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ARTICLE
Received 28 Apr 2015 |Accepted 28 Oct 2015 |Published 30 Nov 2015
The epigenomic landscape of African rainforest
hunter-gatherers and farmers
Maud Fagny1,2,3, Etienne Patin1,2, Julia L. MacIsaac4, Maxime Rotival1,2, Timothe
´e Flutre5, Meaghan J. Jones4,
Katherine J. Siddle1,2,He
´le
`ne Quach1,2, Christine Harmant1,2, Lisa M. McEwen4, Alain Froment6, Evelyne Heyer7,
Antoine Gessain8, Edouard Betsem8,9, Patrick Mouguiama-Daouda10, Jean-Marie Hombert11, George H. Perry12,
Luis B. Barreiro13,*, Michael S. Kobor4,* & Lluis Quintana-Murci1,2
The genetic history of African populations is increasingly well documented, yet their patterns
of epigenomic variation remain uncharacterized. Moreover, the relative impacts of DNA
sequence variation and temporal changes in lifestyle and habitat on the human epigenome
remain unknown. Here we generate genome-wide genotype and DNA methylation profiles for
362 rainforest hunter-gatherers and sedentary farmers. We find that the current habitat and
historical lifestyle of a population have similarly critical impacts on the methylome, but the
biological functions affected strongly differ. Specifically, methylation variation associated with
recent changes in habitat mostly concerns immune and cellular functions, whereas that
associated with historical lifestyle affects developmental processes. Furthermore, methylation
variation—particularly that correlated with historical lifestyle—shows strong associations
with nearby genetic variants that, moreover, are enriched in signals of natural selection. Our
work provides new insight into the genetic and environmental factors affecting the
epigenomic landscape of human populations over time.
DOI: 10.1038/ncomms10047 OPEN
1Institut Pasteur, Unit of Human Evolutionary Genetics, Paris 75015, France. 2Centre National de la Recherche Scientifique, URA3012, Paris 75015, France.
3Universite
´Pierre et Marie Curie, Cellule Pasteur UPMC, Paris 75015, France. 4Centre for Molecular Medicine and Therapeutics, Child and Family Research
Institute and Department of Medical Genetics, University of British Columbia, Vancouver, Canada BC V5Z 4H4. 5INRA, UMR AGAP, Montpellier 34060,
France. 6IRD-MNHN, Sorbonne Universite
´s, UMR208, Paris 75005, France. 7CNRS, MNHN, Universite
´Paris Diderot, Sorbonne Paris Cite
´, Sorbonne
Universite
´, UMR7206, Paris 75005, France. 8Institut Pasteur, Unite
´d’Epide
´miologie et Physiopathologie des Virus Oncoge
`nes, Paris 75015, France. 9Faculty
of Medicine and Biomedical Sciences, University of Yaounde
´I, BP1364 Yaounde
´, Cameroon. 10 Laboratoire Langue, Culture et Cognition (LCC), Universite
´
Omar Bongo, BP 13131 Libreville, Gabon. 11 CNRS UMR 5596, Universite
´Lumie
`re-Lyon 2, Lyon 69007, France. 12 Departments of Anthropology and Biology,
Pennsylvania State University, University Park, Pennsylvania 16802, USA. 13 Universite
´de Montre
´al, Centre de Recherche CHU Sainte-Justine, Montre
´al,
Canada H3T 1C5. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to L.Q.-M. (email:
quintana@pasteur.fr).
NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications 1
Africa is the birthplace of modern humans and a region of
extensive genetic, cultural, environmental and phenotypic
diversity1. Over the past years, the increasing amounts of
genomic data available have provided significant insight into
African evolutionary history, including the origins of hunter-
gatherers, population structure, and patterns of migration and
admixture2–10. Moreover, these studies have reported evidence of
selection targeting gene functions related to the changes in
environment, diet and exposure to infectious disease11. Adding
an additional layer of complexity, the study of epigenetic
variation can inform the interplay between the environment
and the genome, yet the epigenomic landscape of African
populations remains unexplored.
DNA methylation—an important epigenetic mark that serves
as biomarker for variation in gene regulation12,13—can be
affected by both inherited DNA sequence variation and a
broad range of environmental factors, such as nutrition,
exposure to toxic pollutants and social environment14–17.
Accumulating evidence indicates that a substantial portion of
DNA methylation variation is accounted for by genetic variation
(methylation quantitative trait loci, meQTLs)16,18–22, which could
affect methylation levels through impaired transcription factor
(TF) binding12,13. Although the role of DNA methylation in gene
regulation (active or passive) and the mechanisms involved
remain controversial, DNA methylation data offer a rich
source of information about ongoing gene activity, and thus it
can provide insight into gene functions that contribute to
phenotypic variation12,13. Recent studies have shown that
DNA methylation differences exist between major ethnic
groups20,23–25, highlighting the potential contribution of
epigenetic modifications to human phenotypic variation.
However, these studies have mostly compared urban
populations of different continental ancestries, so the relative
impacts of DNA sequence variation and temporal changes in
lifestyle and habitat on the human DNA methylome remain
unknown.
The Central African belt provides an ideal setting in which to
address this issue, as it hosts the world’s largest group of active
hunter-gatherers—the rainforest hunter-gatherers (RHGs,
traditionally known as ‘pygmies’)—as well as populations that
have adopted an agrarian lifestyle (AGRs) over the last 5,000
years26,27. In addition to differing in their subsistence strategies,
these two groups differ in other historical and recent aspects of
their evolutionary history. The historical factors relate to
the differences in demography and habitat. The ancestors of
the RHGs and AGRs diverged B60,000 years ago7,8,28–30 and
subsequently experienced population contractions and expan-
sions, respectively10. These groups have also historically occupied
separate ecological habitats—the ancestors of RHGs the
equatorial rainforest while those of AGRs open spaces, such as
savannah and grasslands27,31. More recent changes in the
lifestyles and habitats of these groups are also apparent. Many
RHG groups still live in the rainforest as mobile bands, whereas
AGR populations now occupy primarily rural or urban deforested
areas, though some AGR groups have settled in the rainforest
over the last millennia27,31.
In this study, we define the genome-wide DNA methylation
profiles in blood of various populations of RHG and AGR
inhabiting the Central African belt to first assess the degree of
inter-population variation in DNA methylation. We then explore
the genomic and functional features of differentially methylated
genes to obtain insight into the putative phenotypes involved.
Finally, we assess the contribution of genetic variation to the
DNA methylation levels observed, and search for signals of
positive selection targeting genetic variants associated with
methylation variation.
Here, we show that while both recent changes and historical
differences in the habitat and lifestyle of RHG and AGR have had
a critical impact on their patterns of DNA methylation variation,
the biological functions affected strongly differ. We also show that
DNA methylation variation that correlates with historical lifestyle
shows strong associations with nearby genetic variants that,
moreover, are enriched in signals of natural selection. The
integration of these results allow us to propose a comprehensive
framework of how temporal differences in lifestyle and habitat,
together with the genetic variation, have impacted the epigenomic
landscape of human populations.
