Cited by 1,291Bioinformatics and Genomic NetworksHealth, Environment, Cognitive AgingMetabolomics and Mass Spectrometry Studies

Personal omics profiling reveals dynamic molecular and medical phenotypes

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Chen, Rui, Mias, George I., Li-Pook-Than, Jennifer, Jiang, Lihua, Lam, Hugo Y.K., Chen, Rong, Miriami, Elana, Karczewski, Konrad J. +33 more·2012·Cell

Abstract

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.

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Crossref · 10.1016/j.cell.2012.02.009

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Authors
Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., Cheng, Y., Clark, M. J., Im, H., Habegger, L., Balasubramanian, S., O'Huallachain, M., Dudley, J. T., Hillenmeyer, S., Haraksingh, R., Sharon, D., Euskirchen, G., Lacroute, P., Bettinger, K., Boyle, A. P., Kasowski, M., Grubert, F., Seki, S., Garcia, M., Whirl-Carrillo, M., Gallardo, M., Blasco, M. A., Greenberg, P. L., Snyder, P., Klein, T. E., Altman, R. B., Butte, A. J., Ashley, E. A., Gerstein, M., Nadeau, K. C., Tang, H., & Snyder, M.
Affiliations
  • Stanford Medicine (US)
  • Yale University (US)
  • Stanford University (US)
  • Spanish National Cancer Research Centre (ES)
Corresponding author
Snyder, Michael
Venue
Cell
Publisher
Elsevier BV
Published
2012-03
Volume
148
Issue
6
Pages
1293-1307
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2079859830
Cited by
1,291
References
108
Citations by year
2026: 39 · 2025: 44 · 2024: 56 · 2023: 47 · 2022: 89 · 2021: 79 · 2020: 62 · 2019: 84 · 2018: 108 · 2017: 72 · 2016: 100 · 2015: 121 · 2014: 125 · 2013: 169 · 2012: 93
FWCI
65.26
Type
journal article
Language
en
Open access
Locations
  • Cell (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (OD004613)
  • National Heart, Lung, and Blood Institute (K08HL083914, T32HL094274)
  • National Human Genome Research Institute (P50HG002357, U54HG004558, T32HG000044)
  • National Institute of General Medical Sciences (R24GM061374, R24-GM61374, T32GM007205)
  • U.S. National Library of Medicine (T15LM007033)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
Keywords
multi-omics integration, type 2 diabetes risk, RNA editing, autoantibodies, transcriptomics, proteomics, metabolomics, personalized medicine
MeSH terms
Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Mutation, Respiratory Syncytial Viruses, Rhinovirus, Genome, Human, Gene Expression Profiling, Genomics, Proteomics, Metabolomics, Precision Medicine

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