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

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

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 revi...

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Main Authors: 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, on behalf of the Consortium of Metabolomics Studies Statistics Working Group
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Metabolites
Subjects:
multi-omic integration
systems biology
epidemiology
study design
integrative analysis
Online Access:https://www.mdpi.com/2218-1989/9/6/117
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spelling doaj-539774cf5d42486582f1d7643ee090e92020-11-25T02:40:25ZengMDPI AGMetabolites2218-19892019-06-019611710.3390/metabo9060117metabo9060117Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological PerspectiveSu H. Chu0Mengna Huang1Rachel S. Kelly2Elisa Benedetti3Jalal K. Siddiqui4Oana A. Zeleznik5Alexandre Pereira6David Herrington7Craig E. Wheelock8Jan Krumsiek9Michael McGeachie10Steven C. Moore11Peter Kraft12Ewy Mathé13Jessica Lasky-Su14on behalf of the Consortium of Metabolomics Studies Statistics Working GroupChanning Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USAChanning Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USAChanning Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USAInstitute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USADepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USAChanning Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USADepartment of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, BrazilDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USADivision of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, SwedenInstitute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USAChanning Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USADivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USADepartment of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USADepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USAChanning Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USAIt 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.https://www.mdpi.com/2218-1989/9/6/117multi-omic integrationsystems biologyepidemiologystudy designintegrative analysis
collection DOAJ
language English
format Article
sources DOAJ
author 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
on behalf of the Consortium of Metabolomics Studies Statistics Working Group
spellingShingle 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
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
multi-omic integration
systems biology
epidemiology
study design
integrative analysis
author_facet 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
on behalf of the Consortium of Metabolomics Studies Statistics Working Group
author_sort Su H. Chu
title Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective
title_short Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective
title_full Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective
title_fullStr Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective
title_full_unstemmed Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective
title_sort integration of metabolomic and other omics data in population-based study designs: an epidemiological perspective
publisher MDPI AG
series Metabolites
issn 2218-1989
publishDate 2019-06-01
description 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.
topic multi-omic integration
systems biology
epidemiology
study design
integrative analysis
url https://www.mdpi.com/2218-1989/9/6/117
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