Results
Population samples and genetic structure. We investigated
genome-wide genotype and DNA methylation data from a total
of 362 individuals, including a group of RHGs (w-RHG, n¼112),
AGR groups occupying nearby urban deforested habitats
(w-AGR, n¼94), and an AGR group that lives and regularly
practices hunting in a forested region (f-AGR, n¼61) of the
Gabon/Cameroon area (Fig. 1a; Table 1). To compare our results
with an independent set of samples, we also studied RHGs and
AGRs living in the eastern part of the Central African belt
(e-RHG, n¼47 and e-AGR, n¼48, from Uganda). We first
investigated the global genetic structure of the studied popula-
tions using genome-wide SNP (single nucleotide polymorphism)
data. Principal component analysis (PCA) clearly reflected their
history of population divergence7,8,28–30. The largest differences
were observed between RHG and AGR populations, regardless of
their geographic location, followed by the more recent split
between the western and eastern Central African RHG groups
(Fig. 1b).
Processing genome-wide DNA methylation data. We char-
acterized DNA methylation variation in whole blood-derived
samples using the Illumina 450 K array, which interrogates more
than 485,000 sites across the genome. After normalization and
filtering, including the removal of probes containing genetic
variants at a frequency higher than 1% in the populations studied,
we retained 365,886 probes in 352 individuals (Methods).
Samples showed both high reproducibility and expected DNA
methylation profiles across genomic regions, with sites near gene
promoters being less methylated than those located in gene
bodies and intergenic regions (Supplementary Note 1;
Supplementary Fig. 1).
We next sought to correct methylation values (M-values) for
known biological and technical potential confounders, including
gender, age and heterogeneity in blood cell composition. We thus
estimated ages for all samples, and compared predicted and
declared ages for individuals in which chronological age was
reliably ascertained (N¼256, Pearson’s R¼0.84; Supplementary
Fig. 2; Supplementary Note 2), confirming the accuracy of the
epigenetic clock model32. Similarly, we estimated the proportions
of different blood cell types in all samples, using a predictive
model based on a subset of DNA methylation probes33, which
were removed from all subsequent analyses, yielding a final set of
365,401 probes. These predicted values showed strong
correlations with observed proportions of blood cell subtypes,
which were determined in a subset of samples (N¼66) by
fluorescence-activated cell sorting (Pearson’s R: 0.48–0.57;
Supplementary Fig. 3; Supplementary Note 3). Thus, gender,
estimated ages and cell subtype heterogeneity across populations
were used to adjust M-values for all subsequent analyses,
including PCA, the estimation of differentially methylated sites
and the mapping of methylation quantitative trait loci.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047
2NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications
Population differences in DNA methylation profiles. When
performing PCA using all samples, while age and cell counts
strongly correlated with the first 10 PCs using unadjusted
M-values, the subsistence strategy (RHG versus AGR) and
geographic location (western versus eastern Central Africa) of the
populations were the only factors associated with the first 10 PCs
using adjusted M-values (Supplementary Fig. 4; Supplementary
Table 1). Because of technical variables associated with
differences in sample collection and DNA processing between the
western and eastern African samples, one cannot entirely rule out
PC2 (5.97%)
Rainforest
hunter-gatherers
w-RHG (forest)
e-RHG (forest)
w-RHG
e-RHG
w-AGR (urban)
e-AGR (rural)
f-AGR (forest) PC1 (7.23%)
Agriculturalists
Different current habitat
Same historical lifestyle
Same genetics
w-AGR
e-AGR
"Recent"
"Historical + Recent"
"Historical"
f-AGR
Western Central Africa
Eastern Central Africa
Same current habital
Different historical lifestyle
Different genetics
AGR w-RHG e-RHG
ba
c
Figure 1 | Study design and genetic structure of rainforest hunter-gatherers and farmers. (a) Geographic location of the sampled rainforest hunter-
gatherer (RHG) and farmer (AGR) populations. (b) Principal component analysis (PCA) of the genotype data for the study populations, based on 456,507
independent genome-wide SNPs. The tree presented at the top right of the panel represents the branching model for these populations7,8,28–30.
(c) Schematic representation of the different population comparisons, indicated by arrows, used for the detection of differentially methylated sites (DMS)
between groups.
Table 1 | Description of historical modes of subsistence and current habitat of populations in the study.
Population Sampling location(s) Historical mode
of subsistence
Language
family
Current habitat/lifestyle N*NwNz
w-RHG Baka Lomie
´-Messok,
Salapoumbe, Oveng-Djoum,
Southeast Cameroon
Hunter-gatherers Ubangi Villages in the equatorial rainforest. Slash-and-burn
agriculture, subsistence farming, hunting and gathering
in the equatorial forest
78 73 68
w-RHG Baka Minvoul, Northeast Gabon Hunter-gatherers Ubangi Villages in the equatorial rainforest. Slash-and-burn
agriculture, subsistence farming, hunting and gathering
in the equatorial forest
34 30 29
e-RHG Batwa Southwest Uganda Hunter-
gatherersy
N. Bantu|| Villages near the forest. Subsistence farming, hunting
and gathering in the equatorial forest before settling
47 47 47
w-AGR Nzebi Libreville, Gabon Agriculturalists N. Bantu Urban 55 55 55
w-AGR FangzYaounde
´, Cameroon Agriculturalists N. Bantu Urban 39 39 39
e-AGR Bakiga Southwest Uganda Agriculturalists N. Bantu Villages in rural, deforested areas.
Subsistence farming in stable deforested area.
48 48 48
f-AGR Nzime Lomie
´-Messok, Southeast
Cameroon
Agriculturalists N. Bantu Villages in the equatorial rainforest, shared habitat with
w-RHG Baka from Cameroon (mostly from the Lomie
´
region). Slash-and-burn agriculture, forest hunting
61 60 59
*Sample sizes before normalization and filtering.
wSample sizes, after normalization and filtering, used for methylation analyses.
zSample sizes, after SNP imputation and filtering for low call rates, used for meQTL mapping.
yAlthough, at present, the Batwa RHG do not live in the forest, they hunted and gathered in the Bwindi Impenetrable Forest in southwest Uganda until it became a national park in 1991. All individuals
included in this study were born and raised in the equatorial forest, where they lived in non-permanent camps.
||N. Bantu stands for Narrow Bantu.
zThis sample corresponds to a composite sample of Bantu-speaking individuals from Yaounde
´, mostly belonging to the Fang ethnic group.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047 ARTICLE
NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications 3
that the observed geographic differences are due to technical
factors. To understand the relationship between DNA methyla-
tion variation and differences in subsistence strategies and
habitat, we thus performed all subsequent population compa-
risons within each geographic region separately.
