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. 2011 Jul;6(7):908-19.
doi: 10.4161/epi.6.7.16431. Epub 2011 Jul 1.

The influence of aging, environmental exposures and local sequence features on the variation of DNA methylation in blood

Scott M Langevin  1 , E Andres Houseman, Brock C Christensen, John K Wiencke, Heather H Nelson, Margaret R Karagas, Carmen J Marsit, Karl T Kelsey
Affiliations

The influence of aging, environmental exposures and local sequence features on the variation of DNA methylation in blood

Scott M Langevin et al. Epigenetics. 2011 Jul.

Erratum in

  • Epigenetics. 2012 Jun 1;7(6):668

Abstract

In order to properly comprehend the epigenetic dysregulation that occurs during the course of disease, there is a need to characterize the epigenetic variability in healthy individuals that arises in response to aging and exposures, and to understand such variation within the biological context of the DNA sequence. We analyzed the methylation of 26,486 autosomal CpG loci in blood from 205 healthy subjects, using three complementary approaches to assess the association between methylation, age or exposures, and local sequence features, such as CpG island status, repeat sequences, location within a polycomb target gene or proximity to a transcription factor binding site. We clustered CpGs (1) using unsupervised recursively partitioned mixture modeling (RPMM) and (2) bioinformatically-informed methods, and (3) also employed a marginal model-based (non-clustering) approach. We observed associations between age and methylation and hair dye use and methylation, where the direction and magnitude was contingent on the local sequence features of the CpGs. Our results demonstrate that CpGs are differentially methylated dependent upon the genomic features of the sequence in which they are embedded, and that CpG methylation is associated with age and hair dye use in a CpG context-dependent manner in healthy individuals.

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Figures

Figure 1
Figure 1
Overview of the analytic strategy for this study. Methylation data for 26,486 autosomal CpGs was assayed for each of 205 blood specimens by the Infinium HumanMethylation27k BeadArray. Three complementary approaches were used to assess the association between DNA methylation and age, sex and select environmental exposures, while taking into account variation in DNA sequence features of each CpG: (1) unsupervised clustering of CpGs by recursively partitioned mixture modeling (RPMM) into 32 CpG classes based on like methylation patterns, followed by evaluation of an association of mean methylation of the CpGs for each resultant class with age, gender or exposures; (2) clustering of CpGs into 41 classes based on bioinformatic attributes (CpG sequence features), again followed by evaluation of an association of mean methylation of the CpGs for each resultant class with age, gender or exposures; and (3) a marginal-model based analysis (no clustering), assessing interactions between DNA sequence features and age, gender and exposures, with respect to methylation.
Figure 2
Figure 2
Adjusted association of exposures with unsupervised RPMM class methylation. The colored dots indicate the degree of average class methylation. The y-axis represents the t-statistic for the association of class methylation and the corresponding exposure from the multiple regression models, while the 32 CpG classes are depicted on the x-axis. The p value for the omnibus test (first difference test) of significance for the association of each exposure and average class methylation is found at the top left of the corresponding exposure plot. The red dotted lines represent the 95th percentile of the permutation distribution of the maximum absolute value (over 32 classes) of the regression coefficient t-statistics as a control for multiple comparisons. Note: age was adjusted for gender; gender was adjusted for age and hair dye use; all other models were adjusted for age and gender. *Restricted to ever-smokers. **Compared to non-drinkers (zero alcoholic drinks per week).
Figure 3
Figure 3
Frequency of sequence features associated with unsupervised RPMM class CpG loci. Classes are represented on each plot by the colored dots, which indicate the degree of average class methylation. The y-axis represents the frequency of CpG loci in each class associated with the sequence feature of interest, while the 32 CpG classes are depicted on the x-axis. Abbreviations: CGI = CpG island; PcG Target = located in a polycomb group protein target gene; TFBS, located within 1 kb of a transcription factor binding site; LINE, long interspersed nuclear element; MIR, mammalian wide-interspersed repeat element.
Figure 4
Figure 4
Adjusted association of exposures with bioinformatically-derived CpG class methylation. The colored dots indicate the degree of average class methylation. The y-axis represents the t-statistic for the association of class methylation and the corresponding exposure from the multiple regression models, while the 41 bioinformatically-derived Cp6 classes are listed numerically on the x-axis; the bioinformatic attributes corresponding to the numbers are provided in Supplemental Table S3. The p-value for the omnibus test (supremum) of significance for the association of each exposure and average class methylation is found at the top left the corresponding exposure plot. The red dotted lines represent the 95th percentile of the permutation distribution of the maximum absolute value (over 32 classes) of the regression coefficient t-statistics as a control for multiple comparisons. Note: age was adjusted for gender; gender was adjusted for age and hair dye use; all other models were adjusted for age and gender. There were 8 classes significantly associated with age: (12) CGI|MIR; (16) PcG|TFBS; (20) PcG; (23) TFBS; (24) PcG|MIR|TFBS; (27) MIR|TFBS; (29) CGI|PcG|LINE2; (31) LINE2|TFBS. Abbreviations: CGI = CpG island; PcG = located in a polycomb group protein target gene; TFBS = located within 1 kb of a transcription factor binding site; LINE = long interspersed nuclear element; MIR = mammalian wide-interspersed repeat element. *Restricted to ever-smokers. **Compared to non-drinkers (zero alcoholic drinks per week).
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