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Nat Commun. 2018 Aug 30;9(1):3522. doi: 10.1038/s41467-018-05624-4.

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

Author information

1
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. jonathan.d.mosley@vanderbilt.edu.
2
Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. jonathan.d.mosley@vanderbilt.edu.
3
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
4
Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
5
Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
6
Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
7
Essentia Institute of Rural Health, Duluth, MN, 55805, USA.
8
Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
9
Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, 21205, USA.
10
Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, 98195, USA.
11
Departments of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA.
12
Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA.
13
Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, 55905, USA.
14
Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
15
Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, 54449, USA.
16
Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI, 54449, USA.
17
Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
18
Genomic Medicine Institute, Geisinger Health System, Danville, PA, 17822, USA.
19
Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, 17822, USA.
20
Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, Arizona, 85721, USA.
21
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
22
Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.

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.

PMID:
30166544
PMCID:
PMC6117367
DOI:
10.1038/s41467-018-05624-4
[Indexed for MEDLINE]
Free PMC Article

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