Network based conditional genome wide association analysis of human metabolomics
BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ( omics) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.\n\nResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.\n\nConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific contra-intuitive pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.\n\nShort abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.