Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data

Abstract

BackgroundIn human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other.ResultsWe propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project.ConclusionsThe proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-3040-x) contains supplementary material, which is available to authorized users.

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

“…Subsequently, Jiao et al proposed the functional epigenetic modules (FEM) method [ 67 ], which revealed HAND2 as a methylation hotspot in the endometrium and indicator of drug response in progesterone treatment [ 69 ]. Kim and Sun [ 71 ] showed that PPI networks are beneficial in network-regularized feature selection after dimension reduction. Li et al used principal component analysis for feature reduction and sparse canonical correlation analysis to infer edge weights for gene pairs.…”

Gene selection by incorporating genetic networks into case-control association studies

“…The most common method to obtain the optimal tuning parameters λ and α is to use k-fold cross-validation. However, the results of selection are not stable due to that the samples are randomly split in the cross-validation (9). Meinshausen et al (18) proposed a stability selection method that used a half-sample approach in combination with selection algorithms.…”

“…Due to the outperformance of the nPC (9) compared with the other PC-based dimension reduction techniques, PC and sPC, we only apply nPC to compare the performance with our proposed methods in real data analyses. We consider all genes according to the USCS (GRCh37/hg19) genome sequence annotation list which can be downloaded from the UCSC website (https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/).…”

“…It has been demonstrated that network-based penalized regression using PC-based dimension reduction techniques can outperform other methods that ignore genetic network structures (9) Simulation studies show that our proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for rheumatoid arthritis patients and normal controls.…”

“…method is only used for case-control study (9). For the continuous phenotypes, we need to switch the logistic model with logistic likelihood to the linear regression model with mean squared error or more robust loss function, such as Huber function (43).…”