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

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    s41467-018-05624-4.pdf
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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. cc
    Weiss, Scott T.
    Wang, Thomas J.
    Stein, C. Michael
    Denny, Josh C.
    Roden, Dan M.
    Show allShow less
    Affiliation
    Univ Arizona, Coll Pharm, Dept Pharm Practice & Sci
    Issue Date
    2018-08-30
    
    Metadata
    Show full item record
    Publisher
    NATURE PUBLISHING GROUP
    Citation
    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/s41467-018-05624-4
    Journal
    NATURE COMMUNICATIONS
    Rights
    © 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
    2041-1723
    PubMed ID
    30166544
    DOI
    10.1038/s41467-018-05624-4
    Version
    Final published version
    Sponsors
    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/s41467-018-05624-4
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41467-018-05624-4
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