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

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, Christopher G. Chute, Gail P. Jarvik, Adam S. Gordon, Melody R. Palmer, David R. Crosslin, Eric B. Larson, David S. Carrell, Iftikhar J. Kullo, Jennifer A. Pacheco, Peggy L. Peissig, Murray H. Brilliant, James G. Linneman, Bahram Namjou, Marc S. Williams, Marylyn D. Ritchie, Kenneth M. Borthwick, Shefali S. Verma, Jason H. Karnes, Scott T. Weiss, Thomas J. Wang, C. Michael Stein, Josh C. Denny and Dan M. Roden
Additional contact information
Jonathan D. Mosley: 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
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
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
Dan M. Roden: Vanderbilt University Medical Center

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05624-4

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DOI: 10.1038/s41467-018-05624-4

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