A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
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Author
Mosley, Jonathan D.Feng, QiPing
Wells, Quinn S.
Van Driest, Sara L.
Shaffer, Christian M.
Edwards, Todd L.
Bastarache, Lisa
Wei, Wei-Qi
Davis, Lea K.
McCarty, Catherine A.
Thompson, Will
Chute, Christopher G.
Jarvik, Gail P.
Gordon, Adam S.
Palmer, Melody R.
Crosslin, David R.
Larson, Eric B.
Carrell, David S.
Kullo, Iftikhar J.
Pacheco, Jennifer A.
Peissig, Peggy L.
Brilliant, Murray H.
Linneman, James G.
Namjou, Bahram
Williams, Marc S.
Ritchie, Marylyn D.
Borthwick, Kenneth M.
Verma, Shefali S.
Karnes, Jason H.

Weiss, Scott T.
Wang, Thomas J.
Stein, C. Michael
Denny, Josh C.
Roden, Dan M.
Affiliation
Univ Arizona, Coll Pharm, Dept Pharm Practice & SciIssue Date
2018-08-30
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NATURE PUBLISHING GROUPCitation
Mosley, J. D., Feng, Q., Wells, Q. S., Van Driest, S. L., Shaffer, C. M., Edwards, T. L., ... & Thompson, W. (2018). A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nature communications, 9(1), 3522. https://doi.org/10.1038/s41467-018-05624-4Journal
NATURE COMMUNICATIONSRights
© The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.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.Note
Open access journal.ISSN
2041-1723PubMed ID
30166544Version
Final published versionSponsors
Vanderbilt-Ingram Cancer Center; Vanderbilt Vision Center; Vanderbilt Faculty Research Scholars Fund; American Heart Association [16FTF30130005]; PGRN [P50-GM115305, R01 GM10945, R01 LM010685, R01 HL133786-01A1, R01 GM120523, 16SDG29090005]; Burroughs Wellcome Fund [IRSA 1015006]; CTSA award [KL2TR000446]; NIH [S10RR025141]; CTSA [UL1TR002243, UL1TR000445, UL1RR024975]; NHGRI [U01HG006378, U01HG006830, U01HG006389, U01HG006382, U01HG006375, U01HG006379, U01HG006380, U01HG006388, U01HG8685, U01HG8672, U01HG006385, U01HG004438, U01HG004424]; NHLBI [HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C]; NHGRI grant [U01HG004402]; [U01HG004798]; [R01NS032830]; [RC2GM092618]; [P50GM115305]; [U19HL065962]; [R01HD074711]Additional Links
http://www.nature.com/articles/s41467-018-05624-4ae974a485f413a2113503eed53cd6c53
10.1038/s41467-018-05624-4