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

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

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.

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Publishing Group 2018-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-05624-4

Internet

https://doi.org/10.1038/s41467-018-05624-4

Similar Items