Cell - Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Cell - Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Cell, Volume 148, Issue 6, 1293-1307, 16 March 2012
Copyright 2012 Elsevier Inc. All rights reserved.
10.1016/j.cell.2012.02.009

Authors

Rui Chen, George I. Mias, Jennifer Li-Pook-Than, Lihua Jiang, Hugo Y.K. Lam, Rong Chen, Elana Miriami, Konrad J. Karczewski, Manoj Hariharan, Frederick E. Dewey, Yong Cheng, Michael J. Clark, Hogune Im, Lukas Habegger, Suganthi Balasubramanian, Maeve O'Huallachain, Joel T. Dudley, Sara Hillenmeyer, Rajini Haraksingh, Donald Sharon, Ghia Euskirchen, Phil Lacroute, Keith Bettinger, Alan P. Boyle, Maya Kasowski, Fabian Grubert, Scott Seki, Marco Garcia, Michelle Whirl-Carrillo, Mercedes Gallardo, Maria A. Blasco, Peter L. Greenberg, Phyllis Snyder, Teri E. Klein, Russ B. Altman, Atul J. Butte, Euan A. Ashley, Mark Gerstein, Kari C. Nadeau, Hua Tang, Michael SnyderSee Affiliations

• Highlights
• Physiological states analyzed by integrative personal omics profiling
• Extensive molecular changes revealed during different health states
• Individual disease risk predicted from integrated omics data
• Extensive heteroallele and RNA editing during healthy and disease states

Summary

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.