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Open Access Highly Accessed Research article

Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects

Kristel R van Eijk12, Simone de Jong5, Marco PM Boks2, Terry Langeveld1, Fabrice Colas3, Jan H Veldink4, Carolien GF de Kovel1, Esther Janson1, Eric Strengman15, Peter Langfelder6, René S Kahn2, Leonard H van den Berg4, Steve Horvath67 and Roel A Ophoff256*

Author Affiliations

1 Department of Medical Genetics, University Medical Center Utrecht, Utrecht, 3584, CG, The Netherlands

2 Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, 3508, GA, The Netherlands

3 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, 2300, RC, The Netherlands

4 Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, 3508, GA, The Netherlands

5 Center for Neurobehavioral Genetics, University of California Los Angeles, Box 951761 Gonda #4357C, 695 Charles E. Young Drive, South Los Angeles, CA 90095-1761, USA

6 Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA

7 Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA, 90095, USA

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BMC Genomics 2012, 13:636  doi:10.1186/1471-2164-13-636

Published: 17 November 2012

Abstract

Background

The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex.

Results

Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels.

Conclusions

Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated.

Keywords:
DNA methylation; Gene expression; Association; Epigenetics; WGCNA