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Review

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

1
Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
2
Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
3
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
4
Department of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil
5
Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
6
Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden
7
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA
8
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this manuscript.
Metabolites 2019, 9(6), 117; https://doi.org/10.3390/metabo9060117
Received: 8 May 2019 / Revised: 12 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
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. View Full-Text
Keywords: multi-omic integration; systems biology; epidemiology; study design; integrative analysis multi-omic integration; systems biology; epidemiology; study design; integrative analysis
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MDPI and ACS Style

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.; on behalf of the Consortium of Metabolomics Studies Statistics Working Group. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites 2019, 9, 117. https://doi.org/10.3390/metabo9060117

AMA Style

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, on behalf of the Consortium of Metabolomics Studies Statistics Working Group. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites. 2019; 9(6):117. https://doi.org/10.3390/metabo9060117

Chicago/Turabian Style

Chu, Su H., 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, and on behalf of the Consortium of Metabolomics Studies Statistics Working Group. 2019. "Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective" Metabolites 9, no. 6: 117. https://doi.org/10.3390/metabo9060117

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