Mining the Selective Remodeling of DNA Methylation in Promoter Regions to Identify Robust Gene-Level Associations with Phenotype

Epigenetics is an essential biological frontier linking genetics to the environment, where DNA methylation is one of the most studied epigenetic events. In recent years, through the epigenome-wide association study (EWAS), researchers have identified thousands of phenotype-related methylation sites. However, the overlap between identified phenotype-related DNA methylation sites are often quite small, and it might clue to methylation remodeling has a certain degree of randomness within the genome. Thus, the identification of robust gene-phenotype associations is crucial for interpreting pathogenesis. How to integrate the methylation values of different sites on the same gene and to mining the DNA methylation at the gene level remains a challenge. A recent study found that the DNA methylation difference of the gene body and promoter region has a strong correlation with gene expression. In this study, we proposed a Statistical difference of DNA Methylation between Promoter and Other Body Region (SIMPO) algorithm to extract DNA methylation values at the gene level. First, by choosing to smoke as an environmental exposure factor, our method led to significant improvements in gene overlaps (from 5% to 17%) between different datasets. In addition, the biological significance of these genes ([~]23%) are significantly better than those identified by traditional probe-based methods ([~]18%, P-value = 5.18e-03). Then, we selected two disease content (e.g., insulin resistance and Parkinsons disease) to show that the biological efficiency of disease-related gene identification increased from 15.43% to 44.44% (P-value = 1.20e-28). Thus, our results declare that mining the selective remodeling of DNA methylation in promoter regions can identify robust gene-level associations with phenotype, and the characteristic remodeling of a given genes promoter region can reflect the essence of disease.