Competitive pathway analysis using Structural Equation Models (CPA-SEM) for gene expression data

There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.