Valcárcel, B;
Ebbels, TM;
Kangas, AJ;
Soininen, P;
Elliot, P;
Ala-Korpela, M;
Järvelin, MR;
(2014)
Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity.
J R Soc Interface
, 11
(94)
, Article 20130908 . 10.1098/rsif.2013.0908.
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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.
| Type: | Article |
|---|---|
| Title: | Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity. |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsif.2013.0908 |
| Publisher version: | http://dx.doi.org/20130908 |
| Language: | English |
| Additional information: | © 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. |
| Keywords: | GEMINi, correlation analysis, differential networks, genome-wide association analysis, metabolomics, Adolescent, Adult, Female, Gene Regulatory Networks, Genome, Human, Genome-Wide Association Study, Humans, Infant, Male, Metabolome, Obesity, Quantitative Trait Loci |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1450555 |
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