Nature Communications (Aug 2018)
A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
- Jonathan D. Mosley,
- QiPing 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
Affiliations
- Jonathan D. Mosley
- Department of Medicine, Vanderbilt University Medical Center
- QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center
- Quinn S. Wells
- Department of Medicine, Vanderbilt University Medical Center
- Sara L. Van Driest
- Department of Medicine, Vanderbilt University Medical Center
- Christian M. Shaffer
- Department of Medicine, Vanderbilt University Medical Center
- Todd L. Edwards
- Vanderbilt Epidemiology Center, Vanderbilt University Medical Center
- Lisa Bastarache
- Biomedical Informatics, Vanderbilt University Medical Center
- Wei-Qi Wei
- Biomedical Informatics, Vanderbilt University Medical Center
- Lea K. Davis
- Department of Medicine, Vanderbilt University Medical Center
- Catherine A. McCarty
- Essentia Institute of Rural Health
- Will Thompson
- Department of Medicine, Feinberg School of Medicine, Northwestern University
- Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University
- Gail P. Jarvik
- Department of Medicine (Medical Genetics), University of Washington
- Adam S. Gordon
- Department of Medicine (Medical Genetics), University of Washington
- Melody R. Palmer
- Department of Medicine (Medical Genetics), University of Washington
- David R. Crosslin
- Departments of Biomedical Informatics and Medical Education, University of Washington
- Eric B. Larson
- Department of Medicine (Medical Genetics), University of Washington
- David S. Carrell
- Kaiser Permanente Washington Health Research Institute
- Iftikhar J. Kullo
- Department of Cardiovascular Diseases, Mayo Clinic
- Jennifer A. Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine
- Peggy L. Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute
- Murray H. Brilliant
- Center for Human Genetics, Marshfield Clinic Research Institute
- James G. Linneman
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center
- Marc S. Williams
- Genomic Medicine Institute, Geisinger Health System
- Marylyn D. Ritchie
- Biomedical and Translational Informatics, Geisinger Health System
- Kenneth M. Borthwick
- Biomedical and Translational Informatics, Geisinger Health System
- Shefali S. Verma
- Biomedical and Translational Informatics, Geisinger Health System
- Jason H. Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy
- Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School
- Thomas J. Wang
- Biomedical Informatics, Vanderbilt University Medical Center
- C. Michael Stein
- Department of Medicine, Vanderbilt University Medical Center
- Josh C. Denny
- Department of Medicine, Vanderbilt University Medical Center
- Dan M. Roden
- Department of Medicine, Vanderbilt University Medical Center
- DOI
- https://doi.org/10.1038/s41467-018-05624-4
- Journal volume & issue
-
Vol. 9,
no. 1
pp. 1 – 11
Abstract
Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.
