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Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes
Open accessAbstract
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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“However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Chen et al., 2012
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“none of these approaches are truly integrative on the population scale and compare the predictive nature and value of the various data types in the context of biomedicine.”
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Metadata
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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