Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

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• 作者：Rui Chen¹ ; ¹¹ ; George  ; I. Mias¹ ; ¹¹ ; Jennifer Li-Pook-Than¹ ; ¹¹ ; Lihua Jiang¹ ; ¹¹ ; Hugo  ; Y.K. Lam¹ ; ¹² ; Rong Chen² ; ¹² ; Elana Miriami¹ ; Konrad  ; J. Karczewski¹ ; Manoj Hariharan¹ ; Frederick  ; E. Dewey³ ; Yong Cheng¹ ; Michael  ; J. Clark¹ ; Hogune Im¹ ; Lukas Habegger⁶ ; ⁷ ; Suganthi Balasubramanian⁶ ; ⁷ ; Maeve O'Huallachain¹ ; Joel  ; T. Dudley² ; Sara Hillenmeyer¹ ; Rajini Haraksingh¹ ; Donald Sharon¹ ; Ghia Euskirchen¹ ; Phil Lacroute¹ ; Keith Bettinger¹ ; Alan  ; P. Boyle¹ ; Maya Kasowski¹ ; Fabian Grubert¹ ; Scott Seki² ; Marco Garcia² ; Michelle Whirl-Carrillo¹ ; Mercedes Gallardo⁹ ; ¹⁰ ; Maria  ; A. Blasco⁹ ; Peter  ; L. Greenberg⁴ ; Phyllis Snyder¹ ; Teri  ; E. Klein¹ ; Russ  ; B. Altman¹ ; ⁵ ; Atul J. Butte² ; Euan  ; A. Ashley³ ; Mark Gerstein⁶ ; ⁷ ; ⁸ ; Kari  ; C. Nadeau² ; Hua Tang¹ ; Michael Snyder¹ ; ^{mpsnyder@stanford.edu}
• 刊名：Cell
• 出版年：2012
• 出版时间：16 March, 2012
• 年：2012
• 卷：148
• 期：6
• 页码：1293-1307
• 全文大小：7554 K

文摘

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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.