Weighted gene co-expression network analysis (WGCNA)

WGCNA⁶² is a systems biology method that can be used to find modules (clusters) with highly correlated methylation levels and to relate modules to clinical traits. We applied the WGCNA R package⁶³ on 491,174 CpGs to identify modules significantly associated with AS/PH traits. First, an unsigned co-methylation network was constructed by using blockwiseModules function (soft thresholding power = 6, merge cut height = 0.25, and minimum module size = 30). 41 modules were identified from the WGCNA network. DNAm levels of CpGs within a module were summarized by the module eigengene (ME) value which represents the overall methylation level of CpGs clustering in a module. Next, the linear regression model adjusted for covariates was performed between ME value and AS/PH traits for each module to identify significantly associated modules with AS/PH traits. The covariates were the same as EWAS analysis. We considered the module with association p-value < 0.05 as a significant module. Finally, we used CpGs in the significant modules to carry out gene set enrichment analysis.