  Introduction: To leverage functionality and clinical relevance into
understanding systems biology, one needs to understand the pathway of the
genetic effects on risk factors/disease through intermediate molecular levels,
such as metabolomics. Systems approaches integrate multi-omic information to
find pathways to disease endpoints and make optimal inference decisions.
Method: Here, we introduce a multi-stage approach to integrate causal networks
in observational studies and GWAS to facilitate mechanistic understanding
through identification of pathways from the genome to risk factors/disease via
metabolomics. The pathways in causal networks reveal the underlying
relationships behind observations, which do not play a significant role in more
traditional correlative analyses, where one variable at a time is considered.
Results: We identified a causal network over the metabolomic level using the
genome directed acyclic graph (G-DAG), to systematically assess whether
variations in the genome lead to variations in triglyceride levels as a risk
factor of cardiovascular disease. We found LRRC46 and LRRC69 harboring
loss-of-function mutations have significant effect on two metabolites with
direct effects on triglyceride levels. We also found pathways of FAM198B and
C6orf25 to triglycerides through indirect paths from metabolites. Conclusion:
Integrating causal networks with GWAS facilitates mechanistic understanding in
comparison to one-variable-at-a-time approaches due to accounting for
relationships among components at intermediate molecular levels. This approach
is complementary to experimental studies to identify efficacious targets in the
age of big data sets.
