Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population.

Abstract

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype-phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes.

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Epigenome-Wide Scans Identify Differentially
Methylated Regions for Age and Age-Related
Phenotypes in a Healthy Ageing Population
Jordana T. Bell1,2.*, Pei-Chien Tsai2., Tsun-Po Yang3, Ruth Pidsley4, James Nisbet3, Daniel Glass2,
Massimo Mangino2, Guangju Zhai2,5, Feng Zhang2, Ana Valdes2, So-Youn Shin3, Emma L. Dempster4,
Robin M. Murray6, Elin Grundberg2,3, Asa K. Hedman1, Alexandra Nica7, Kerrin S. Small2, The MuTHER
Consortium{, Emmanouil T. Dermitzakis7, Mark I. McCarthy1,8,9, Jonathan Mill4, Tim D. Spector2"*,
Panos Deloukas3"*
1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom, 2Department of Twin Research and Genetic Epidemiology, King’s College
London, London, United Kingdom, 3Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom, 4MRC Social, Genetic, and
Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London, United Kingdom, 5Discipline of Genetics, Faculty of Medicine, Memorial
University of Newfoundland, St. John’s, Canada, 6Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London, United Kingdom, 7Department
of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland, 8Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford,
Churchill Hospital, Oxford, United Kingdom, 9Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
Abstract
Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and
functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy
ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female
twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed
evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with
methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions
(DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans
(EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal
longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated
age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype.
Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy
ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61,
suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms
underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS
ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype–phenotype association, where the CpG-site
was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential
environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small
set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects
on age-related phenotypes.
Citation: Bell JT, Tsai P-C, Yang T-P, Pidsley R, Nisbet J, et al. (2012) Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related
Phenotypes in a Healthy Ageing Population. PLoS Genet 8(4): e1002629. doi:10.1371/journal.pgen.1002629
Editor: Jun Li, University of Michigan, United States of America
Received November 7, 2011; Accepted February 22, 2012; Published April 19, 2012
Copyright: ? 2012 Bell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported in part by the Wellcome Trust and the European Research Council (ERC 250157). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: jordana.bell@kcl.ac.uk (JTB); tim.spector@kcl.ac.uk (TDS); panos@sanger.ac.uk (PD)
. These authors contributed equally to this work.
" These authors also contributed equally to this work.
{ Membership of the MuTHER Consortium is provided in the Acknowledgments.
Introduction
DNA methylation is an epigenetic mechanism that plays an
important role in gene expression regulation, development, and
disease. Increasing evidence points to the distinct contributions of
genetic [1,2,3,4,5], environmental [6,7,8], and stochastic factors to
DNA methylation levels at individual genomic regions. In
addition, DNA methylation patterns at specific CpG-sites can
also vary over time within an individual [9,10] and correspond-
ingly, age-related methylation changes have been identified in
multiple tissues and organisms [11,12,13,14,15]. Although age-
related changes in methylation have been implicated in healthy
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ageing and longevity, the causes and functional consequences of
these remain unclear.
Ageing is a complex process, which represents the progression of
multiple degenerative processes within an individual. Studies in
different organisms have identified many factors that contribute to
lifespan and the rate of healthy ageing within an individual. These
include components of biological mechanisms involved in cellular
senescence, oxidative stress, DNA repair, protein glycation, and
others (see [16]). Taking these into account, the concept of
biological age has been proposed as a better predictor of lifespan
and functional capacity than chronological age alone. Previous
studies have proposed that certain traits can be used as measures of
biological age [17] and have put forward a stringent definition of
an ageing biomarker (see [18]). Here, we examined age-related
phenotypes that have previously been considered biomarkers of
ageing (see [19]), specifically white cell telomere length, blood
pressure, lung function, grip strength, bone mineral density,
parental longevity, parental age at reproduction, and serum levels
of 5-dehydroepiandrosterone (DHEAS), cholesterol, albumin, and
creatinine.
Epigenetic studies of age-related phenotypes can help identify
molecular changes that associate with the ageing process. Such
changes may include both biological markers of accumulated
stochastic damage in the organism, as well as specific susceptibility
factors that may play a regulatory role. We explored the
hypothesis that epigenetic changes contribute to the rate of ageing
and potential longevity in a sample of 172 middle-aged female
twins, where methylation profiles and age-DMRs were previously
characterized in 93 individuals from the sample [14]. We
compared DNA methylation patterns with chronological age in
the sample of 172 individuals and related epigenetic variation to
age-related phenotypes that have previously been used as
biomarkers of ageing. We identified phenotype-associated DNA
methylation changes and combined genetic, epigenetic, expres-
sion, and phenotype data to help understand the underlying
mechanism of association between epigenetic variation, chrono-
logical age, and ageing-related traits.
Results
DNA methylation patterns in twins associate with genetic
variants
We characterized DNA methylation patterns in a sample of 172
female twins at 26,690 promoter CpG-sites that map uniquely
across the genome. We observed that the majority of autosomal
CpG-sites were un-methylated (beta ,0.3, 69% of probes), unlike
X-chromosome CpG-sites, which were predominantly hemi-
methylated consistent with X-chromosome inactivation (Figure
S1). Comparisons of methylation rates within twin pairs indicated
that MZ twins had more similar DNA methylation patterns
compared to DZ twins, and methylation levels were more similar
within co-twins compared to unrelated pairs of individuals
(Figure 1A). Correspondingly, intra-class correlation coefficients
were significantly greater in MZ twin pairs compared to DZ pairs
(Figure S2) indicating evidence for DNA methylation heritability.
Estimates of DNA methylation heritability were obtained from
CpG-site specific distributions of the MZ and DZ correlation
differences. The average whole blood autosomal genome-wide
heritability rate was estimated to be 0.182 (genome-wide mean
estimate was between 0.176 (95%CI: 0.168–0.185) and 0.188
(95%CI: 0.180–0.196), see Figure S2).
