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

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; Consortium of Metabolomics Studies Statistics Working Group

doi:10.3390/metabo9060117

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

Metabolites. 2019 Jun 18;9(6):117. doi: 10.3390/metabo9060117.

Authors

Su H Chu¹, Mengna Huang², Rachel S Kelly³, Elisa Benedetti⁴, Jalal K Siddiqui⁵, Oana A Zeleznik⁶, Alexandre Pereira⁷, David Herrington⁸, Craig E Wheelock⁹, Jan Krumsiek¹⁰, Michael McGeachie¹¹, Steven C Moore¹², Peter Kraft¹³, Ewy Mathé¹⁴, Jessica Lasky-Su¹⁵; Consortium of Metabolomics Studies Statistics Working Group

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].

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.

Publication types

Review

Abstract

Publication types

Grants and funding