|
Abstract:
|
The aim of modeling a biological system is to gain sufficient understanding such that the behavior of the system can be predicted. To understand how the components and their interactions give rise to emergent properties of a system, statistical causal networks are employed in a systems biology framework. Knowing the network, we can measure causal effects using structural equation modeling to make strong hypotheses for causal genes and stable pathways from the genome to disease via intermediate levels. We focused on loss-of-function variants across the whole genome, serum metabolites distributed across multiple functional classes, cardiovascular risk factors, and disease. Through, convex-concave rare variants selection (CCRS) method, we hypothesized causal genes. A causal network over the metabolites was identified using the genome directed acyclic graph (G-DAG) algorithm. Knowing the metabolomics network, we measured causal effects using structural equation modeling to assess the hypothesized genes and identify metabolites with direct effects on risk factors/disease. By integrating the results, we identified stable pathways from the genome to risk factors/disease via metabolomics.
|
