details do not appreciably impact genetic association study test statistics.
authors.affiliation: Center for Human Genetics Research, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232 USA ; Department of Molecular Physiology and Biophysics, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232 USA.
authors.forename: Logan
authors.initials: L
authors.lastname: Dumitrescu
authors.affiliation: Center for Human Genetics Research, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232 USA ; Department of Molecular Physiology and Biophysics, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232 USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Goodloe
authors.affiliation: Center for Systems Genomics, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 512 Wartik Laboratory, University Park, PA 16802 USA.
authors.forename: Yukiko
authors.initials: Y
authors.lastname: Bradford
authors.affiliation: Center for Human Genetics Research, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232 USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Farber-Eger
authors.affiliation: Center for Human Genetics Research, Vanderbilt University, 2215 Garland Avenue, 519 Light Hall, Nashville, TN 37232 USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Boston
authors.affiliation: Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road, Suite 2527, Cleveland, OH 44106 USA.
authors.forename: Dana C
authors.initials: DC
authors.lastname: Crawford
keywords: Electronic medical record; Genetic risk score; HDL-C; PAGE I study; eMERGE network