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 | |
| State | Published - Dec 1 2018 |
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ASJC Scopus subject areas
- Chemistry(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Physics and Astronomy(all)
Cite this
A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers. / Mosley, Jonathan D.; Feng, Qi Ping; 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 Jan; 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.
In: Nature Communications, Vol. 9, No. 1, 3522, 01.12.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
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 Jan
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
Y1 - 2018/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85052679446&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052679446&partnerID=8YFLogxK
U2 - 10.1038/s41467-018-05624-4
DO - 10.1038/s41467-018-05624-4
M3 - Article
C2 - 30166544
AN - SCOPUS:85052679446
VL - 9
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 3522
ER -