We compared DNA methylation variation between popula-
tions differing in genetic background, historical lifestyle and
current habitat—the RHG and AGR groups living in the
rainforest and rural/urban areas, respectively (Fig. 1c). PCA
clearly separates the RHG and AGR groups on PC1, in both
western (P¼9.9 10 15) and eastern (P¼5.7 10 11) Central
Africa (Fig. 2a,b; Supplementary Fig. 5). We identified 25,820
differentially methylated sites (DMS; located across 8,803 genes)
between w-RHG and w-AGR, and 19,401 DMS (located across
6,288 genes) between e-RHG and e-AGR (false discovery rate
(FDR)o0.01). Interestingly, when comparing the western and
eastern settings, we detected an overlap of 6,844 sites (located
across 2,528 genes) differentially methylated in the same
direction—corresponding to 96% of the overlapping DMS
(resampling Po10 7). Collectively, these findings attest to
strong, shared differences in DNA methylation between RHG
and AGR groups, regardless of their geographic location.
Impact of habitat and lifestyle changes on DNA methylation.
To distinguish the respective effects on DNA methylation of
recent changes in habitat from historical differences in lifestyle
and genetics of these groups, we next compared populations with
a common historical lifestyle and genetic background but dif-
ferent recent habitats, specifically the forest f-AGR and the urban
w-AGR (Fig. 1c). The observed patterns of DNA methylation
variation were accounted for primarily by the habitat in which the
populations live (PC1 P¼3.5 10 4; Fig. 2c), highlighting the
important role of current habitat in determining global DNA
methylation profiles. We found 5,716 DMS (located across 3,550
genes) between the two groups, which we termed ‘recent DMS’.
The differential methylation in the same direction of 3,304 of
these recent DMS (corresponding to 99% of the overlapping
DMS, resampling Po10 7; 2,146 genes) between the more
distantly related w-RHG and w-AGR provided strong evidence in
favour of the methylation status at these shared DMS being
determined by recent changes in habitat independently of
genotypic differences.
Focusing on populations with different historical lifestyles and
genetic backgrounds but with the same current habitat (f-AGR
and w-RHG in the Central African rainforest, Fig. 1c), PCA also
tended to separate the samples with respect to their population
identity (PC1 P¼2.4 10 5; Fig. 2d). We found 4,049 DMS
(located across 2,128 genes) between these groups, which we
termed ‘historical DMS’. Notably, historical DMS presented
larger absolute differences in mean DNA methylation levels
between populations (|Db|, using here b-values instead of
Historical + recent
e-RHG vs e-AGR: 19,401 DMS
Historical + recent
w-RHG vs w-AGR: 25,820 DMS
Historical
w-RHG vs f-AGR: 4,049 DMS
Recent
f-AGR vs w-AGR: 5,716 DMS
PC2 (4.05%)
PC2 (4.71%)
PC2 (4.08%)
PC2 (4.26%)
PC1 (5.55%) PC1 (7.63%) PC1 (5.06%) PC1 (4.89%)
Immune system process
Immune response
Cellular portein metabolic process
Interspecies interaction between organisms
Symbiosis, encompassing mutualism through parasitism
Intracellular transport
Multi-organism cellular process
Viral process
Positive regulation of immune response
Protein metabolic process
Activation of immune response
Single-organism intracellular transport
Positive regulation of immune system process
Immune response-activating signal transduction
Regulation of immune response
Protein binding
RNA binding
Small molecule binding
Poly(A) RNA binding
Nucleotide binding
0123456
–log10(P)
01234
–log10(P)
Molecular function Molecular function
Biological process Biological process
Single-multicellular organism process
Developmental process
Multicellular organismal process
Multicellular organismal development
Cell fate commitment
Single-organism developmental process
Nervous system development
Central nervous system development
System development
Anatomical structure development
Cell–cell signalling
Single organism signalling
Signalling
Synaptic transmission
Cell communication
Sequence-specific DNA binding
Growth factor binding
abcd
f
e
Figure 2 | DNA methylation profiles and functional differentially methylated regions. (a–d) PCA of genome-wide DNA methylation profiles for the
different population comparisons. (e,f) Gene ontology (GO) enrichment analysis for (e) recent DMS and (f) historical DMS. The top GO categories for
biological processes and molecular functions are shown, together with the log-transformed FDR-adjusted enrichment Pvalues.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047
4NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications
M-values, see ref. 34) than recent DMS. In particular, the
proportion of DMS for which |Db| values are 45% was higher for
historical than for recent DMS (Po10 16; Supplementary
Fig. 6a,b). These historical DMS showed no significant overlap
with the recent DMS described above (only 52 DMS were shared).
The set of historical DMS identified thus reflects DNA
methylation variation related to the historical differences in
lifestyle and habitat characterizing the RHG and AGR groups.
Genomic features of differentially methylated regions.To
understand the putative functional implications of DMS, we first
localized them across distinct genomic regions. We found that
recent DMS were enriched in sites located in gene bodies and
distal promoters, while historical DMS were preferentially located
around the transcription start sites (TSS), 50-UTR (untranslated
region) and first exon regions (Supplementary Fig. 7a,c). We next
mapped DMS to histone modification peaks from peripheral
blood mononuclear cells (PBMCs) as mapped by the ENCODE
project35. We found that both recent and historical DMS mapped
in excess to H3K4me1 modification peaks (32% for both DMS
sets versus 20% expected) (Supplementary Fig. 7b,d). Notably, the
recent DMS that were hypermethylated in f-AGR were further
enriched in H3K4me3 peaks (57% versus 27%), while the
historical DMS that were hypermethylated in w-RHG were
enriched in H3K27me3 (32% versus 12%).
Finally, we explored the colocalization of DMS with
TF-binding sites (Methods). We found that recent DMS were
significantly enriched in binding sites of TFs related to cell
differentiation, proliferation and development, but also to
immune regulation (NFIL3, IRF1 and GATA3), and fatty acid
storage and glucose metabolism (HNF1A, RORA and
NR1H2::RXRA) (Supplementary Table 2). Conversely, historical
DMS, particularly those that were hypermethylated in RHG, were
preferentially overlapping binding sites of TF involved in
developmental processes (TFAP2A and NHLH1). Collectively,
these findings indicate that recent and historical DMS not only
represent independent sets, but also are located in distinct
genomic regions that contain different TF-binding sites, sugge-
sting that they are associated to regulatory features related to
different biological functions.
Biological functions of recent and historical DMS differ.We
investigated the relevance of recent and historical DMS for
explaining phenotypic diversity by exploring whether differen-
tially methylated genes in each set were enriched in gene ontology
categories or in genes reported to be associated with traits or
diseases by genome-wide association studies (GWAS). We found
that genes containing recent DMS were enriched in categories
largely associated with immune response, host–pathogen inter-
actions and various cellular processes (Fig. 2e; Supplementary
Table 3). Consistently, recent DMS genes were enriched in genes
reported by GWAS (FDR-corrected resampling Po8.1 10 3),
including autoimmune disorders, such as vitiligo (20 genes
associated versus 10.1 expected, P¼0.045) and systemic lupus
erythematosus (19 genes associated with versus 9.2 expected,
P¼0.028).
