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Cited by 256

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

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

Open access
Bell, Christopher G., Finer, Sarah, Lindgren, Cecilia M., Wilson, Gareth A., Rakyan, Vardhman K., Teschendorff, Andrew E., Akan, Pelin, Stupka, Elia, Down, Thomas A., Prokopenko, Inga, Morison, Ian M., Mill, Jonathan, Pidsley, Ruth, International Type 2 Diabetes 1q Consortium, Deloukas, Panos, Frayling, Timothy M., Hattersley, Andrew T., McCarthy, Mark I., Beck, Stephan, Hitman, Graham A.·2010·PLoS ONE
DOI 10.1371/journal.pone.0014040
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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.

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Metadata

Authors
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., International Type 2 Diabetes 1q Consortium, Deloukas, P., Frayling, T. M., Hattersley, A. T., McCarthy, M. I., Beck, S., & Hitman, G. A.
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2010-11-18
Volume
5
Issue
11
Pages
e14040
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0014040
OpenAlex
W2138557828
Cited by
256
References
21
FWCI
10.40
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus. https://app.cochat.ai/papers/10.1371/journal.pone.0014040
The paper
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., International Type 2 Diabetes 1q Consortium, 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. PLoS ONE. https://doi.org/10.1371/journal.pone.0014040

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.