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Abstract:
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In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and the corresponding genes from high-dimensional DNA methylation data. However, most of existing methods are not able to utilize genetic networks although methylation levels among linked genes in the networks tend to be highly correlated with each other. In this article, we propose new approach that combines independent component analysis with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. The proposed approach first captures gene-level signals from multiple CpG sites using independent component analysis and then regularizes them to perform gene selection according to given biological network information.
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