Conversely, genes overlapping historical DMS were enriched in
functions almost exclusively related to developmental processes,
including multicellular organismal development, anatomical
structure development, or growth factor binding (Fig. 2f;
Supplementary Table 3). In contrast to recent DMS, historical
DMS genes were not enriched in genes reported by GWAS. We
also found that 1,302 historical DMS (699 genes) overlapped with
the DMS detected in western (w-AGR versus w-RHG) and
eastern (e-RHG versus e-AGR) comparisons, in the same
direction (corresponding to 99% of the overlapping DMS,
resampling Po10 7), despite the splitting of the RHG groups
B20,000 years ago8,28,30. This common set of historical DMS was
again enriched in functions primarily related to development
(Supplementary Table 4). We thus identified a gene set in which
epigenomic variation reflected differences in the lifestyle and
habitat, as well as in genetic background, of RHGs and AGRs,
regardless of their geographic location.
Because recent DMS were found to be particularly enriched
in functions related to immune processes, we next evaluated
the extent to which potential variability in blood cell proportions,
despite our adjustments for cell count heterogeneity (Supple-
mentary Note 3), may still affect our findings. No major
differences in immune cell counts were observed between the
populations compared (Supplementary Fig. 8; Supplementary
Note 4). Furthermore, when using a ‘filtered’ data set, in which we
removed a set of 51,386 probes that have been shown to correlate
with cell counts by an independent study36, we found that the
biological functions associated with recent and historical DMS
clearly differed and were primarily associated with host–pathogen
interactions/cellular processes and development, respectively
(Supplementary Table 5; Supplementary Note 4), confirming
the results obtained using the global data set.
Genetic contribution to DNA methylation variation. To assess
the contribution of genetic variation to the DNA methylation
levels, we mapped meQTLs, focusing our analyses on SNPs
located in cis within a 200-kb window around the target site
(Methods; Supplementary Fig. 9). We identified 45,916 DNA
methylation sites (B13% of all sites) associated with a nearby
meQTL, in at least one population, with a FDR set to 1%. The
majority of meQTLs (B90%) were shared across populations,
with only 1,283 and 500 meQTLs detected exclusively in the RHG
and AGR groups, respectively. Such extensive sharing of meQTLs
reflects the closer genetic proximity of the populations studied
here and the use of a different cellular model, with respect to
previous studies23,25 (Supplementary Fig. 10; Supplementary
Table 6; Supplementary Note 5).
We next tested the potential enrichment of differentially
methylated regions in associations with genotype variants, with
respect to all DNA methylation sites. We found a moderate
enrichment in DMS characterizing the western (16%, (odds ratio)
OR ¼1.5, s.e. ¼0.02; resampling Po10 7) and eastern compar-
isons (12%, OR ¼1.1, s.e. ¼0.02; resampling P¼1.2 10 2),
where populations differ in both historical and recent lifestyles
and habitats (Fig. 3a). Furthermore, historical DMS were strongly
enriched in meQTLs (30%, OR ¼3.5, s.e. ¼0.03; resampling
Po10 7), whereas recent DMS were depleted in these associa-
tions (9%, OR ¼0.80, s.e. ¼0.05; resampling Po10 7). These
findings were replicated using the ‘filtered’ data set
(Supplementary Note 4), indicating that the potential presence
of blood cell heterogeneity is unlikely to account for these
observations.
We also found that the proportions of DMS associated with
meQTLs were systematically higher in historical than in recent
DMS, irrespective of the mean differences in DNA methylation
levels between populations (Supplementary Fig. 6c). In addition,
the proportion of the variance of DNA methylation accounted for
by meQTLs (R2) was higher for meQTLs associated with
historical DMS (B11%) than for meQTLs related to recent
DMS (6.6%), the R2values obtained being significantly higher
and lower, respectively, than for all meQTLs (Fig. 3b). Consistent
with all our previous observations, historical DMS were more
strongly associated with genotypic differences, which had also a
larger effect, than the remaining sets of DMS.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047 ARTICLE
NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications 5
Two scenarios can explain the observed associations between
historical DMS and DNA sequence variants. In the large majority
of cases (B96%), DNA methylation differences were accounted
for by meQTLs detected in all populations but with differences in
allelic frequency between the RHG and AGR groups (Fig. 3c–e;
see Supplementary Fig. 11 for more examples). More rarely
(B4%), genetic variants appeared to correlate with DNA
methylation differences only in some populations, indicating
interactions with other genetic variants and/or the environment
(G GorGE interactions; Fig. 3f).
To validate our findings and evaluate the possibility that
despite our stringent filtering criteria (Methods), unknown
genetic variants in the methylation probe sequence may still
drive some of these associations, we compared our array findings
to bisulfite pyrosequencing of a selected group of DMS associated
with a meQTL (that is, IGF2BP2,HOXC6,ZNF492, 6p12.3,
DOCK1,COL23A1,RORA and ADAM28). We observed, in all
cases, a very good correlation between the DNA methylation
levels measured by pyrosequencing and the array (Pearson’s
R¼0.74–0.94) as well as a good agreement between the two
methods (Supplementary Figs 12 and 13). Our results were
verified by an independent method, where we confirmed both the
differences in methylation levels and the association with
meQTLs for these probes, thus suggesting that unfiltered genetic
0.4
0.3
0.2
0.1
0.0
Proportion of meQTL-associated sites
All sites (365,401)
***
*** ***
***
*** *** ***
***
25,820
19,401
5,716
4,049
f(A) = 0.211
w-AGR f-AGRw-RHG w-AGR
w-AGR
w-AGR
f-AGR
f-AGR e-AGR
f-AGR
w-RHG
w-RHG e-RHG
w-RHG
w-AGR
f-AGR
w-RHG
f(A) = 0.5 f(A) = 0.458
f(T) = 0.613
w-AGR
w-AGR
f-AGR
f-AGR
w-RHG w-AGR f-AGRw-RHG
w-RHG
w-AGR
f-AGR
w-RHG
f(T) = 0.394 f(T) = 0.364 f(G) = 0.139 f(G) = 0.309 f(G) = 0.288
f(C) = 0.577 f(C) = 0.255 f(C) = 0.246
M-value (cg23956648)
M-value (cg21582112)
0.5
0.0
–0.5
–1.0
0.0 1.5
0.0 1.5
AA AG GG AA AG GG AA AG GG
rs16860216
(IGF2BP2)
1.0
0.5
0.0
–0.5
AA CA CC AA CA CC AA CA CC
rs3889697
(HOXC6)
–1.5 0.0
–1.5 0.0
<
β
<
β
<
β
<
β
CC TC TT CC TC TT CCTC TT
rs937078
(ZNF492)
GG GT TT GG GT TT GG GT TT
rs1534362
(6p12.3 – enhancer)
M-value (cg23053977)
3.5
3.0
2.5
2.0
1.5
1.0
M-value (cg09314196)
3
2
1
0
–1
1.0
0.8
0.6
0.4
0.2
0.0
meQTL R2
NS
NS NSNS
ab
cd
ef
w-RHG vs w-AGR (Historical + recent) e-RHG vs e-AGR (Historical + recent) w-AGR vs f-AGR (Recent) w-RHG vs f-AGR (Historical)
Figure 3 | Contribution of genetic variation to the DNA methylation levels. (a) Proportion of methylation sites that are associated with a nearby genetic
variant (in grey) and among different DMS sets (in colour). The numbers in the bars correspond to the total number of DMS per population comparison.
