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