We further investigated methylation heritability by identifying
genetic associations with DNA methylation, or methylation QTLs
(meQTLs). Methylation QTLs have previously been identified in
multiple samples and tissues, and the majority of reported
associations have been observed in cis and close to the probe
[1,3,20]. Therefore, we restricted our analyses to cis-meQTLs
only, that is, SNPs within 100 kb of the methylation probe. At a
permutation-based FDR of 5% (P=1.061025), we identified
1,537 probes (6.3% of probes tested) that had cis-meQTLs
associations involving 22,849 SNPs (Figure 1B). The majority of
associations were obtained for SNPs within a few kb of the
methylation probe (Figure 1C). Altogether, of the 1,537 probes
with meQTLs identified in this study, 444 (28%) and 61 (34%)
were previously reported in brain [3] and lymphoblastoid cell lines
[1], respectively (Figure 1D).
Genetic variants that associate with methylation can also have
effects on gene expression variation. For the individuals in our
sample we also had available gene expression data [21]. We
compared the SNPs that were meQTLs in our data with eQTLs
from lymphoblastoid cell lines (LCLs) in these individuals, as
previously defined [21]. We observed that 10% of previously
reported eQTLs in LCLs also had significant meQTL signals in
whole blood, suggesting shared mechanisms of methylation and
gene-expression regulation in a small proportion of genes, which is
consistent with previous findings [1,3,5].
Identification of differentially methylated regions (DMRs)
for age and ageing-related phenotypes
We next compared DNA methylation patterns to age and age-
related phenotypes by conducting epigenome-wide association
scans (EWAS). We fitted a linear mixed effects model regressing
methylation levels at each probe on the chronological age of the
individuals and included fixed-effect (methylation chip and order
of the sample on the chip) and random-effect (family-structure and
zygosity) covariates. Differentially methylated regions (DMRs)
associated with age (a-DMRs) were identified as those that
surpassed the 5% FDR threshold (P=3.961024). We identified
490 a-DMRs in the 172 females twins (Table S1, Figure 2A), of
which the majority (98%) exhibited increased methylation with
age (hyper-methylated a-DMRs). Of the 490 a-DMRs in our
study, 75 hyper-methylated a-DMRs were previously reported as
Author Summary
Epigenetic patterns vary during healthy ageing and
development. Age-related DNA methylation changes have
been implicated in cellular senescence and longevity, yet
the causes and functional consequences of these variants
remain unclear. To understand the biological mechanisms
involved in potential longevity and rate of healthy ageing,
we performed genome-wide association of epigenetic and
genetic variation with both chronological age and age-
related phenotypes. We identified hundreds of DNA
methylation variants significantly associated with age
and replicated these in an independent sample of young
adult twins. Only a small proportion of these variants were
also associated with age-related phenotypes. Therefore,
the majority of age-related epigenetic changes do not
contribute to rate of healthy ageing at later stages in life.
Our results suggest that age-related changes in methyla-
tion occur throughout an individual’s lifespan and that a
proportion of these may be initiated from an early age.
Intriguingly, a fraction of the age differentially methylated
regions also associated with genetic variants in our
sample, suggesting that DNA methylation may be a
candidate mechanism of mediating not only environmen-
tal but also genetic effects on age-related phenotypes.
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hyper-methylated a-DMRs in a subset of these data (93 individuals
from [14]). Furthermore, 36 a-DMRs from our study replicated
with the same direction of effect as 88 a-DMRs identified in saliva
samples in male twins [11], and 3 a-DMRs were also in the top 10
reported a-DMRs from multiple brain tissues [13]. The a-DMR
probes had similar mean levels of methylation, but significantly
greater variability (Wilcoxon rank-sum test P,2.2610216) com-
pared to autosomal CpG-sites across the genome.
The phenotype EWAS DMR analyses focused on the
comparison between methylation and age-related phenotypes in
the linear mixed effects regression (LMER-DMRs) framework. We
examined sixteen phenotypes (Table 1, Figure S3, Table S2),
which have previously been studied as biomarkers of age. These
phenotypes included telomere length, systolic blood pressure
(SBP), diastolic blood pressure (DBP), FEV1 and FVC to examine
lung function, grip strength, bone mineral density (BMD), serum
levels of DHEAS, serum total cholesterol levels, serum high
density cholesterol levels (HDL), calculated levels of serum low
density cholesterol (LDL), serum albumin levels, serum creatinine
levels,maternallongevity(MLONG),
(PLONG), maternal age at reproduction (MREPROD), and
paternal age at reproduction (PREPROD). For each phenotype
we regressed methylation levels against the phenotype and
included methylation chip and order on the chip as fixed-effect
paternallongevity
Figure 1. DNA methylation variation associates with genetic variation. A. Genome-wide pair-wise correlation coefficients in 21 pairs of MZ
twins, 31 pairs of DZ twins, and 1091 pairs of unrelated individuals. B. Histogram of the observed distribution of P-values (black bars) and the
expected distribution (red area indicates 90% confidence region) obtained from ten permutations of the data. C. Majority of SNPs that are cis-meQTLs
are located within few kb of the methylation probe. D. Overlap of probes that have cis-meQTLs from the current study (red) with probes reported to
have meQTLs in brain tissues (blue, [3]) and in LCLs (grey, [1]), not accounting for SNP overlap.
doi:10.1371/journal.pgen.1002629.g001
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covariates and family and zygosity as random effects. We also
performed the analyses by including or excluding chronological
age as a fixed effect covariate. We examined the results using a
permutation-based significance threshold, by preserving twin-
structure and taking into account missing data patterns for each
phenotype and evidence of co-methylation and deviations from
normality in the DNA methylation data. We observed that four
ap-DMRs for LDL (cg03001305 in STAT5A with LDL: age-
corrected methylation,LDL beta=4.7361023, se=8.7561024,
P=8.7261027), lung function (cg16463460 in WT1 with FEV1:
methylation,FEV1 beta=20.035, se=6.7261023, P=5.3161027;
cg16463460 in WT1 with FVC: methylation,FVC beta=
20.0293, se=5.5961023, P=4.6761027), and maternal longevity
(cg09259772 in ARL4A with MLONG: methylation,MLONG
beta=2.1161023, se=4.2161024, P=1.8361026; cg13870866 in
TBX20: methylation,MLONG beta=1.1061023, se=2.1161024,
P=1.2161026) were genome-wide significant at a permutation-based
FDR of 5% (Figure 2, Figure S4). We repeated the LMER-DMR
analyses using normalized methylation levels and observed that the
reported FDR 5% ap-DMRs (Table 1) also fell in the top-ranked
results from the normalized methylation DMR analyses.
We compared the 490 a-DMRs to ap-DMRs. Only one of the
490 a-DMRs was also significantly associated with ageing-related
phenotypes, specifically ap-DMR for maternal longevity (TBX20).
