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

Autor: Mosley JD; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. jonathan.d.mosley@vanderbilt.edu.; Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. jonathan.d.mosley@vanderbilt.edu., Feng Q; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Wells QS; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Van Driest SL; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Shaffer CM; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Edwards TL; Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Bastarache L; Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Wei WQ; Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Davis LK; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., McCarty CA; Essentia Institute of Rural Health, Duluth, MN, 55805, USA., Thompson W; Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA., Chute CG; Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, 21205, USA., Jarvik GP; Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, 98195, USA., Gordon AS; Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, 98195, USA., Palmer MR; Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, 98195, USA., Crosslin DR; Departments of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA., Larson EB; Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, 98195, USA.; Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA., Carrell DS; Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA., Kullo IJ; Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, 55905, USA., Pacheco JA; Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA., Peissig PL; Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, 54449, USA., Brilliant MH; Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI, 54449, USA., Linneman JG; Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, 54449, USA., Namjou B; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA., Williams MS; Genomic Medicine Institute, Geisinger Health System, Danville, PA, 17822, USA., Ritchie MD; Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, 17822, USA., Borthwick KM; Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, 17822, USA., Verma SS; Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, 17822, USA., Karnes JH; Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, Arizona, 85721, USA., Weiss ST; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA., Wang TJ; Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Stein CM; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Denny JC; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.; Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA., Roden DM; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.; Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.; Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2018 Aug 30; Vol. 9 (1), pp. 3522. Date of Electronic Publication: 2018 Aug 30.
DOI: 10.1038/s41467-018-05624-4
Abstrakt: 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.
Databáze: MEDLINE