Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity - Valcarcel,B; Ebbels,TMD; Kangas,AJ; Soininen,P; Elliot,P; Ala-Korpela,M; Jarvelin,M-R; de,Iorio M (Journal of the Royal Society Interface, 2014)
Current studies of phenotype diversity by genome-wide association studies(GWAS) are mainly focused on identifying genetic variants that influencelevel changes of individual traits without considering additional alterations atthe system-level. However, in addition to level alterations of single phenotypes,differences in association between phenotype levels are observed across differentphysiological states. Such differences in molecular correlations betweenstates can potentially reveal information about the system state beyond thatreported by changes in mean levels alone. In this study, we describe a novelmethodological approach, which we refer to as genome metabolome integratednetwork analysis (GEMINi) consisting of a combination of correlation networkanalysis and genome-wide correlation study. The proposed methodologyexploits differences in molecular associations to uncover genetic variantsinvolved in phenotype variation. We test the performance of the GEMINiapproach in a simulation study and illustrate its use in the context of obesity anddetailed quantitative metabolomics data on systemic metabolism. Applicationof GEMINi revealed a set of metabolic associations which differ betweennormal and obese individuals. While no significant associations were foundbetween genetic variants and body mass index using a standard GWASapproach, further investigation of the identified differences in metabolic associationrevealed a number of loci, several of which have been previouslyimplicated with obesity-related processes. This study highlights the advantageof using molecular associations as an alternative phenotype when studying thegenetic basis of complex traits and diseases