We examined the genome-wide distribution of ap-DMR associ-
ation P-values in the set of a-DMRs, but did not observe an
enrichment of ap-DMRs in the set of a-DMRs compared to
random sets of probes (Figure S5).
We tested for correlation in DNA methylation (co-methylation)
between nearby CpG-sites both genome-wide and specifically at
the 490 a-DMR CpG-sites. We observed evidence for co-
methylation, that is, pairs of CpG-sites located within 1–2 kb
apart showed greater correlation in methylation patterns com-
pared to pairs of CpG-sites located further apart. The pattern of
co-methylation was also observed at the a-DMR CpG-sites, in
particular DNA methylation levels at CpG-sites located within
500 bp of an a-DMR were highly correlated with the a-DMR
DNA methylation levels compared to CpG-sites located further
away from a-DMRs (Figure S6).
To assess if the DMRs identified in our study capture
differential proportion of whole blood cell (WBC) sub-types we
compared DNA methylation levels with WBC sub-type propor-
tions for neutrophils, eosinophils, monocytes, and lymphocytes.
Blood count DMR analyses were performed at the 493 a-DMRs
and ap-DMRs, and results are presented at a DMR Bonferroni
corrected P-value=0.05 (nominal P=161024). We did not
observe significant associations between DNA methylation at the
490 a-DMR probes with proportion of neutrophils, eosinophils, or
monocytes in our data. However, at 19 a-DMRs (3.9% of a-
DMRs) DNA methylation levels were significantly associated with
Figure 2. Epigenome-wide association scans of age and age-related phenotypes. (A) Genome-wide results for chronological age at
FDR=5% (a-DMRs). Red dashed line corresponds to FDR 5% significance level threshold. (B–E) Peak ap-DMRs were obtained for (B) LDL-DMR
cg03001305, (C) lung function (FVC) DMR cg16463460, and maternal longevity (MLONG) DMRs cg09259772 (D) and cg13870866 (E); grey lines
correspond to fitted linear regression models on these data.
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lymphocyte counts (Table S3), suggesting that the a-DMR effects
at these probes may in part reflect variability in the number of
lymphocytes over time. We did not observe significant associations
between DNA methylation levels at the four ap-DMRs with any of
the blood cell sub-types tested. We conclude that variability in
WBC sub-types does not have a major effect on age and age-
related DMRs in our study.
Genetic associations for age-related traits may be
mediated by DNA methylation
To explore potential mechanisms underlying a-DMRs and ap-
DMRs in our sample, we first considered the hypothesis that DMR
effects may mediate genetic-phenotype associations. We focused
specifically on the overall set of 493 identified DMRs for age (490 a-
DMRs) and age-related phenotypes (4 ap-DMRs). We observed
that 5% of these DMRs also had cis-meQTL effects, which was
lower than the genome-wide rate of 6.3% of probes on the array
with cis-meQTLs. Altogether, the DMRs with cis-meQTLs were
located in 26 genes and some of the genes had previously reported
genetic associations with longevity (a-DMR MEFV [22]) or had
been implicated in longevity and ageing (a-DMRs SMPD3 [23],
GALR1 [24,25], ID4 [26]; see Figure 3A). Therefore, genetic and
methylation effects may impact age-related phenotypes in a small
proportion of genes, either with independent effects or by mediating
genetic-phenotype associations through DNA methylation.
To explore this hypothesis further on a genome-wide level, we
estimated the extent to which cis-meQTLs, genotype-phenotype
associations, and ap-DMRs overlapped in our data. We performed
genome-wide association scans (GWAS) for 12 phenotypes in the
set of 172 twins. We assessed the overlap between: (1) SNPs that
were cis-meQTLs and were also phenotype-GWAS-QTLs, (2)
phenotypes with GWAS-QTLs that also had ap-DMRs, and (3)
CpG-sites with meQTLs that were also ap-DMRs. We compared
the overlap in the observed data to two genome-wide permutations
of the analyses.
There were 1,537 CpG-sites associated with 22,849 cis-meQTLs
SNPs in our data. Of the 22,849 SNPs, 344 SNPs (which were
originally cis-meQTLs for 111 CpG-sites) also showed modest
suggestive evidence for association in the phenotype-GWAS
analyses for each trait (at P=0.001). Of the 111 CpG sites, 16
CpG-sites (with 53 SNPs) also had suggestive evidence for ap-
DMR signals (P=0.01), where the CpG-site was associated with
the same phenotype as the GWAS QTL SNPs (which were also
cis-meQTLs for that CpG-site). Altogether, we observed 1% (16 of
1,537 probes) three-way overlap across the analyses combining the
12 phenotypes, and up to 0.2% overlap for individual phenotypes
(for BMD, Cholesterol, DBP, DHEAS, FVC, HDL, and Telomere
length; see Table S4). In all cases, a SNP genotype was associated
with both CpG-site methylation and phenotype, and the CpG-site
methylation was also associated with the phenotype, suggesting
that these are likely genotype-phenotype associations that may be
mediated through DNA methylation. We estimated the expected
overlap of results under the null hypothesis that methylation does
not mediate genotype-phenotype associations by permuting the
methylation data only, preserving twin structure and patterns of
co-methylation, for two genome-wide permutations. We selected
the top 1,537 CpG-sites that showed most associations with cis-
meQTL SNPs in the permutations, and assessed the proportion of
Table 1. Age and age-related phenotype EWAS DMR results.
