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Sparse profiling of CpG methylation in blood by microarrays has identified epigenetic links to common diseases. Here we apply methylC-capture sequencing (MCC-Seq) in a clinical population of ~200 adipose tissue and matched blood samples (Ntotal~400), providing high-resolution methylation profiling (>1.3 M CpGs) at regulatory elements. We link methylation to cardiometabolic risk through associations to circulating plasma lipid levels and identify lipid-associated CpGs with unique localization patterns in regulatory elements. We show distinct features of tissue-specific versus tissue-independent lipid-linked regulatory regions by contrasting with parallel assessments in ~800 independent adipose tissue and blood samples from the general population. We follow-up on adipose-specific regulatory regions under (1) genetic and (2) epigenetic (environmental) regulation via integrational studies. Overall, the comprehensive sequencing of regulatory element methylomes reveals a rich landscape of functional variants linked genetically as well as epigenetically to plasma lipid traits.

Most genome-wide methylation studies (EWAS) of multifactorial disease traits use targetedarrays or enrichment methodologies preferentially covering CpG-dense regions, tocharacterize sufficiently large samples. To overcome this limitation, we present here a newcustomizable, cost-effective approach, methylC-capture sequencing (MCC-Seq), forsequencing functional methylomes, while simultaneously providing genetic variationinformation. To illustrate MCC-Seq, we use whole-genome bisulfite sequencing on adiposetissue (AT) samples and public databases to design AT-specific panels. We establish itsefficiency for high-density interrogation of methylome variability by systematic comparisonswith other approaches and demonstrate its applicability by identifying novel methylationvariation within enhancers strongly correlated to plasma triglyceride and HDL-cholesterol,including atCD36. Our more comprehensive AT panel assesses tissue methylation andgenotypes in parallel atB4 andB3 M sites, respectively. Our study demonstrates thatMCC-Seq provides comparable accuracy to alternative approaches but enables more efficientcataloguing of functional and disease-relevant epigenetic and genetic variants for large-scale EWAS.

Cluster of differentiation 36 (CD36) variants influence fasting lipids and risk of metabolic syndrome, but their impact on postprandial lipids, an independent risk factor for cardiovascular disease, is unclear. We determined the effects of SNPs within a approximate to 410 kb region encompassing CD36 and its proximal and distal promoters on chylomicron (CM) remnants and LDL particles at fasting and at 3.5 and 6 h following a high-fat meal (Genetics of Lipid Lowering Drugs and Diet Network study, n = 1,117). Five promoter variants associated with CMs, four with delayed TG clearance and five with LDL particle number. To assess mechanisms underlying the associations, we queried expression quantitative trait loci, DNA methylation, and ChIP-seq datasets for adipose and heart tissues that function in postprandial lipid clearance. Several SNPs that associated with higher serum lipids correlated with lower adipose and heart CD36 mRNA and aligned to active motifs for PPAR, a major CD36 regulator. The SNPs also associated with DNA methylation sites that related to reduced CD36 mRNA and higher serum lipids, but mixed-model analyses indicated that the SNPs and methylation independently influence CD36 mRNA. The findings support contributions of CD36 SNPs that reduce adipose and heart CD36 RNA expression to inter-individual variability of postprandial lipid metabolism and document changes in CD36 DNA methylation that influence both CD36 expression and lipids.