Pvalues were calculated by resampling. (b) Proportion of the variance of DNA methylation explained by nearby genetic variants (R2) for the various meQTL
sets, in each population. The Pvalues (Mann–Whitney U-test) obtained indicate a significant skew in the R2distribution of the various meQTL–DMS sets
(in colour) with respect to that of all meQTLs (in grey) in the corresponding population. R2values are higher for meQTLs associated with historical DMS
(11.5% (10.7–12.3%) and 10.0% (8.9–11.2%) in w-RHG and f-AGR, respectively) than for those related to recent DMS (6.5% (5.7–7.2%) and 6.8%
(6.1–7.4%) in w-AGR and f-AGR, respectively). NS, not significant, *Po0.05, **Po0.01, ***Po0.001. (c–f) Examples of meQTLs detected in this study. The
three boxplots on the left represent the distribution of M-values as a function of genotype. The minor allele frequency of each meQTL is presented for each
population. Red lines indicate the fitted linear regression model for M-value Bgenotype for each population. The forest plots on the right represent the
estimated b, corresponding to the slope of the linear regression, for each population. (c–e) meQTLs detected in all populations but presenting different
allelic frequencies between RHG and AGR groups. The mean F
ST
values between w-RHG and f-AGR/w-AGR groups for the SNPs concerned were higher
(0.15, 0.19 and 0.10, respectively) than that observed genome wide (F
ST
o0.03). (f) Population-specific meQTL, where the SNP rs1534362 is associated
with methylation differences in the enhancer region at 6p12.3 only in RHGs.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047
6NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications
variation on the 450 K array is unlikely to have contributed to the
global patterns of DNA methylation observed.
Signals of positive selection targeting meQTLs. Finally, we
explored the adaptive significance of meQTLs using three metrics
that detect positive selection signals: F
ST
, which compares the
variance of allele frequencies within and between populations37;
the locus-specific branch length (LSBL), which uses pairwise
calculations of F
ST
from three or more populations to detect
population-specific changes in allele frequency38; and the
integrated haplotype score (iHS), which is based on the degree
of extended haplotype homozygosity39. We found that meQTLs
were significantly enriched in high F
ST
and LSBL values with
respect to the remainder of genome-wide SNPs located in the
vicinity of a methylation probe, in nearly all population
comparisons involving the RHG and AGR groups (Fig. 4a,b).
In addition, LSBL analysis revealed that the enrichment in signals
of RHG–AGR population differentiation detected at meQTLs is
particularly observed in AGR populations. Likewise, meQTLs
were significantly enriched in high 7iHS7among AGR groups,
suggesting more recent events of positive selection targeting
regulatory variation in these groups (Fig. 4c). Collectively, these
findings suggest that positive selection has targeted DNA
sequence variants that influence—directly or indirectly—
variation in DNA methylation.
Discussion
Dissecting the means by which populations have responded, and
conceivably adapted, to environmental cues associated with
changes in subsistence strategies and ecological habitats is key
to understand the mechanisms underlying natural phenotypic
variation. Studies of genetic adaptation of African populations,
including hunter-gatherers such as ‘pygmies’, Hadza, Sandawe
and San, have detected selection signals in genes related to
morphology, diet and immune response, and shown that most of
these signals are unique to each population group1,5,7,40–42. These
studies have increased our knowledge of how populations might
have genetically adapted to their respective environments.
However, the impact that temporal changes in subsistence
strategies and habitat, together with genetic diversity, have on
epigenetic variation remains unexplored, despite it can inform
about additional mechanisms of human responses to
environmental challenges. Our findings show that recent and
historical changes in habitat and lifestyle have both critical
impacts on DNA methylation variation, with differences in the
functions affected and the degree of genetic control.
One possible limitation of our study is the measurement of
DNA methylation from whole blood36, which could reflect
population differences in the abundance of cell types, particularly
when it comes to compare populations being exposed to different
environmental challenges (that is, those used to detect recent
DMS). Indeed, a diverse set of environmental factors, including
air pollution, exposure to carcinogens and socioeconomic status,
have been shown to affect DNA methylation in blood cells16,43,44.
Environmental variables can also alter blood cell proportions,
but it remains unclear whether changes in DNA methylation are
the cause or the consequence of such cellular patterns15.
Although we cannot completely rule out a partial effect of cell
composition, we adopted stringent measures to control for it
(Supplementary Notes 3 and 4). These analyses support the
conclusion that variability in blood cell subtypes should not have
a major effect on our findings (for example, replication of
both the differences in biological functions between recent
and historical DMS and enrichment in genetic control of
historical DMS), and suggest a series of important biological
implications.
First, we show that recent changes in habitat, such as those
experienced by agriculturalist populations living in urban/rural
areas or in the rainforest, can substantially alter the methylome of
** ** *** *** *** ***** ** ****
NS NS NS NS NS NS
1.3 1.3
1.25 1.25
1.2 1.2
1.15 1.15
1.1 1.1
1.05 1.05
11
0.95
0.9
0.85
0.8
1.3
1.25
1.2
1.15
1.1
1.05
1
0.95
0.9
0.85
0.8
Odds ratio (FsT)
Odds ratio (LSBL)
Odds ratio (iHS)
w-AGR
w-AGR w-AGR
w-AGR
e-AGR e-AGR
e-AGR
f-AGR f-AGR
f-AGR
f-AGR
w-RHG
w-RHG
w-RHG
w-RHG
e-RHG
e-RHG
e-RHG
San
San
San
San
abc
Figure 4 | Selection signals at genetic variants associated to DNA methylation levels. (a,b) Odds ratios measuring the enrichment in high (a)F
ST
and
(b) LSBL values among meQTLs, with respect to the remainder of genome-wide SNPs located in a 20-kb window surrounding each methylation probe, in
the different population comparisons. Pvalues were calculated using a Cochran–Mantel–Haenszel test, stratified by derived allele frequencies. The colours
in the plots correspond to the (a) population comparisons and (b) genetic distances shown in the schematic trees below each plot. (c) Odds ratios
measuring the enrichment in high |iHS| values for the different meQTL data sets (in colour). Pvalues were estimated using a w2-test. For F
ST
, LSBL and |iHS|,
we considered only SNPs with an LD r2o0.8. NS, not significant, *Po0.05, **Po0.01, ***Po0.001.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047 ARTICLE
NATURE COMMUNICATIONS | 6:10047 | DOI: 10.1038/ncomms10047 | www.nature.com/naturecommunications 7
genetically homogeneous populations, indicating that most of
their divergence in DNA methylation is unlikely to be explained
by underlying genetic differences. Such epigenetic alterations
affect principally immune functions and processes involved in
host–pathogen interactions and cellular metabolism. This is
consistent with previous findings based on gene expression
variation in Moroccan populations, where immunity is the most
altered function in urban populations, as compared with rural
and nomadic groups45. We also find that differentially methylated
regions between urban and forest-based farmers are particularly
enriched in genes associated with autoimmune disorders,
suggesting that urbanization likely has an influence on
susceptibility to immunity-related disorders, as previously
hypothesized for allergies and inflammatory bowel disease46,47.
