Cargando…

Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity

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 ph...

Descripción completa

Detalles Bibliográficos
Autores principales: Valcárcel, Beatriz, Ebbels, Timothy M. D., Kangas, Antti J., Soininen, Pasi, Elliot, Paul, Ala-Korpela, Mika, Järvelin, Marjo-Riitta, de Iorio, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973353/
https://www.ncbi.nlm.nih.gov/pubmed/24573330
http://dx.doi.org/10.1098/rsif.2013.0908
_version_ 1782309727651233792
author Valcárcel, Beatriz
Ebbels, Timothy M. D.
Kangas, Antti J.
Soininen, Pasi
Elliot, Paul
Ala-Korpela, Mika
Järvelin, Marjo-Riitta
de Iorio, Maria
author_facet Valcárcel, Beatriz
Ebbels, Timothy M. D.
Kangas, Antti J.
Soininen, Pasi
Elliot, Paul
Ala-Korpela, Mika
Järvelin, Marjo-Riitta
de Iorio, Maria
author_sort Valcárcel, Beatriz
collection PubMed
description 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.
format Online
Article
Text
id pubmed-3973353
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-39733532014-05-06 Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity Valcárcel, Beatriz Ebbels, Timothy M. D. Kangas, Antti J. Soininen, Pasi Elliot, Paul Ala-Korpela, Mika Järvelin, Marjo-Riitta de Iorio, Maria J R Soc Interface Research Articles 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. The Royal Society 2014-05-06 /pmc/articles/PMC3973353/ /pubmed/24573330 http://dx.doi.org/10.1098/rsif.2013.0908 Text en http://creativecommons.org/licenses/by/3.0/ © 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.
spellingShingle Research Articles
Valcárcel, Beatriz
Ebbels, Timothy M. D.
Kangas, Antti J.
Soininen, Pasi
Elliot, Paul
Ala-Korpela, Mika
Järvelin, Marjo-Riitta
de Iorio, Maria
Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
title Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
title_full Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
title_fullStr Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
title_full_unstemmed Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
title_short Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
title_sort genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3973353/
https://www.ncbi.nlm.nih.gov/pubmed/24573330
http://dx.doi.org/10.1098/rsif.2013.0908
work_keys_str_mv AT valcarcelbeatriz genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT ebbelstimothymd genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT kangasanttij genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT soininenpasi genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT elliotpaul genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT alakorpelamika genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT jarvelinmarjoriitta genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity
AT deioriomaria genomemetabolomeintegratednetworkanalysistouncoverconnectionsbetweengeneticvariantsandcomplextraitsanapplicationtoobesity