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dc.contributor.authorMosley, Jonathan D.
dc.contributor.authorFeng, QiPing
dc.contributor.authorWells, Quinn S.
dc.contributor.authorVan Driest, Sara L.
dc.contributor.authorShaffer, Christian M.
dc.contributor.authorEdwards, Todd L.
dc.contributor.authorBastarache, Lisa
dc.contributor.authorWei, Wei-Qi
dc.contributor.authorDavis, Lea K.
dc.contributor.authorMcCarty, Catherine A.
dc.contributor.authorThompson, Will
dc.contributor.authorChute, Christopher G.
dc.contributor.authorJarvik, Gail P.
dc.contributor.authorGordon, Adam S.
dc.contributor.authorPalmer, Melody R.
dc.contributor.authorCrosslin, David R.
dc.contributor.authorLarson, Eric B.
dc.contributor.authorCarrell, David S.
dc.contributor.authorKullo, Iftikhar J.
dc.contributor.authorPacheco, Jennifer A.
dc.contributor.authorPeissig, Peggy L.
dc.contributor.authorBrilliant, Murray H.
dc.contributor.authorLinneman, James G.
dc.contributor.authorNamjou, Bahram
dc.contributor.authorWilliams, Marc S.
dc.contributor.authorRitchie, Marylyn D.
dc.contributor.authorBorthwick, Kenneth M.
dc.contributor.authorVerma, Shefali S.
dc.contributor.authorKarnes, Jason H.
dc.contributor.authorWeiss, Scott T.
dc.contributor.authorWang, Thomas J.
dc.contributor.authorStein, C. Michael
dc.contributor.authorDenny, Josh C.
dc.contributor.authorRoden, Dan M.
dc.date.accessioned2018-12-19T18:09:45Z
dc.date.available2018-12-19T18:09:45Z
dc.date.issued2018-08-30
dc.identifier.citationMosley, 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/s41467-018-05624-4en_US
dc.identifier.issn2041-1723
dc.identifier.pmid30166544
dc.identifier.doi10.1038/s41467-018-05624-4
dc.identifier.urihttp://hdl.handle.net/10150/631218
dc.description.abstractDefining 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.en_US
dc.description.sponsorshipVanderbilt-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]en_US
dc.language.isoenen_US
dc.publisherNATURE PUBLISHING GROUPen_US
dc.relation.urlhttp://www.nature.com/articles/s41467-018-05624-4en_US
dc.rights© The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.titleA study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkersen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Coll Pharm, Dept Pharm Practice & Scien_US
dc.identifier.journalNATURE COMMUNICATIONSen_US
dc.description.noteOpen access journal.en_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleNature Communications
dc.source.volume9
dc.source.issue1
refterms.dateFOA2018-12-19T18:09:45Z


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