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; Panos Deloukas; Timothy M. Frayling; Andrew T. Hattersley; Mark I. McCarthy; Stephan Beck; Graham A. Hitman

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, Panos Deloukas, Timothy M. Frayling, Andrew T. Hattersley, Mark I. McCarthy, Stephan Beck, Graham A. Hitman

Social Genetic & Developmental Psychiatry

Research output: Contribution to journal › Article › peer-review

210 Citations (Scopus)

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 x 10(-4), permutation p = 1.0 x 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 x 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.

Original language	English
Article number	e14040
Journal	PL o S One
Volume	5
Issue number	11
Publication status	Published - 2010

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Bell, C. G., Finer, S., Lindgren, C. M., Wilson, G. A., Rakyan, V. K., Teschendorff, A. E., Akan, P., Stupka, E., Down, T. A., Prokopenko, I., Morison, I. M., Mill, J., Pidsley, R., Deloukas, P., Frayling, T. M., Hattersley, A. T., McCarthy, M. I., Beck, S., & Hitman, G. A. (2010). Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus. PL o S One , 5(11), Article e14040.

@article{7b4d4aecd1a54c4c877c170493aac90c,

title = "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 x 10(-4), permutation p = 1.0 x 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 x 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.",

author = "Bell, {Christopher G.} and Sarah Finer and Lindgren, {Cecilia M.} and Wilson, {Gareth A.} and Rakyan, {Vardhman K.} and Teschendorff, {Andrew E.} and Pelin Akan and Elia Stupka and Down, {Thomas A.} and Inga Prokopenko and Morison, {Ian M.} and Jonathan Mill and Ruth Pidsley and Panos Deloukas and Frayling, {Timothy M.} and Hattersley, {Andrew T.} and McCarthy, {Mark I.} and Stephan Beck and Hitman, {Graham A.}",

year = "2010",

language = "English",

volume = "5",

journal = "PL o S One ",

issn = "1932-6203",

publisher = "Public Library of Science",

number = "11",

}

Bell, CG, Finer, S, Lindgren, CM, Wilson, GA, Rakyan, VK, Teschendorff, AE, Akan, P, Stupka, E, Down, TA, Prokopenko, I, Morison, IM, Mill, J, Pidsley, R, Deloukas, P, Frayling, TM, Hattersley, AT, McCarthy, MI, Beck, S & Hitman, GA 2010, 'Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus', PL o S One , vol. 5, no. 11, e14040.

TY - JOUR

T1 - Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus

AU - Bell, Christopher G.

AU - Finer, Sarah

AU - Lindgren, Cecilia M.

AU - Wilson, Gareth A.

AU - Rakyan, Vardhman K.

AU - Teschendorff, Andrew E.

AU - Akan, Pelin

AU - Stupka, Elia

AU - Down, Thomas A.

AU - Prokopenko, Inga

AU - Morison, Ian M.

AU - Mill, Jonathan

AU - Pidsley, Ruth

AU - Deloukas, Panos

AU - Frayling, Timothy M.

AU - Hattersley, Andrew T.

AU - McCarthy, Mark I.

AU - Beck, Stephan

AU - Hitman, Graham A.

PY - 2010

Y1 - 2010

N2 - 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 x 10(-4), permutation p = 1.0 x 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 x 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.

AB - 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 x 10(-4), permutation p = 1.0 x 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 x 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.

M3 - Article

SN - 1932-6203

VL - 5

JO - PL o S One

JF - PL o S One

IS - 11

M1 - e14040

ER -

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

Abstract

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