Shared genetic control of expression and methylation in peripheral blood

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Abstract

Background

Expression QTLs and epigenetic marks are often employed to provide an insight into the possible biological mechanisms behind GWAS hits. A substantial proportion of the variation in gene expression and DNA methylation is known to be under genetic control. We address the proportion of genetic control that is shared between these two genomic features.

Results

An exhaustive search for pairwise phenotypic correlations between gene expression and DNA methylation in samples from human blood ( n = 610) was performed. Of the 5 × 10 ⁹ possible pairwise tests, 0.36 % passed Bonferroni corrected p-value cutoff of 9.9 × 10 ^-12. We determined that the correlation structure between probe pairs was largely due to blood cell type specificity of the expression and methylation probes. Upon adjustment of the expression and methylation values for observed blood cellular composition ( n = 422), the number of probe pairs which survived Bonferroni correction reduced by more than 5400 fold. Of the 614 correlated probe pairs located on the same chromosome, 75 % share at least one methylation and expression QTL at nominal 10 ^-5 p-value cutoff. Those probe pairs are located within 1Mbp window from each other and have a mean of absolute value of genetic correlation equal to 0.69, further demonstrating the high degree of shared genetic control.

Conclusions

Overall, this study demonstrates notable genetic covariance between DNA methylation and gene expression and reaffirms the importance of correcting for cell-counts in studies on non-homogeneous tissues.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2498-4) contains supplementary material, which is available to authorized users.

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Most cited references 9

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R: A Language and environmental for statistical computing

R Development Core Team, RDC Team, RC Team … (2013)

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A general test of association for quantitative traits in nuclear families.

G Abecasis, L R Cardon, W Cookson (2000)

High-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.

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

Kristel R van Eijk, Simone de Jong, Marco Boks … (2012)

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.

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Author and article information

Contributors

Konstantin Shakhbazov: konstantin.shakhbazov@gmail.com

Joseph E. Powell: joseph.powell@uq.edu.au

Gibran Hemani: gh13047@bristol.ac.uk

Anjali K. Henders: a.henders@uq.edu.au

Nicholas G. Martin: Nick.Martin@qimrberghofer.edu.au

Peter M. Visscher: peter.visscher@uq.edu.au

Grant W. Montgomery: Grant.Montgomery@qimrberghofer.edu.au

Allan F. McRae: a.mcrae@uq.edu.au

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 6 April 2016

Publication date PMC-release: 6 April 2016

Publication date Collection: 2016

Volume: 17

Electronic Location Identifier: 278

Affiliations

[ ]Queensland Brain Institute, University of Queensland, Brisbane, QLD Australia

[ ]The Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD Australia

[ ]QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Brisbane, QLD Australia

[ ]University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD Australia

[ ]Current address: MRC Integrative Epidemiology Unit and School of Social and Community Medicine, University of Bristol, Bristol, BS8 2BN UK

Article

Publisher ID: 2498

DOI: 10.1186/s12864-016-2498-4

PMC ID: 4822256

PubMed ID: 27048375

SO-VID: 60192624-4206-4bbc-bbde-26bca7084d47

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 16 July 2015

Date accepted : 17 February 2016