Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
Author(s)
Type
Journal Article
Abstract
Current studies of phenotype diversity by genome-wide association studies
(GWAS) are mainly focused on identifying genetic variants that influence
level changes of individual traits without considering additional alterations at
the system-level. However, in addition to level alterations of single phenotypes,
differences in association between phenotype levels are observed across different
physiological states. Such differences in molecular correlations between
states can potentially reveal information about the system state beyond that
reported by changes in mean levels alone. In this study, we describe a novel
methodological approach, which we refer to as genome metabolome integrated
network analysis (GEMINi) consisting of a combination of correlation network
analysis and genome-wide correlation study. The proposed methodology
exploits differences in molecular associations to uncover genetic variants
involved in phenotype variation. We test the performance of the GEMINi
approach in a simulation study and illustrate its use in the context of obesity and
detailed quantitative metabolomics data on systemic metabolism. Application
of GEMINi revealed a set of metabolic associations which differ between
normal and obese individuals. While no significant associations were found
between genetic variants and body mass index using a standard GWAS
approach, further investigation of the identified differences in metabolic association
revealed a number of loci, several of which have been previously
implicated with obesity-related processes. This study highlights the advantage
of using molecular associations as an alternative phenotype when studying the
genetic basis of complex traits and diseases
(GWAS) are mainly focused on identifying genetic variants that influence
level changes of individual traits without considering additional alterations at
the system-level. However, in addition to level alterations of single phenotypes,
differences in association between phenotype levels are observed across different
physiological states. Such differences in molecular correlations between
states can potentially reveal information about the system state beyond that
reported by changes in mean levels alone. In this study, we describe a novel
methodological approach, which we refer to as genome metabolome integrated
network analysis (GEMINi) consisting of a combination of correlation network
analysis and genome-wide correlation study. The proposed methodology
exploits differences in molecular associations to uncover genetic variants
involved in phenotype variation. We test the performance of the GEMINi
approach in a simulation study and illustrate its use in the context of obesity and
detailed quantitative metabolomics data on systemic metabolism. Application
of GEMINi revealed a set of metabolic associations which differ between
normal and obese individuals. While no significant associations were found
between genetic variants and body mass index using a standard GWAS
approach, further investigation of the identified differences in metabolic association
revealed a number of loci, several of which have been previously
implicated with obesity-related processes. This study highlights the advantage
of using molecular associations as an alternative phenotype when studying the
genetic basis of complex traits and diseases
Date Issued
2014-05-06
Date Acceptance
2014-02-05
Citation
Journal of the Royal Society Interface, 2014, 11 (94)
ISSN
1742-5689
Publisher
Royal Society, The
Journal / Book Title
Journal of the Royal Society Interface
Volume
11
Issue
94
Copyright Statement
© 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original
author and source are credited
License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original
author and source are credited
License URL
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MULTIDISCIPLINARY SCIENCES
correlation analysis
differential networks
genome-wide association analysis
metabolomics
GEMINi
DENSITY-LIPOPROTEIN CHOLESTEROL
CHANARIN-DORFMAN-SYNDROME
BODY-MASS INDEX
WIDE ASSOCIATION
LOCI
POPULATION
LIPOLYSIS
DISEASES
PROFILE
CGI-58
Publication Status
Published
Article Number
20130908