Although the underlying mechanisms remain unknown,
highlighting the need of additional studies of DNA methylation
variation using purified cell types and tissues, our results suggest
functional links between DNA methylation variation and
environmentally triggered phenotypes, owing to a combination
of biotic, abiotic and cultural factors associated with increasing
urbanization and modern lifestyles.
Second, when comparing rainforest hunter-gatherers and
farmers who share the same forest environment—a setting that
minimizes the effects that recent environmental changes have had
on methylation—we find that DNA methylation differences
related to historical factors mostly reside in genes with functions
in developmental processes. Furthermore, such differences in
DNA methylation profiles are strongly associated with nearby
genetic variants, the frequency of which differs between hunter-
gatherer and farmer groups. This is the case, for example, for
meQTLs in genes such as IGF2BP2,HOXC6 and ZNF492
(Fig. 3c–e), which have been associated with height, age at
menarche, type-2 diabetes, bone mineral density and gene–diet
interactions48–52. We also observe cases of population-specific
effects of DNA methylation variation, such as that of the 6p12.3
enhancer region that was hypomethylated in rainforest hunter-
gatherers and under genetic control only in this group (Fig. 3f).
Our analyses identify a gene set showing extensive methylation
differences between human groups that started to diverge at least
45,000 years ago—a division corresponding to the second deepest
divergence among African populations7,8,28,30. In specific cases,
we provide a link between DNA methylation variation, genetic
diversity and phenotypic traits. For example, the SNP-meQTL
detected for IGF2BP2 (Fig. 3c), as well as those detected at nine
other loci, have been directly identified as presenting the strongest
association signals for various phenotypes, including height, by
GWAS (Supplementary Data 1). In doing so, our study motivates
further work to understand the mechanistic links between the
patterns of epigenetic variation observed and the extensive
phenotypic diversity characterizing African populations.
Third, we show that genetic variants associated with DNA
methylation variation are enriched in signals of positive selection.
That these signals appear to be more pronounced among
agriculturalist populations, both in the western and eastern
settings, suggests the occurrence of increased local adaptation
targeting regulatory variation in these human groups. One of the
most iconic phenotypes distinguishing rainforest hunter-gath-
erers and farmers is small body size26, the genetic and adaptive
bases of which are increasingly recognized. Recent studies have
reported several independent loci with adaptive alleles that appear
to correlate with height, supporting a scenario of convergent
evolution related to the African ‘Pygmy’ phenotype5,40–42.
Among the candidate loci proposed, the CISH–MAPKAPK3–
DOCK3 region in chromosome 3 presents both signals of
selection and association with height40. Specifically, genetic
variation at DOCK3 has been associated with height in
Europeans52 and, together with CISH, which is involved in the
human growth hormone pathway, presents a suggestive
association in a combined RHG–AGR sample40. Furthermore,
variants of CISH have been associated with susceptibility to
infectious disease, including tuberculosis and malaria, in several
African populations53.
Interestingly, we find that CISH,MAPKAPK3 and DOCK3 are
differentially methylated between populations, owing to meQTLs
that show strong population differentiation between rainforest
hunter-gatherers and farmers (F
ST
¼0.17–0.23, with longer
branch lengths among RHG, among the 5% highest of the
genome). Likewise, the height-associated SNP rs16860216 at
IGF2BP2 (ref. 52), which we also find to control methylation
variation, presents strong allele frequency differences between
groups (F
ST
¼0.15, with longer branch length among AGR,
among the 5% highest of the genome). Collectively, these results
provide new insight into how DNA methylation variation might
have participated, through its association with genetic variants, to
adaptive phenotypes, including the Pygmy phenotype,
broadening our understanding of hunter-gatherer and farmer
evolutionary ecology.
In summary, this study substantially increases our under-
standing of the relative impacts that population genetic variation
and differences in lifestyles and ecologies have on the human
epigenome, and illustrates the utility of DNA methylation as a
marker to track variation in regulatory activity following
environmental change. Furthermore, our findings suggest that
populations can initially respond to environmental challenges via
epigenetic changes, uncoupled from variation in the DNA
sequence, with the adaptive phenotype increasingly being
achieved via genetic changes as time passes. We thus provide a
basis for further experimental and theoretical studies assessing the
role of epigenetic mechanisms in human adaptation over different
time scales.
Methods
Population samples.We studied peripheral whole blood DNA from a total of 381
samples, corresponding to 362 individuals and 19 replicate samples, from seven
populations located across the Central African belt (Fig. 1a; Table 1). These
populations can be divided into two main groups: RHG populations, historically
known as ‘pygmies’, who have traditionally relied on the equatorial forest for
subsistence and who live close to, or within, the forest; and AGR populations, living
either in rural/urban deforested regions or in forested habitats in which they
practice slash-and-burn agriculture. The w-RHG sample consisted of 112 Baka
from Minvoul (Gabon) and the regions of Oveng-Djoum, Lomie
´-Messok, and
Salapoumbe (Cameroon). Given the highly similar methylation and genetic profiles
of the Baka individuals from Cameroon and Gabon (Fig. 1b; Supplementary
Fig. 5a,c), and their residence in the same ecological habitat (Table 1), we pooled
these samples in a single group. The e-RHG sample consisted of 47 unrelated
Batwa from the surroundings of the Bwindi Impenetrable Forest in southwest
Uganda, all of whom were born in the forest42. The w-AGR sample contained 55
Nzebi from Libreville (Gabon) and 39 Fang from Yaounde
´(Cameroon). Again,
based on the similarity of their methylation and genetic profiles (Fig. 1b;
Supplementary Fig. 5b,c) and habitats (Table 1), these samples were merged into a
single group. The e-AGR sample contained 48 Bakiga from the surroundings of the
Bwindi Impenetrable Forest in southwest Uganda42. We also analysed an AGR
sample of 61 Nzime from Messok (Cameroon) (referred to as f-AGR), who were
recruited on the basis of their frequent practice of hunting in the forest traditionally
inhabited by the w-RHG sample.
Further details about the modes of subsistence of these populations, their
habitats and sample sizes, before and after filtering, are provided in Table 1.
Informed consent was obtained from all participants and from both parents of any
participants under the age of 18. Ethical approval for this study was obtained from
the institutional review boards of Institut Pasteur, France (RBM 2008-06 and
2011-54/IRB/3), Makerere University, Uganda (IRB 2009-137) and University of
Chicago, USA (16986A).
Genotyping data.Of the 362 individuals included in this study, 191 had already
been genotyped by Illumina Omni1 in two previous studies10,42. This consisted of
46 w-RHG, 15 e-RHG, 29 w-AGR, 31 e-AGR and 21 f-AGR individuals from ref.
10, and 34 e-RHG and 15 e-AGR individuals from ref. 42. The remaining 171
samples—105 w-RHG, 26 w-AGR and 40 f-AGR individuals—were genome-wide
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms10047
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genotyped using the Illumina OmniExpress for 719,665 SNPs. We filtered out
7,120 SNPs on the basis of their physical location (that is, those on the
Y-chromosome and SNPs unmapped on dbSNP build 37), problematic genotype
clusters in GenomeStudio (Illumina, San Diego) based on a GenTrain score o0.35,
and SNP call rate o95%. We also filtered out two w-RHG individuals with a call
rate o95% and eight individuals presenting cryptic relatedness (that is, kinship
coefficient 40.15 with another individual), with the KING program54.
