Recent approaches to understanding the biological underpinnings of risk factors and disease have focused on describing the role of genetic variation, with the advent of large-scale genome-wide association studies (GWAS). These approaches have been successful as independent signals have been identified at genome-wide significance for many complex traits, although we are only starting to understand the mechanisms by which GWAS variants influence phenotype. Many of the variants described are hypothesized to influence gene regulation. DNA methylation is the best-characterized epigenetic modification, stably influencing gene expression. It has previously been demonstrated that DNA methylation is under local genetic control, with enrichment of DNA methylation quantitative trait loci (mQTL) identified amongst genomic regions associated with several complex diseases. We have undertaken a genetic study of DNA methylation using data from the Illumina EPIC array and matched GWAS data from the same individuals. We also highlight the potential for using polygenic risk scores (PRS) as disease biomarkers, demonstrating their utility for exploring the molecular genomic mechanisms involved in disease pathogenesis. PRS-associated epigenetic variation is potentially less affected by reverse causation (e.g., medication exposure, stress, and smoking), which can confound epigenome-wide association study (EWAS) analyses of disease traits. Leveraging information from genetic data and using a previously described paradigm, we will describe an integrated genetic-epigenetic study to further our functional understanding of common variants associated with biological pathways and the aetiology of disease.