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Epigenetics meets metabolomics: An epigenome-wide association study with blood serum metabolic traits.

Hum. Mol. Genet. 23, 534-545 (2014)
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Previously, we reported strong influences of genetic variants on metabolic phenotypes, some of them with clinical relevance. Here we hypothesize that DNA methylation may have an important and potentially independent effect on human metabolism. To test this hypothesis we conducted what is to the best of our knowledge the first epigenome-wide association study (EWAS) between DNA methylation and metabolic traits (metabotypes) in human blood. We assess 649 blood metabolic traits from 1,814 participants of the KORA population study for association with methylation of 457,004 CpG sites, determined on the Infinium HumanMethylation450 BeadChip platform. Using the EWAS approach, we identified two types of methylome-metabotype associations. One type is driven by an underlying genetic effect; the other type is independent of genetic variation and potentially driven by common environmental and life-style dependent factors. We report eight CpG loci at genome-wide significance that have a genetic variant as confounder (p=3.9x10-20 to 2.0x10-108, r2=0.036 to 0.221). Seven loci display CpG-site-specific associations to metabotypes, but do not exhibit any underlying genetic signals (p=9.2x10-14 to 2.7x10-27, r2=0.008 to 0.107). We further identify several groups of CpG loci that associate with a same metabotype, such as 4-vinylphenol sulfate and 4-androsten-3beta,17beta-diol disulfate. In these cases the association between CpG-methylation and metabotype are likely the result of a common external environmental factor, including smoking. Our study shows that analysis of EWAS with large numbers of metabolic traits in large population cohorts are, in principle, feasible. Taken together, our data suggests that DNA methylation plays an important role in regulating human metabolism.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Dna Methylation Patterns; Quantile Normalization; Mass-spectrometry; Genetic-variation; Subset-quantile; Breast-cancer; Phenotypes; Estrogen; Pipeline; Disease
Reviewing status