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

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.

Aberrant promoter methylation of the CD4 gene in peripheral blood cells of mastitic dairy cows

“…Therefore, a functional study requires the identification and characterization of the TFs that recruit chromatin modifying enzymes to these binding sites. In humans, over 10% of common SNPs overlap with the differentially methylated region (DMR) between different alleles (Bell et al, 2010). DNA methylation changes can be influenced by genetic differences between animals.…”

Next Generation Sequencing - Advances, Applications and Challenges

“…Haplotype specific methylation (HSM) is a result of the cumulative methylation effect driven by the phase of a number of CpG-SNPs within the haplotype. This signal was strong enough to be identified across the 47kb FTO linkage disequilibrium block [65]. Such a finding is only possible through the integration of DNA methylation data and genome wide association study data.…”

Non-coding genome functions in diabetes

“…Although few studies have addressed this issue, mounting evidence now links DNA sequence variation and DNA methylation, and it is possible that methylation changes act together with particular genetic traits to confer higher disease susceptibility (Bell et al 2010, Dayeh et al 2013, Petersen et al 2014, Orozco et al 2015.…”