Network-based Regularization for Matched Case-Control Analysis of High-dimensional DNA Methylation Data - Sun, Hokeun (2012)
The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier article, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this article, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping-effect of 1) linked CpG sites within a gene or 2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. The proposed method was applied to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients were investigated using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper.