Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus

Christopher G Bell, Sarah Finer, Cecilia M Lindgren, Gareth A Wilson, Vardhman K Rakyan, Andrew E Teschendorff, Pelin Akan, Elia Stupka, Thomas A Down, Inga Prokopenko, Ian M Morison, Jonathan Mill, Ruth Pidsley, International Type 2 Diabetes 1q Consortium, Panos Deloukas, Timothy M Frayling, Andrew T Hattersley, Mark I McCarthy, Stephan Beck and Graham A Hitman

PLOS ONE, 2010, vol. 5, issue 11, 1-12

Abstract: Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10−4, permutation p = 1.0×10−3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10−7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within disease-associated loci, thus providing a novel route to aid unravelling common complex diseases.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0014040

DOI: 10.1371/journal.pone.0014040

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