Phenotypea
Data (%)Age Effectb
EWAS LMER-DMRsc
EWAS MZ-DMRse
Age 100NA490 age DMRs NA
Telomere length62.2
20.03060.009- NA
SBP1000.66360.146--
DBP100
20.01960.098--
Lung function97.7
20.02860.006cg16463460 (WT1)-
Grip strength64.0
20.45160.081- NA
BMD 86.7
20.00560.001--
Serum DHEAS 99.4
20.02360.007--
Cholesterol97.10.05260.012- cg01136458 (CSMD1)
HDL 97.10.01660.012--
LDL94.8 0.01860.011 cg03001305 (STAT5A)d
cg01136458 (CSMD1)
Serum Albumin 91.9
20.10260.030--
Serum Creatinine 86.00.12060.102--
MLONG73.8 2.36102662.861024
cg09259772 (ARL4A)
cg13870866 (TBX20)
NA
PLONG73.34.16102661.761024
-NA
MREPROD80.86.36102665.361024
-NA
PREPROD 82.06.26102664.561024
- NA
aPhenotypes are listed as follows: Telomere length, systolic blood pressure (SBP), diastolic blood pressure (DBP), Lung function (FVC), grip strength, bone mineral
density (BMD), serum levels of DHEAS, serum total cholesterol, high density cholesterol (HDL), low density cholesterol (LDL), serum albumin, serum creatinine, maternal
longevity (MLONG), paternal longevity (PLONG), and maternal age at reproduction (MREPROD), and paternal age at reproduction (PREPROD).
bRegression coefficient estimate from the linear mixed effect regression model regressing raw phenotype on chronological age (age regression coefficient +/2 se).
cLMER-DMR results are shown at a permutation-based FDR threshold of 5%.
dResults were significant when age was included as a fixed-effect covariate.
eMZ-DMRs are shown at FDR 5% threshold, including age correction.
doi:10.1371/journal.pgen.1002629.t001
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CpG-sites that showed suggestive methylation-phenotype associ-
ations (P=0.01) and had cis-meQTLs SNPs that showed
suggestive genotype-phenotype associations (P=0.001). In both
replicates, we observed minimal overlap of probes across the three
sets of the analyses under the null hypothesis (mean overlap 0.36%
or 5.5 probes of 1,537 overlapped under the null).
Age-related DNA methylation differences in
monozygotic twins
Epigenetic variants may also accumulate independent of the
genetic sequence, because different lifestyle choices and environ-
ments may trigger epigenetic changes. The recently reported
association between smoking and methylation levels in F2RL3 is
likely to be an example of such effects [6]. Therefore, we next
tested for ageing-phenotype associated methylation variants that
appeared uncorrelated with genetic variation, by comparing
methylation and phenotype differences within monozygotic twin
pairs (MZ-DMRs). We limited analyses to 21 MZ twin pairs and
12 phenotypes for which at least 12 of the 21 pairs had phenotype
data available for both twins (Table 1). At a permutation-based
FDRof5%,we observed
P=3.1261027) in the promoter of the CUB and Sushi multiple
domains 1 gene (CSMD1) that associated with total cholesterol and
LDL (Figure 3B). Genetic variants in CSMD1 have previously been
associated with several complex traits in multiple studies, but we
did not observe an enrichment of ap-DMR or MZ-DMR signals in
this gene for the other age-related phenotypes in our data.
oneMZ-DMR(cg01136458,
Replication of age DMRs in younger adult twins
We pursued replication of the 490 a-DMRs in a sample of 44
younger adult MZ twins (age range 20–61, median age 28), who
were discordant for psychosis [27]. In the overall set of 44 twins,
Figure 3. Examples of age and age-related phenotype DMRs in the discovery and replication samples. (A) Example of an a-DMR probe
(cg00468146 in ID4), which also has cis-meQTLs. Individuals are coloured according to cis-meQTL rs12660828 genotype (AG=red, GG=blue). (B) MZ
twin methylation difference analyses identify potential environmentally-triggered DMR cg01136458 in CSMD1 associated with LDL. MZ co-twins are
linked by dark blue (positive DMR effect) or light blue (negative DMR effect) dashed lines. (C,D) The two most associated a-DMRs (in NHLRC1 (C) and
IRX5 (D)) in the discovery sample of 172 individuals (black dots) also replicate in the sample of 44 younger individuals (red triangles). Dashed lines
represent estimated effects within the discovery (black) and replication (red) sample.
doi:10.1371/journal.pgen.1002629.g003
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we replicated 184 a-DMRs (38%) with the same direction of effect
at a nominally significant threshold (P=0.05). In the set of 22
unaffected unrelated individuals alone, 69 a-DMRs (14%)
replicated with the same direction of association at nominal
significance. Given the relatively modest sample size, we also
examined the direction of the association between methylation and
age without considering significance thresholds. We observed that
404 a-DMRs (82%) showed consistent effects in the overall set of
44 twins, and 369 a-DMRs (77%) had consistent effects in the set
22 unaffected unrelated individuals alone. The two most
significant a-DMRs (cg22736354 in NHLRC1 and cg05266781
in IRX5) showed consistent effects in both discovery and
replication samples (Figure 3C, 3D). Both a-DMRs were hyper-
methylated withageinthediscovery(cg22736354methylation,age
beta=2.7661023, se=3.7361024; cg05266781 methylation,age
beta=2.0061023, se=3.0361024) and replication (cg22736354
methylation,age beta=2.0161023, se=3.0361024; cg05266781
methylation,age beta=2.0061023, se=4.8761024) samples.
Functional characterization of age DMRs
We explored the functional role of a-DMRs by studying their
genome localization, by comparing the a-DMR methylation data
to gene expression estimates from LCLs, and by searching for gene
ontology terms associated with the a-DMR genes.
We first characterized the a-DMRs by examining their location
with respect to functional genomic annotations and other
epigenetic signature marks. We considered functional categories
with respect to CpG islands, histone modification marks in LCLs,
and DNA binding motifs. For each category we assessed the
enrichment or depletion of a-DMR probes relative to all 26,690
probes (Figure 4A). We found an enrichment of a-DMRs in CpG
islands (see Figure 4A), which is consistent with previous
observations for hyper-methylated a-DMRs [14,28]. We also
observed a depletion of a-DMRs in the presence of histone marks
that target active genes in LCLs (Figure 4A). For example, a-
DMRs were under-represented in H3K27ac, H3K4me3, and
H3K9ac peaks, which are indicative of enhancers or transcrip-
tional activity, and have been positively correlated with transcrip-
tion levels.
To search for an enrichment of DNA binding motifs in the set of
435 a-DMR genes, we used PSCAN [29] with the JASPAR
database [30]. We found a significant enrichment for 28
transcription factor binding sites, many of which could play a
role in ageing (Table S5). The transcription factors associated with
enriched DNA binding sites were involved in development
(PLAG1), cellular senescence (Mycn), regulation of cell cycle
(Egr1, CTCF, E2F1), or had also been associated with ageing (NF-
kappaB), age-related processes (NFKB1, Klf4, MIZF, Mafb,
ESR1) or other established ageing-related genes such as WRN
(SP1, TFAP2A, Myc, Mycn), TERT (Myc), and TORC1
(HIF1A::ARNT).
