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Background: Many genome-wide association studies have detected genomicregions associated with traits, yet understanding the functional causes ofassociation often remains elusive. Utilizing systems approaches and focusing onintermediate molecular phenotypes might facilitate biologic understanding.Results: The availability of exome sequencing of two populations ofAfrican-Americans and European-Americans from the Atherosclerosis Risk inCommunities study allowed us to investigate the effects of annotatedloss-of-function (LoF) mutations on 122 serum metabolites. To assess thefindings, we built metabolomic causal networks for each population separatelyand utilized structural equation modeling. We then validated our findings witha set of independent samples. By use of methods based on concepts of Mendelianrandomization of genetic variants, we showed that some of the affectedmetabolites are risk predictors in the causal pathway of disease. For example,LoF mutations in the gene KIAA1755 were identified to elevate the levels ofeicosapentaenoate (p-value=5E-14), an essential fatty acid clinicallyidentified to increase essential hypertension. We showed that this gene is inthe pathway to triglycerides, where both triglycerides and essentialhypertension are risk factors of metabolomic disorder and heart attack. We alsoidentified that the gene CLDN17, harboring loss-of-function mutations, hadpleiotropic actions on metabolites from amino acid and lipid pathways.Conclusion: Using systems biology approaches for the analysis of metabolomicsand genetic data, we integrated several biological processes, which lead tofindings that may functionally connect genetic variants with complex diseases.

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics