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

Jonathan D. Mosley; Qi Ping Feng; Quinn S. Wells; Sara L. Van Driest; Christian M. Shaffer; Todd L. Edwards; Lisa Bastarache; Wei Qi Wei; Lea K. Davis; Catherine A. McCarty; Will Thompson; Christopher G. Chute; Gail P. Jarvik; Adam S. Gordon; Melody R. Palmer; David R. Crosslin; Eric B. Larson; David S. Carrell; Iftikhar J. Kullo; Jennifer A. Pacheco; Peggy L. Peissig; Murray H. Brilliant; James G. Linneman; Bahram Namjou; Marc S. Williams; Marylyn D. Ritchie; Kenneth M. Borthwick; Shefali S. Verma; Jason H. Karnes; Scott T. Weiss; Thomas J. Wang; C. Michael Stein; Josh C. Denny; Dan M. Roden

doi:10.1038/s41467-018-05624-4

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

Jonathan D. Mosley, Qi Ping Feng, Quinn S. Wells, Sara L. Van Driest, Christian M. Shaffer, Todd L. Edwards, Lisa Bastarache, Wei Qi Wei, Lea K. Davis, Catherine A. McCarty, Will Thompson, Christopher G. Chute, Gail P. Jarvik, Adam S. Gordon, Melody R. Palmer, David R. Crosslin, Eric B. Larson, David S. Carrell, Iftikhar J. Kullo, Jennifer A. PachecoPeggy L. Peissig, Murray H. Brilliant, James G. Linneman, Bahram Namjou, Marc S. Williams, Marylyn D. Ritchie, Kenneth M. Borthwick, Shefali S. Verma, Jason H. Karnes, Scott T. Weiss, Thomas J. Wang, C. Michael Stein, Josh C. Denny, Dan M. Roden

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11 Scopus citations

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.

Original language	English (US)
Article number	3522
Journal	Nature communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-05624-4
State	Published - Dec 1 2018
Externally published	Yes

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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10.1038/s41467-018-05624-4

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Mosley, J. D., Feng, Q. P., Wells, Q. S., Van Driest, S. L., Shaffer, C. M., Edwards, T. L., Bastarache, L., Wei, W. Q., Davis, L. K., McCarty, C. A., Thompson, W., Chute, C. G., Jarvik, G. P., Gordon, A. S., Palmer, M. R., Crosslin, D. R., Larson, E. B., Carrell, D. S., Kullo, I. J., ... Roden, D. M. (2018). A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. Nature communications, 9(1), Article 3522. https://doi.org/10.1038/s41467-018-05624-4

Mosley, JD, Feng, QP, Wells, QS, Van Driest, SL, Shaffer, CM, Edwards, TL, Bastarache, L, Wei, WQ, Davis, LK, McCarty, CA, Thompson, W, Chute, CG, Jarvik, GP, Gordon, AS, Palmer, MR, Crosslin, DR, Larson, EB, Carrell, DS, Kullo, IJ, Pacheco, JA, Peissig, PL, Brilliant, MH, Linneman, JG, Namjou, B, Williams, MS, Ritchie, MD, Borthwick, KM, Verma, SS, Karnes, JH, Weiss, ST, Wang, TJ, Stein, CM, Denny, JC & Roden, DM 2018, 'A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers', Nature communications, vol. 9, no. 1, 3522. https://doi.org/10.1038/s41467-018-05624-4

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title = "A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers",

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.",

author = "Mosley, {Jonathan D.} and Feng, {Qi Ping} and Wells, {Quinn S.} and {Van Driest}, {Sara L.} and Shaffer, {Christian M.} and Edwards, {Todd L.} and Lisa Bastarache and Wei, {Wei Qi} and Davis, {Lea K.} and McCarty, {Catherine A.} and Will Thompson and Chute, {Christopher G.} and Jarvik, {Gail P.} and Gordon, {Adam S.} and Palmer, {Melody R.} and Crosslin, {David R.} and Larson, {Eric B.} and Carrell, {David S.} and Kullo, {Iftikhar J.} and Pacheco, {Jennifer A.} and Peissig, {Peggy L.} and Brilliant, {Murray H.} and Linneman, {James G.} and Bahram Namjou and Williams, {Marc S.} and Ritchie, {Marylyn D.} and Borthwick, {Kenneth M.} and Verma, {Shefali S.} and Karnes, {Jason H.} and Weiss, {Scott T.} and Wang, {Thomas J.} and Stein, {C. Michael} and Denny, {Josh C.} and Roden, {Dan M.}",

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AU - Mosley, Jonathan D.

AU - Feng, Qi Ping

AU - Wells, Quinn S.

AU - Van Driest, Sara L.

AU - Shaffer, Christian M.

AU - Edwards, Todd L.

AU - Bastarache, Lisa

AU - Wei, Wei Qi

AU - Davis, Lea K.

AU - McCarty, Catherine A.

AU - Thompson, Will

AU - Chute, Christopher G.

AU - Jarvik, Gail P.

AU - Gordon, Adam S.

AU - Palmer, Melody R.

AU - Crosslin, David R.

AU - Larson, Eric B.

AU - Carrell, David S.

AU - Kullo, Iftikhar J.

AU - Pacheco, Jennifer A.

AU - Peissig, Peggy L.

AU - Brilliant, Murray H.

AU - Linneman, James G.

AU - Namjou, Bahram

AU - Williams, Marc S.

AU - Ritchie, Marylyn D.

AU - Borthwick, Kenneth M.

AU - Verma, Shefali S.

AU - Karnes, Jason H.

AU - Weiss, Scott T.

AU - Wang, Thomas J.

AU - Stein, C. Michael

AU - Denny, Josh C.

AU - Roden, Dan M.

PY - 2018/12/1

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N2 - 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.

AB - 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.

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

Abstract

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