To explore the functional consequences of a-DMRs, we
examined gene-expression data at the a-DMR genes, using gene
expression estimates obtained in LCLs from the same individuals
[21]. We compared whole blood DNA methylation to LCL gene
expression in 168 individuals at 348 genes, which had methylation
CpG-sites within 2 kb of the transcription start site. We found
significant negative correlations between methylation and gene
expression in the set of a-DMR genes (Figure 4B), and an overall
trend towards low levels of expression at a-DMR genes. One
caveat applying to this analysis is that blood methylation
corresponds to multiple cell types including lymphocytes.
We performed gene ontology term enrichment analyses of
biological processes and molecular functions in the set of 435 a-
DMR genes [31]. The results indicated strong enrichment for
genes involved in the regulation of developmental morphological
processes, DNA binding, regulation of cell differentiation,
regulation of transcription, and regulation of metabolic and
biosynthetic processes (Table S6).
Discussion
We identify hundreds of CpG-sites that exhibit age-related
directional changes in methylation. The majority of these effects
are hyper-methylated with age, a large proportion replicate in an
independent sample, and some changes are observed in multiple
tissues. These findings indicate that a-DMRs are not likely
stochastic events, but instead may associate with biological
mechanisms involved in ageing and potential longevity. To
address this we compared methylation variants to measures of
biological ageing, focusing on markers like telomere length and
other age-associated phenotypes that have previously been linked
to ageing. However, our phenotype-methylation comparisons
identified only a small subset of a-DMRs that also associate with
ageing related traits. These findings suggest that although a-DMRs
do not appear to be random events, the majority of observed a-
DMRs may either be neutral (or of very small individual effect) to
measures of biological age at later stages in life, or may relate to as
yet unknown pathways that correlate with biological ageing.
The a-DMRs we detected in blood overlap with previously
reported a-DMRs obtained in saliva and brain samples, and
previous observations also show that some hyper-methylated a-
DMRs occur in both blood and buccal tissues [14]. These results
indicate that a proportion of a-DMRs are conserved across tissues
in samples of different ages and genders, and raise the question of
when such age related methylation changes occur during an
individual’s lifespan and what their functional role is. Functional
annotation of a-DMRs show an enrichment of genes involved in
regulation of development, morphology, regulation of transcrip-
tion, and DNA binding, which has also been previously observed
in brain samples [13]. The genes nearest to a-DMRs also showed
an enrichment of DNA binding motifs for transcription factors
linked to ageing. Functional genomic annotation indicated that a-
DMRs tend to associate with epigenetic marks targeting low levels
of transcription. Consistent with this, a-DMR genes showed
predominantly low levels of expression in LCLs and significant
negative correlations between methylation and gene expression.
Altogether, we find that a-DMRs are located in regions of the
genome that functionally link to development and ageing, and
tend to show low gene expression rates in our sample of middle-
aged individuals.
DNA methylation plays a key role in development and tissue
differentiation and therefore, it is plausible that at some a-DMRs
differential methylation patterns are established early on in
development prior to tissue differentiation and continue to
intensify over time. For example, CpG-sites that are methylated
during early development may become hyper-methylated over
time, either because such sites are more prone to methylation or
because cells carrying the methylated variant are more likely to
replicate. Our findings indicate that age-related changes in
methylation occur throughout life, but the timing of the initial
age-related trigger at each CpG-site remains unclear. Our results
are consistent with a model where at some CpG-sites the initial
change may occur during development and early life, but
specifically at an age prior to adulthood. Age DMR studies of
younger samples could be useful in establishing the proportion of
a-DMRs that are also observed at earlier stages in life. We were
able to replicate up to 38% of a-DMRs in a sample of younger
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adults, but samples from newborns or samples obtained prior to
tissue differentiation would help resolve the question of when a-
DMRs are established, especially tissue conserved a-DMRs.
We tested for methylation associations with age-related
phenotypes (ap-DMRs) to gain insight into potential mechanisms
underlying a-DMRs. We identified four ap-DMRs, of which only
one (cg13870866 in TBX20) was also an a-DMR. Two of the ap-
DMRs were in genes already implicated in ageing, longevity, or
cell senescence, STAT5A [32] and WT1 [33]. Our genotype-
methylation-phenotype overlap results suggest that in a small
proportion of genes DNA methylation may be a candidate
mechanism of mediating genetic association effects on ageing-
related phenotypes, however, we cannot exclude the possibility
that rare genetic variants in the methylation probe sequence drive
some of these associations. We also assayed DNA methylation
levels at the four ap-DMR probes in 48 of the individuals in the
current study using the new Illumina Infinium HumanMethyla-
tion450 BeadChip and obtained significant positive correlations in
Figure 4. Functional characterization of a-DMRs. A. Enrichment and depletion of a-DMRs in functional genomic categories. Enrichment is
calculated as the proportion of a-DMRs in each functional category (CpG islands (green) or HapMap CEPH LCL histone peaks (blue, black)) over the
proportion of 26,690 probe in that functional category. Bars represent the 95% bootstrap percentile confidence intervals. B. Whole blood methylation
and LCL gene expression estimates in the age DMR genes show significant negative correlation (histogram shows the distribution of gene-based rank
correlation coefficients between methylation and gene expression).
doi:10.1371/journal.pgen.1002629.g004
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DNA methylation levels at three ap-DMRs (cg16463460,
cg09259772, and cg13870866), indicating evidence for technical
validation at these probes.
A difficult question in epigenetic studies of phenotypic data is
establishing the timing of the epigenetic change relative to trait
progression. The age-related phenotype methylation changes
identified here may occur prior to the phenotypic change and
potentially contribute to phenotype variation, or they may occur
as a consequence of ageing processes in the cell. In this cross-
sectional study we cannot establish the timing of ap-DMRs with
respect to phenotype progression, but can use the findings as
potential markers of rate of ageing.
Regions that exhibit evidence for DNA methylation heritability,
such as the IGF2/H19 region, also exhibit more stable DNA
methylation levels over time and tend to occur in functionally
important promoter regions [4]. Epigenetic variants in heritable
methylation regions are likely to be present at birth, to be more
stable over time, and may be involved in regulating the rate of
ageing. In our study, 26 a-DMRs also had cis-meQTLs and
represent a candidate set of heritable DNA methylation regions
that are likely to be more stable and may be involved in longevity.
