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

Nat Commun 2018 08 30;9(1):3522. Epub 2018 Aug 30.

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.

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 rateĀ (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.

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Source
http://www.nature.com/articles/s41467-018-05624-4
Publisher Site
http://dx.doi.org/10.1038/s41467-018-05624-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117367PMC
August 2018
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