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A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers

Author

Listed:
  • Jonathan D. Mosley

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

  • QiPing Feng

    (Vanderbilt University Medical Center)

  • Quinn S. Wells

    (Vanderbilt University Medical Center)

  • Sara L. Van Driest

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

  • Christian M. Shaffer

    (Vanderbilt University Medical Center)

  • Todd L. Edwards

    (Vanderbilt University Medical Center)

  • Lisa Bastarache

    (Vanderbilt University Medical Center)

  • Wei-Qi Wei

    (Vanderbilt University Medical Center)

  • Lea K. Davis

    (Vanderbilt University Medical Center)

  • Catherine A. McCarty

    (Essentia Institute of Rural Health)

  • Will Thompson

    (Northwestern University)

  • Christopher G. Chute

    (Johns Hopkins University)

  • Gail P. Jarvik

    (University of Washington)

  • Adam S. Gordon

    (University of Washington)

  • Melody R. Palmer

    (University of Washington)

  • David R. Crosslin

    (University of Washington)

  • Eric B. Larson

    (University of Washington
    Kaiser Permanente Washington Health Research Institute)

  • David S. Carrell

    (Kaiser Permanente Washington Health Research Institute)

  • Iftikhar J. Kullo

    (Mayo Clinic)

  • Jennifer A. Pacheco

    (Northwestern University Feinberg School of Medicine)

  • Peggy L. Peissig

    (Marshfield Clinic Research Institute)

  • Murray H. Brilliant

    (Marshfield Clinic Research Institute)

  • James G. Linneman

    (Marshfield Clinic Research Institute)

  • Bahram Namjou

    (Cincinnati Children’s Hospital Medical Center)

  • Marc S. Williams

    (Genomic Medicine Institute, Geisinger Health System)

  • Marylyn D. Ritchie

    (Geisinger Health System)

  • Kenneth M. Borthwick

    (Geisinger Health System)

  • Shefali S. Verma

    (Geisinger Health System)

  • Jason H. Karnes

    (University of Arizona College of Pharmacy)

  • Scott T. Weiss

    (Brigham and Women’s Hospital, Harvard Medical School)

  • Thomas J. Wang

    (Vanderbilt University Medical Center)

  • C. Michael Stein

    (Vanderbilt University Medical Center)

  • Josh C. Denny

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

  • Dan M. Roden

    (Vanderbilt University Medical Center
    Vanderbilt University Medical Center
    Vanderbilt University Medical Center)

Abstract

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.

Suggested Citation

  • Jonathan D. Mosley & QiPing Feng & Quinn S. Wells & Sara L. Van Driest & Christian M. Shaffer & Todd L. Edwards & Lisa Bastarache & Wei-Qi Wei & Lea K. Davis & Catherine A. McCarty & Will Thompson & C, 2018. "A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05624-4
    DOI: 10.1038/s41467-018-05624-4
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