On the other hand, environment-dependent changes in DNA
methylation in MZ twins have been reported to occur preferen-
tially in gene-poor regions (see [34]). Here, we identify CSMD1 as
the most likely example of an environmentally driven DMR for
LDL, but this gene does not fall in a gene-poor locus.
The methylation heritability estimates obtained in our data,
0.176 and 0.188 (Figure S2), are slightly greater than those
previously reported for whole-blood methylation [4], which may
be due to the difference in regions assayed by the two arrays and to
thepromoterlocationsof ourprobes. Correspondingly we identified
1,537CpG-siteswith meQTLs.Itispossiblethat aproportion ofthe
meQTLs in our data are due to linkage disequilibrium between the
cis-meQTL SNPs and unknown genetic variants in the probe
sequence. Obtaining genetic sequences for these individuals will
establish the extent to which rare-probe variants exist and affect
meQTL findings. However, the overlap across probes with
meQTLs across studies and tissues suggests that a significant
proportion of the QTLs are conserved across tissues [35,36]. These
are likely to exhibit stable patterns of methylation across mitosis and
meiosis, and may be of functional importance.
Many factors will impact the power to detect differential
methylation effects related to age and age-related phenotypes.
One of these factors relates to the coverage and precision of the
methylation assay. In our case, the coverage of methylation sites on
the Illumina27k array is relatively sparse and promoter-specific, and
therefore limits power to detect age related methylation changes. It
is likely that additional age related changes in methylation may be
identified using higher resolution methylation assays in larger
sample sizes of wider age ranges.
In this study, we identified methylation changes associated with
chronological age and ageing-related phenotypes and we explored
mechanisms underlying ageing-related changes in DNA methyl-
ation. Both environmental and genetic factors are thought to
contribute to healthy ageing, and epigenetic mechanisms represent
a potential pathway of mediating these effects on ageing and age
related traits.
Materials and Methods
Ethics statement
All samples and information were collected with written and
signed informed consent. The study was approved by the local
research ethics committee.
Phenotype data
Phenotype data were obtained for 172 female twins from the
TwinsUK cohort. The TwinsUK cohort (St Thomas’ UK Adult
Twin Registry) comprises unselected volunteers ascertained from
the general population [37]. Means and ranges of quantitative
phenotypes inTwinsUK weresimilar to age-matched samples from
the general population in the UK [38]. The 172 twins in this study
included 33 MZ pairs, 43 DZ pairs, and 20 singletons. Phenotypes
used in the current study included telomere length, systolic blood
pressure (SBP), diastolic blood pressure (DBP), forced expiratory
volume in one second (FEV1) and forced expiratory vital capacity
(FVC) to examine lung function, grip strength, bone mineraldensity
(BMD), serum levels of DHEAS (DHEAS), serum total cholesterol
levels, serum high density cholesterol levels (HDL), calculated levels
of serum low density cholesterol (LDL), serum albumin levels
(Albumin), serum creatinine levels (Creatinine), maternal longevity
(MLONG), paternal longevity (PLONG), maternal age at repro-
duction (MREPROD), and paternal age at reproduction (PRE-
PROD). Phenotype data used in the current study were previously
described in the Twins UK sample for the majority of phenotypes,
specifically for telomere length [39,40], blood pressure [41], lung
function [42], grip strength [43], BMD [43,44,45], DHEAS [46],
serum cholesterol [47,48], serum albumin [49] and serum
creatinine [50]. Parental longevity data were obtained by
questionnaire in 2011, and included parental age at death and
parental age at reproduction for each individual. In cases of missing
data,weusedco-twinestimatestoinfervalues.Inrarecasesparental
age at death estimates varied across co-twins, and if the estimates
were within one year we took the mean, otherwise data were
assigned as missing. In 171 of the individuals from our sample we
also obtained white blood cell (WBC) sub-type counts [51]. WBC
counts were derived from fluorescence activated cell sorting of
peripheral blood. WBC sub-type specific cell counts were calculated
by multiplying the proportion of the WBC count comprised by each
cell type by the total WBC cell count (estimated in thousands of cells
per ml), for four cell types in our sample: neutrophils, eosinophils,
monocytes, and lymphocytes.
Illumina Methylation27K data
DNA methylation levels were obtained in 172 middle-aged (age
range 32–80, median age 57) healthy female volunteers who were
twins, including monozygotic (MZ) twins, dizygotic (DZ) twins, and
unrelated individuals. DNA methylation patterns were assayed in
two batches of 93 [14] and 79 samples. We considered 26,690
probes that mapped uniquely to the human genome (hg18) within 2
mismatches (see [1]) and discarded probes with missing data,
resulting in a final set of 24,641 autosomal probes and 959 X-
chromosome probes. Methylation values are reported as betas,
which represent the ratio of array intensity signal obtained from the
methylated beads over the sum of methylated and unmethylated
bead signals. We performed principal components analysis (PCA) of
the methylation values (normalized to N(0,1) at each probe) and
correlated the first five principal component (PC) loadings to
covariates (age, methylation arrays, order of the sample on the
methylation array) to identify potential confounders. We observed
that both methylation array and order of the sample on the array
were significantly correlated with the first and second PCs and
therefore included these two variables as fixed-effect covariates in
the linear mixed effects models used in the majority of downstream
analyses. Further analyses of DNA methylation patterns within
twins were performed using intraclass correlation coefficients (ICC)
using the R package irr (v0.82). Twin-based DNA methylation
heritabilities were estimated as 2(ICC_MZ - ICC_DZ), and were
calculated within each batch of data separately.
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Genotype data
Direct genotypes were available for 171 samples on a
combination of Illumina platforms (HumanHap300, Human-
Hap610Q, 1M-Duo and 1.2MDuo 1M custom arrays) and
stringent quality control checks were applied to these genotype
data as previously described [21,52]. HapMap genotypes were
imputed in the set of 171 individuals. Imputation was performed in
Impute (v2 [53]) using two reference panels, P0 (HapMap2, rel 22,
combined CEU, YRI and, ASN panels) and P1 (610K+, including
the combined HumanHap610K and 1M array). After imputation,
SNPs were filtered at a MAF.5% and an Impute info value of
.0.8. Altogether, there were 2,054,344 directly genotyped and
imputed autosomal SNPs used in the QTL analyses.
