Epigenome-wide association study of fasting blood lipids in the Genetics of Lipid-lowering Drugs and Diet Network study.

Background: Genetic research regarding blood lipids has largely focused on DNA sequence variation; few studies have explored epigenetic effects. Genome-wide surveys of DNA methylation may uncover epigenetic factors influencing lipid metabolism.

Methods And Results: To identify whether differential methylation of cytosine-(phosphate)-guanine dinucleotides (CpGs) correlated with lipid phenotypes, we isolated DNA from CD4+ T cells and quantified the proportion of sample methylation at >450 000 CpGs by using the Illumina Infinium HumanMethylation450 Beadchip in 991 participants of the Genetics of Lipid Lowering Drugs and Diet Network. We modeled the percentage of methylation at individual CpGs as a function of fasting very-low-density lipoprotein cholesterol and triglycerides (TGs) by using mixed linear regression adjusted for age, sex, study site, cell purity, and family structure. Four CpGs (cg00574958, cg17058475, cg01082498, and cg09737197) in intron 1 of carnitine palmitoyltransferase 1A (CPT1A) were strongly associated with very-low low-density lipoprotein cholesterol (P=1.8×10(-21) to 1.6×10(-8)) and TG (P=1.6×10(-26) to 1.5×10(-9)). Array findings were validated by bisulfite sequencing. We performed quantitative polymerase chain reaction experiments demonstrating that methylation of the top CpG (cg00574958) was correlated with CPT1A expression. The association of cg00574958 with TG and CPT1A expression were replicated in the Framingham Heart Study (P=4.1×10(-14) and 3.1×10(-13), respectively). DNA methylation at CPT1A cg00574958 explained 11.6% and 5.5% of the variation in TG in the discovery and replication cohorts, respectively.

Conclusions: This genome-wide epigenomic study identified CPT1A methylation as strongly and robustly associated with fasting very-low low-density lipoprotein cholesterol and TG. Identifying novel epigenetic contributions to lipid traits may inform future efforts to identify new treatment targets and biomarkers of disease risk.