We phased the 191 Omni1 individuals with SHAPEIT2 (ref. 55) and imputed
missing SNPs in the OmniExpress data set, using the Omni1 data set as a reference,
with IMPUTE2 (ref. 56). Five samples (4 w-RHG and 1 f-AGR) with call rates
o95% after imputation were removed. After filtering out low-quality imputed
SNPs and SNPs with call rate o95% after imputation, we obtained a final set of
genotypes at 876,886 SNPs for 347 individuals, comprising 98 w-RHG, 94 w-AGR,
60 f-AGR, 47 e-RHG and 48 e-AGR individuals. To evaluate imputation accuracy,
we compared the concordance of genotyped and imputed SNPs with whole-
genome sequences from 20 w-RHG (Baka) and 20 w-AGR (Nzebi) studied here,
obtained by Illumina HiSeq 2000 at an average coverage of 5.6 (17,080,726
SNPs, unpublished data). SNP calling of next-generation sequencing data was
performed with GATK57. We kept SNPs passing a sensitivity threshold (VQSR
tranche) of 99.9%, with a confidently called reference allele, passing Hardy–
Weinberg equilibrium and found in genomic regions of ‘strict callability’ (as
defined by the 1000 Genomes Project Consortium58) and limited evidence of
identity-by-descent (IBD). Average concordance rate was 97.2% (individual range:
94.6–99.6%) and 96.5% (individual range: 94.2–98.6%) for genotyped and imputed
SNPs, respectively. Finally, we had to remove another two individuals because of
their methylation profiles (see the ‘DNA methylation data processing’ section),
yielding a final data set of 345 individuals for whom we had both genotype and
methylation data.
Genome-wide DNA methylation analysis.Genome-wide DNA methylation data
at more than 485,000 sites was obtained using an Infinium HumanMethylation450
BeadChip. Bisulfite conversion of 750 ng of genomic DNA was performed with the
EZ DNA Methylation Kit. Successful conversion was confirmed by methylation-
specific PCR before proceeding with subsequent steps of the Infinium assay
protocol. The bisulfite-converted genomic DNA was isothermally amplified at
37 °C for 22 h, enzymatically fragmented, purified and hybridized with the
HumanMethylation450 BeadChip at 48 °C for 18 h. Each BeadChip was then
washed to remove any unhybridized or non-specifically hybridized DNA. Labelled
single-base extension was performed with bead-bound probes hybridized to the
DNA, and the hybridized DNA was removed. The extended probes were stained
with multiple layers of fluorescence, and the BeadChip was then coated with a
proprietary solution and scanned with the Illumina iScan system. Raw data were
processed with Genome Studio Methylation Module software.
Targeted pyrosequencing.Bisulfite PCR-pyrosequencing assays were designed
with PyroMark Assay Design 2.0 (Qiagen). The regions of interest (IGF2BP2
cg23956648, HOXC6 cg21582112, ZNF492 cg09314196, 6p12.3 enhancer region
cg23053977, DOCK1 cg06406458, COL23A1 cg08684511, RORA cg09879458, and
ADAM28 cg18757155) were amplified by PCR, using the HotstarTaq DNA poly-
merase kit (Qiagen) as follows: 15 min at 95 °C (to activate the Taq polymerase),
45 cycles of 95 °C for 30 s, 58 °C for 30 s and 72 °C for 30 s, with a final 5-min
extension step at 72 °C. For pyrosequencing, a single-stranded DNA was prepared
from the PCR product with the Pyromark Vacuum Prep Workstation (Qiagen),
and sequencing was performed with sequencing primers on a Pyromark Q96 MD
pyrosequencer (Qiagen). Methylation levels were calculated for each CpG dinu-
cleotide with Pyro Q-CpG software (Qiagen). The primer sequences are listed in
Supplementary Table 7.
DNA methylation data processing.In total, 381 samples were hybridized with
the HumanMethylation450 array, including 362 unique samples and 19 technical
replicates. We removed probes that potentially cross-hybridize59, those on the
X and Y chromosomes, and those containing SNPs, or associated with CpGs
containing SNPs, at a frequency higher than 1% in at least one of the studied
populations. The list of SNPs was based on (i) our own genotyping data set for
more than 876,886 SNPs genome-wide (see ‘Genotyping data’ section), and (ii) the
whole-genome sequencing data set for 20 w-AGR and 20 w-RHG individuals
mentioned above. Following this filtering process, 365,886 of the original 485,512
sites on the array were retained. We calculated methylation levels from raw data,
using the R bioconductor lumi package. The M-value has been shown to provide
better detection sensitivity than b-values at extreme levels of modification34.We
therefore used the M-value unless otherwise stated. M-values were then adjusted
for background and colour bias with lumi, and quantile normalized. We corrected
for technical differences between Type I and Type II assay designs, by performing
subset-quantile within array normalization on M-values with the R bioconductor
minfi package60. PCA showed that a batch effect explained part of the variance
(Kruskal–Wallis Pvalue of 8.35 10 55 for PC2) of the normalized data, and we
used the ComBat function from the sva bioconductor package to correct for this
effect61. Two samples (1 w-RHG and 1 f-AGR) were removed because they
presented a clear excess of hemi-methylated sites.
Accounting for age and heterogeneity in cell subtypes.To account for the
potential confounding introduced by age and cellular heterogeneity in whole blood,
we first estimated these variables in all samples. Ages were estimated from
methylation data for all samples, with an elastic net regression model32, and the
estimated ages were compared with the ages declared, when these were available
(Supplementary Note 2). To account for cellular heterogeneity, we used a
reference-based method in which the DNA methylation signature of each of the
principal types of immune cells (granulocytes, monocytes, B cells, CD4 þT cells,
CD8 þT cells and NK cells) was used to predict the proportions of these cell types
in unfractionated whole blood33. Predictions for white blood cell types were
obtained by applying the ‘estimateCellCounts’ function of the minfi package60 to
the normalized b-values. This function was modified slightly to accept a matrix of
b-values rather than an RGSet object. The resulting estimated cell counts were
rescaled to 1. We also determined the relative proportions of various cell subtypes
(CD4 þT cells, CD8 þT cells, B cells and NK cells) among the PBMCs of 35
e-RHG and 31 e-AGR subjects, by fluorescence-activated cell sorting (FACS;
Supplementary Note 3). Note that the set of probes that were used to predict
heterogeneity in blood cell composition33 were removed, yielding a final set of
365,401 probes that were used in all the subsequent analyses. Estimated ages and
cell subtype heterogeneity across populations were then used to adjust M-values for
all analyses, including principal component analyses, the estimation of
differentially methylated sites and the mapping of meQTLs.