Gene expression data
Gene expression estimates and eQTLs from lymphoblastoid cell
lines (LCLs) in the samples were obtained for 168 individuals in
the study [21]. Gene expression levels were measured using the
Illumina expression array HumanHT-12 version 3 as previously
described [21]. Each sample had three technical replicates and
log2 - transformed expression signals were quantile normalized
first across the 3 replicates of each individual, followed by quantile
normalization across all individuals [21]. We assigned methylation
and expression probes to the gene with the nearest transcription
start site using Refseq gene annotations. For each gene we
obtained the mean methylation (or gene expression) estimate, by
averaging values over multiple methylation (or gene expression)
probes if more than one probe was assigned to that gene. There
were altogether 435 genes nearest to the 490 age DMRs, of which
348 had transcription start sites within 2 kb of the methylation
CpG-sites and for which we also had whole blood methylation
data and LCLs gene expression data in 168 individuals. Linear
mixed effects models and Spearman rank correlations were used to
compare methylation and expression data per gene.
Methylation QTL analyses
We tested for methylation QTLs at 24,522 autosomal probes,
which had at least one SNP within 50 kb of the probe that passed
genotype QC criteria. We fitted a linear mixed-effects model,
regressing the methylation levels at each probe on fixed-effect
terms including genotype, methylation chip, and sample order on
the methylation chip, and random-effect terms denoting family
structure and zygosity. Prior to these analyses, the methylation
values at each CpG-site were normalized to N(0,1). Results from
meQTL analyses are presented at a false discovery rate (FDR) of
5%, estimated by permutation. Here, we permuted the methyl-
ation data at the 24,522 autosomal probes, performed cis
association analyses on the permuted and normalized methylation
data, and repeated this procedure for 10 replicates selecting the
most associated SNP per probe per replicate. FDR was calculated
as the fraction of significant hits in the permuted data compared to
the observed data at each p-value threshold.
DMR analyses
Linear mixed effects models were used to assess evidence for
DMRs. In the a-DMR analyses we regressed the raw methylation
levels at each probe on fixed-effect terms including age,
methylation chip, and sample order on the methylation chip,
and random-effect terms denoting family structure and zygosity.
To assess the significance of the a-DMRs we compared this model
to a null model, which excluded age from the fixed-effects terms.
In the ap-DMR analyses we regressed the raw methylation levels
at each probe on fixed-effect terms including phenotype,
methylation chip, and sample order on chip, and random-effect
for family and zygosity, and compared the fit of this model to a
null model which excluded the phenotype. We also performed the
ap-DMR analyses by including age as a fixed effect covariate in
both the null and alternative models. We also repeated both the a-
DMR and ap-DMR analyses using normalized methylation levels
(to N(0,1)) and observed that the reported DMRs were top-ranked
in the normalized analyses. To assess genome-wide significance we
performed 100 permutations and estimated FDR by calculating
the fraction of significant hits in the permuted data compared to
the observed data at a specific P-value threshold.
Monozygotic twin DMR effects were calculated in the set of 21
MZ twin pairs where both twins were assayed within the same
batch of methylation arrays. We estimated MZ-DMRs for 12
phenotypes where data were available in at least 12 MZ pairs. For
each phenotype of interest we fitted a linear model comparing
phenotype within-pair differences to methylation within-pair
differences and reported the P-values obtained from the F-statistics
from the overall regression. For the age-corrected analyses we
fitted the regression including age as a covariate and compared the
results to a null model, which included phenotype differences and
age alone. We performed 100 replicates to estimate FDR 5%
significant results as described above. At the FDR 5% significance
threshold (nominal P=2.0361026), we estimated 35% power to
detect the observed correlation (Pearson correlation=0.83)
between methylation MZ-differences at cg01136458 in CSMD1
(mean MZ-beta-difference=5%) and LDL MZ-differences (mean
MZ-LDL-difference=0.73 SD) in 20 MZ pairs.
Age DMR replication
The replication sample comprised 44 MZ twins discordant for
psychosis, that were profiled on the Illumina 27K array as
previously described [27]. The sample consisted of younger adults
(age range 20–61, median age 28), including both female and male
twin pairs. We compared methylation against age at the 490 a-
DMRs both in the entire set of 44 twins and in the set of 22
unaffected unrelated individuals. In the set of 44 twins we fitted
linear mixed effect models, regressing the normalized beta values
per probe (normalized to N(0,1)) against methylation chip, sample
order on the chip, sex, and age as fixed effects, and family as
random effect. In the set of 22 unaffected unrelated individuals
comprising the control twin from each pair we calculated
Spearman rank correlation coefficients on the untransformed
methylation beta values against age.
Genome-wide association scans
Genome-wide association scans were performed using linear
mixed effects models for 12 phenotypes including telomere length,
systolic blood pressure (SBP), diastolic blood pressure (DBP), FEV1
and FVC to examine lung function, grip strength, bone mineral
density (BMD), serum levels of DHEAS, serum total cholesterol
levels, serum high density cholesterol levels (HDL), calculated levels
of serum low density cholesterol (LDL), serum albumin levels, and
serum creatinine levels. Linear models were fit as described in the
meQTL analyses section substituting phenotype for methylation,
using an additive model. SNPs with evidence for association that
surpassed P=0.001, were considered in the overlap across cis-
meQTL, genotype-phenotype, and DMR findings.
Functional characterization of DMRs
The 26,690 methylation probes were assigned to CpG islands
according to previous definitions [54], resulting in 11,299 CpG
sites that were in CpG islands and 15,391 that were outside of
CpG islands. Histone modification ChIP-seq data were obtained
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from the Encode project from one CEPH HapMap LCL
(GM12878) in the UCSC genome browser. Peaks in the
genome-wide read-depth distribution from ChIP-seq histone
modificationsH3K9ac, H3K27ac,
H3K4me2, and H3K4me3 were obtained as previously described
(see [1]). Enrichment a-DMR estimates were calculated as the
proportion of a-DMRs in each functional category (CpG islands or
histone peaks) over the proportion of 26,690 probe in that
functional category. Enrichment 95% confidence intervals were
estimated using bootstrap percentile intervals of 1,000 re-
samplings of the a-DMR data per annotation category.
