학술논문
A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
Document Type
article
Author
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
Source
Nature Communications, Vol 9, Iss 1, Pp 1-11 (2018)
Subject
Language
English
ISSN
2041-1723
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.