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Chu SH, Huang M, Kelly RS, et al. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites. 2019;9(6)doi: 10.3390/metabo9060117.
Chu, S. H., Huang, M., Kelly, R. S., Benedetti, E., Siddiqui, J. K., Zeleznik, O. A., Pereira, A., Herrington, D., Wheelock, C. E., Krumsiek, J., McGeachie, M., Moore, S. C., Kraft, P., Mathé, E., Lasky-Su, J. (2019). Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites, 9(6), . https://doi.org/10.3390/metabo9060117
Chu, Su H, et al. "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective." Metabolites vol. 9,6 (2019). doi: https://doi.org/10.3390/metabo9060117
Chu SH, Huang M, Kelly RS, Benedetti E, Siddiqui JK, Zeleznik OA, Pereira A, Herrington D, Wheelock CE, Krumsiek J, McGeachie M, Moore SC, Kraft P, Mathé E, Lasky-Su J. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites. 2019 Jun 18;9(6). doi: 10.3390/metabo9060117. PMID: 31216675; PMCID: PMC6630728.
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Metabolites. 2019 Jun 18;9(6). doi: 10.3390/metabo9060117.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.

Metabolites

Su H Chu, Mengna Huang, Rachel S Kelly, Elisa Benedetti, Jalal K Siddiqui, Oana A Zeleznik, Alexandre Pereira, David Herrington, Craig E Wheelock, Jan Krumsiek, Michael McGeachie, Steven C Moore, Peter Kraft, Ewy Mathé, Jessica Lasky-Su,

Affiliations

  1. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. [email protected].
  2. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. [email protected].
  3. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. [email protected].
  4. Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA. [email protected].
  5. Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA. [email protected].
  6. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. [email protected].
  7. Department of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil. [email protected].
  8. Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA. [email protected].
  9. Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden. [email protected].
  10. Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA. [email protected].
  11. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. [email protected].
  12. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA. [email protected].
  13. Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. [email protected].
  14. Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA. [email protected].
  15. Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. [email protected].

PMID: 31216675 PMCID: PMC6630728 DOI: 10.3390/metabo9060117

Abstract

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Keywords: epidemiology; integrative analysis; multi-omic integration; study design; systems biology

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