Gene ontology term enrichment analysis was performed using
the GOrilla tool for identifying enriched GO terms in the ranked
list of a-DMR genes [31], using Refseq gene annotations in the
entire set of 26,690 probes as background.
H3K27me3, H3K4me1,
Supporting Information
Figure S1
in 172 female twins. Distribution of methylation scores (beta) in (A)
autosomal and (B) X-chromosomal probes in all individuals.
(PDF)
Summary characteristics of DNA methylation patterns
Figure S2
(ICC) in twins. Density plots of ICC in MZ twins (red) and DZ
twins (blue) for two batches of methylation data (batch 1 consists of
93 twins (left) and batch 2 consists of 79 twins (right)). The mean
MZ-ICCs and DZ-ICCs were estimated as 0.257 and 0.168 in
batch 1 (MZ-ICC vs DZ-ICC P,2610216), and as 0.3557 and
0.261 in batch 2 (MZ-ICC vs DZ-ICC P,2610216). The
corresponding methylation probe heritabilities were calculated as
2(ICC_MZ - ICC_DZ) and the genome-wide estimates were 0.176
(95%CI:0.168–0.185) and 0.188 (95%CI:0.180–0.196) for the data
in batch 1 (left) and batch 2 (right), respectively.
(PDF)
Distribution of intra-class correlation coefficients
Figure S3
diagonal plots represent each pair of phenotypes and the corre-
sponding rank correlation coefficient is shown above the diagonal.
(PDF)
Correlation across age-related phenotypes. Below
Figure S4
ap-DMRs were obtained for (A) LDL, (B) lung function (FVC),
and (C) maternal longevity (MLONG) with (green) and without
(blue) age-correction. Red dashed lines correspond to age-
corrected (A) and non-age-corrected (B,C) analysis FDR 5% levels.
(PDF)
EWAS results for age-related phenotypes. FDR 5%
Figure S5
association in the set of age DMRs.
(PDF)
Lack of enrichment of age-related phenotype DMR
Figure S6
in methylation levels between all pair-wise CpG-sites (black) and
between a-DMR CpG-sites (red) in the sample of 172 related
individuals (solid line) and a subset of 96 unrelated individuals
(dotted line).
(PDF)
Evidence for co-methylation. Spearman correlation
Table S1
(XLS)
List of 490 a-DMRs.
Table S2
(XLS)
Descriptive statistics of the age-related phenotypes.
Table S3
lymphocytes.
(XLS)
List of 19 a-DMRs associated with proportion of
Table S4
methylation-phenotype
(GWAS) association results.
(XLS)
Overlap across genotype-methylation (cis-meQTLs),
(ap-DMRs),and genotype-phenotype
Table S5
genes. Results are shown at P=0.05 threshold.
(XLS)
JASPAR motif search results in the set of a-DMR
Table S6
DMR genes. GO term enrichment in a-DMR genes was assessed
relative to the background set of 14,344 genes that map nearest to
the 26,690 probes tested. Results are shown at P=1e-6 for
biological processes and molecular functions.
(XLS)
Gene Ontology term enrichment results in the set of a-
Acknowledgments
We thank the volunteers who participated in the study. We would like to
acknowledge the Genotyping Facility at the Wellcome Trust Sanger
Institute, in particular Douglas Simpkin, for DNA methylation typing. JTB
is a Sir Henry Wellcome postdoctoral fellow. TDS is an European
Research Council Principal Investigator.
The members of MuTHER (Multiple Tissue Human Expres-
sion Resource) Consortium are as follows:
Kourosh R. Ahmadi1, Chrysanthi Ainali2, Amy Barrett3, Veronique
Bataille1, Jordana T. Bell1,4, Alfonso Buil5, Panos Deloukas6, Emmanoil T.
Dermitzakis5, Antigone S. Dimas4,5,7,13, Richard Durbin6, Daniel Glass1,
Elin Grundberg1,6, Neelam Hassanali3, A˚sa K. Hedman4, Catherine
Ingle6, David Knowles8, Maria Krestyaninova9, Cecilia M. Lindgren4,
Christopher E. Lowe10,11, Mark I. McCarthy3,4,12, Eshwar Meduri1,6,
Paola di Meglio13, Josine L. Min4, Stephen B. Montgomery5, Frank O.
Nestle13, Alexandra C. Nica5, James Nisbet6, Stephen O’Rahilly10,11,
Leopold Parts6, Simon Potter6, Magdalena Sekowska6, So-Youn Shin6,
Kerrin S. Small1,6, Nicole Soranzo6, Tim D. Spector1, Gabriela
Surdulescu1, Mary E. Travers3, Loukia Tsaprouni6, Sophia Tsoka2, Alicja
Wilk6, Tsun-Po Yang6, Krina T. Zondervan4
1. Department of Twin Research and Genetic Epidemiology, King’s
College London, London, UK.
2. Department of Informatics, School of Natural and Mathematical
Sciences, King’s College London, London, UK.
3. Oxford Centre for Diabetes, Endocrinology & Metabolism, University
of Oxford, Churchill Hospital, Oxford, UK.
4. Wellcome Trust Centre for Human Genetics, University of Oxford,
Oxford, UK.
5. Department of Genetic Medicine and Development, University of
Geneva Medical School, Geneva, Switzerland.
6. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus,
Hinxton, UK.
7. Biomedical Sciences Research Center Al. Fleming, 16672 Vari,
Greece.
8. University of Cambridge, Cambridge, UK.
9. European Bioinformatics Institute, Hinxton, UK.
10. University of Cambridge Metabolic Research Labs, Institute of
Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK.
11. Cambridge NIHR Biomedical Research Centre, Addenbrooke’s
Hospital, Cambridge, UK.
12. Oxford NIHR Biomedical Research Centre, Churchill Hospital,
Oxford, UK.
13. St. John’s Institute of Dermatology, King’s College London, London,
UK.
Author Contributions
Conceived and designed the experiments: JTB TDS. Performed the
experiments: JN. Analyzed the data: P-CT JTB. Contributed reagents/
materials/analysis tools: T-PY RP DG MM GZ FZ AV S-YS ELD RMM
EG AKH AN KSS ETD MIM JM TDS PD. Wrote the paper: JTB P-CT
TDS PD.
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Age-Related Changes in DNA Methylation
PLoS Genetics | www.plosgenetics.org 12April 2012 | Volume 8 | Issue 4 | e1002629