Future impact of integrated high-throughput methylome analyses on human health and disease

Lee M Butcher; Stephan Beck

doi:10.1016/S1673-8527(08)60057-0

Volume 35 Issue 7

Jul. 2008

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Abstract

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Article Navigation > Journal of Genetics and Genomics > 2008 > 35(7): 391-401

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Future impact of integrated high-throughput methylome analyses on human health and disease

doi: 10.1016/S1673-8527(08)60057-0

UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK

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Corresponding author: E-mail address: l.butcher@ucl.ac.uk (Lee M Butcher)
Received Date: 2008-04-04
Accepted Date: 2008-06-09
Rev Recd Date: 2008-06-08

Available Online: 2008-07-18

Publish Date: 2008-07-20

Abstract

Abstract

A spate of high-powered genome-wide association studies (GWAS) have recently identified numerous single-nucleotide polymorphisms (SNPs) robustly linked with complex disease. Despite interrogating the majority of common human variation, these SNPs only account for a small proportion of the phenotypic variance, which suggests genetic factors are acting in concert with non-genetic factors. Although environmental measures are logical covariants for genotype-phenotype investigations, another non-genetic intermediary exists: epigenetics. Epigenetics is the analysis of somatically-acquired and, in some cases, transgenerationally inherited epigenetic modifications that regulate gene expression, and offers to bridge the gap between genetics and environment to understand phenotype. The most widely studied epigenetic mark is DNA methylation. Aberrant methylation at gene promoters is strongly implicated in disease etiology, most notably cancer. This review will highlight the importance of DNA methylation as an epigenetic regulator, outline techniques to characterize the DNA methylome and present the idea of reverse phenotyping, where multiple layers of analysis are integrated at the individual level to create personalized digital phenotypes and, at a phenotype level, to identify novel molecular signatures of disease.
- DNA methylation,
- reverse phenotyping,
- complex disease

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References

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