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

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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., Hariharan, Manoj, Dewey, Frederick E., Cheng, Yong, Clark, Michael J., Im, Hogune, Habegger, Lukas, Balasubramanian, Suganthi, O'Huallachain, Maeve, Dudley, Joel T., Hillenmeyer, Sara, Haraksingh, Rajini, Sharon, Donald, Euskirchen, Ghia, Lacroute, Phil, Bettinger, Keith, Boyle, Alan P., Kasowski, Maya, Grubert, Fabian, Seki, Scott, Garcia, Marco, Whirl-Carrillo, Michelle, Gallardo, Mercedes, Blasco, Maria A., Greenberg, Peter L., Snyder, Phyllis, Klein, Teri E., Altman, Russ B., Butte, Atul J., Ashley, Euan A., Gerstein, Mark, Nadeau, Kari C., Tang, Hua, Snyder, Michael·2012·Cell

DOI 10.1016/j.cell.2012.02.009PMC PMC3341616

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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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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
ORCID: Chen, Rui
Mias, George I.
Jiang, Lihua
Lam, Hugo Y.K.
Chen, Rong
Karczewski, Konrad J.
Hariharan, Manoj
Cheng, Yong
Im, Hogune
Balasubramanian, Suganthi
Dudley, Joel T.
Haraksingh, Rajini
Boyle, Alan P.
Kasowski, Maya
Seki, Scott
Greenberg, Peter L.
Klein, Teri E.
Altman, Russ B.
Butte, Atul J.
Ashley, Euan A.
Gerstein, Mark
Nadeau, Kari C.
Tang, Hua
Snyder, Michael
Venue: Cell
Publisher: Elsevier BV
Published: 2012-03
Volume: 148
Issue: 6
Pages: 1293-1307
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2012.02.009
PMC: PMC3341616
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: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 503 d)
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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