Cardiovascular disease (CVD) is of major importance to public health as it accounts for approximately 50% of deaths in Western societies. Risk factors for CVD are influenced by lifestyle factors including smoking, poor diet and physical inactivity, but also demonstrate strong familial tendencies suggesting a genetic component. However, as the susceptibility genes that have been identified to date only partially explain the genetic heritability, epigenetic variation may account for some of the “missing heritability”. This study investigates whether epigenetic modifications, such as DNA methylation, play a role in CVD risk using a well-characterised, homogeneous population from Norfolk Island (NI). The NI community is derived primarily from 18th century English Bounty mutineers and Polynesian women who relocated to NI from Pitcairn in the 1850s. Known CVD risk factors such as blood pressure, body weight, lipid levels and smoking status, have already been characterised from a significant proportion of this population and DNA from blood extracted for genetic studies. In a candidate gene approach we are quantifying DNA methylation levels at regulatory regions of genes, including FTO, MC4R and BDNF for which SNPs have been robustly associated with obesity, to determine whether this modification contributes to relevant risk traits. We are also carrying out a genome-wide DNA methylation scan to identify novel genes or regions where epigenetic variants influence CVD risk traits by interrogating Illumina Human 450K Infinium Methylation Beadchips with bisulfite-treated DNA and correlating methylation levels with our biometric data. Furthermore, phenotypic information and genetic samples were collected in 2000, and again in 2010, enabling us to determine how methylation patterns change with time. Epigenetic information will then be incorporated with our other genomic data for the NI cohort, providing a unique opportunity to undertake an integrative genomic profiling approach to identify the various interacting components that influence CVD risk traits.