Determination of differentially methylated sites.Sites differentially methylated
between populations (DMS) were identified statistically, by fitting a linear regres-
sion model for each site (M-values Bpopulation þsex þage þcell type
proportions þerror), and applying empirical Bayes smoothing to the s.e.’s, with the
R bioconductor limma package62. Sites with a Benjamini and Hochberg adjusted
Po0.01 were considered to be differentially methylated. To define the amplitude of
DMS, we used different criteria: a Benjamini and Hochberg adjusted Pvalue o0.01
and a difference in mean methylation level between the two populations of more
than 2, 5 or 10%. For this analysis, methylation level was determined as the ratio of
methylated probe intensity to overall intensity, the b-value34. We extracted the
overlaps between different DMS sets and calculated the Pvalues measuring the
probability of these overlaps being obtained by chance, using 107resamplings.
DNA methylation levels at targeted sites are strongly correlated within regions of
about 2,000 bp20. Thus, for each DMS list, we randomly resampled the same
number of sites from all 365,401 sites, taking into account the distance between the
DMS.
Genomic features of differentially methylated sites.We analysed the enrich-
ment in target sites of particular genomic regions, by calculating an OR, defined as
follows:
OR ¼PRjDMSðÞ
Pnot RjDMSðÞ
Pnot Rjnot DMSðÞ
PRjnot DMSðÞ
with Rbeing ‘in the region’.
Genic regions were defined according to the UCSC_REFGENE_GROUP
column from the Illumina HumanMethy450 annotation: distal promoter (from
1,500 to 200 bp upstream from the TSS), proximal promoter (less than 200 bp
upstream from the TSS), 50UTR, first Exon, Gene Body and 30UTR. Histone
modification peak data for H3k4me1, H3K4me3, H3K9me3 and H3K27me3, which
correspond to the histone marks for which data was available for PBMCs, were
downloaded from the ENCODE website (http://genome.ucsc.edu/ENCODE/). A
site was considered to colocalize with a histone modification mark if it falls into the
region defined as a ‘narrow peak’ (FDR of 0.01). TF-binding sites affinity scores for
sequences of 30 bp around each methylation site were obtained using the TRAP
software63 and the position weight matrix of 85 human TFs from JASPAR64.For
each TF, a site was considered to have a high affinity if it fell into the top fifth
percentile of the score distribution. Pvalues for enrichment in genomic positions,
histone marks or TF-binding sites among recent and ancient DMS were obtained
using a w2-test.
Biological functions of differentially methylated genes.We extracted all
differentially methylated genes, defined as genes carrying at least one DMS. We
used the goseq R bioconductor package to perform an analysis of the
over-representation of gene ontology categories65 among differentially methylated
genes. We fed the number of probes corresponding to each gene into the
probability weighting function of the goseq package. As not all the genes of the
genome are represented on the Illumina HumanMethy450 BeadChip, our reference
set in the over-representation analysis consisted of the 19,672 genes for which we
had data. DMS sets were significantly enriched in a given category if the
FDR-adjusted Pvalue was o0.05.
Mapping of meQTLs.We identified meQTLs with a Bayesian statistical frame-
work implemented in the eQtlBma package, which was specifically designed for the
detection of QTLs jointly in multiple subgroups66. We filtered out SNPs with an
allele frequency below 10% in all populations. Age, sex and the proportions of the
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various cell types were used as covariates in the linear model. In addition, we
included the first PC obtained from genotyping data as a covariate, to correct for
varying degrees of AGR ancestry across individuals within RHG populations. We
then estimated the genome-wide weight of each configuration (Supplementary
Table 6) using eqtlbma_hm and the default grids provided by the eQtlBma package
as a priori for the hierarchical model. The probability of a methylation site having
no meQTL (p0) was estimated by the EBF method67, and various posterior
probabilities were calculated with eqtlbma_avg_bfs. We then extracted all the
methylation sites with at least one meQTL at an FDR of 0.01 (ref. 68). We
identified the best-associated SNPs, defined as all SNPs for which the sum of
posterior probabilities for being the best-associated SNP, assuming that the site was
associated with only one SNP, was at least 0.85. For most sites with several SNPs
associated with high posterior probabilities, the best configurations (that is, the
combination of populations in which the SNP was a meQTL) were identical for all
the SNPs. In the 2,469 cases in which there were at least two configurations, the
best configuration was chosen by looking directly at the association. The 155 cases
for which there were more than two different configurations were discarded from
the list of significant meQTL-associated sites.
We calculated the proportion of historical DMS either associated with meQTLs
presenting strong differences in allele frequency between the populations compared
(that is, high F
ST
) or reflecting G G/G E interactions (that is, meQTLs that are
detected only in some populations) using an analysis of variance
(M¼population þgenotype þpopulation genotype). We thus obtained the
proportion of the variance in DNA methylation levels explained by each factor and
their corresponding Pvalues. After adjustment for multiple testing, using a
Benjamini and Hochberg correction, we considered that a meQTL-associated DMS
reflected G G/G E interactions when Po0.01 for the population genotype
factor.
Detection of positive selection.To detect mutations presenting signals of
positive selection, we used the analysis of molecular variance-based F
ST
(ref. 37),
the LSBL38 and the haplotype-based iHS39. For LSBL, we choose the Ju/’hoansi
Khoe-San as outgroup, because genetic distances between this population and RHG
and AGR groups were similar. We merged our imputed SNP genotyping data set
with the HumanOmni2.5 data set of the Khoe-San from Schlebusch and
colleagues7, and kept 664,661 shared SNPs that presented neither allele mismatches
nor allele frequency discordances (determined by comparing w-AGR with south-
African Bantu speakers). To measure the enrichment in high F
ST
and LSBL among
meQTLs, we compared the proportions of high F
ST
or LSBL values (defined as the
5% highest values genome wide) between meQTLs and all the remaining SNPs
located in a 20-kb window centred on each HumanMethylation450K probe.
Statistical significance was tested with a Cochran–Mantel–Haenszel test, stratifying
data by bin of derived allele frequencies (from 0 to 1, in 0.1 steps). iHS values were
computed for our entire set of 876,886 SNPs, and normalized by bin of derived
allele frequencies (from 0 to 1, in 0.025 steps) in each of the five populations
separately (w-RHG, w-AGR, f-AGR, e-RHG and e-AGR). Ancestral states of the
SNPs were determined using the sequence provided by the 1000 Genomes
Project58. We used a w2-test to compare the proportion of high |iHS| values
(defined as the 5% highest |iHS| values genome wide) between meQTLs and
all the remaining SNPs located in a 20-kb window centred on each Human-
Methylation450K probe. We filtered out SNPs with LD r2values 40.8 in each pair
of populations merged, for F
ST
, or in each population separately, for LSBL and iHS,
using plink69.
Annotation using data from genome-wide association studies.For all sets of
DMS genes and meQTLs, we explored their implication in human diseases and
traits using hits of GWAS, obtained from the 02/06/2015 version of the NHGRI
database, which we manually modified to include two recent GWAS of height52
and age at menarche51. Only GWAS signals with Pvalues o510 8were
considered. We used two approaches; a gene-based approach and a SNP-based
approach. The gene-based approach relies on the simple fact that a DMS gene is
the reported gene of a GWAS hit. A set of nDMS genes is considered enriched in
GWAS genes if the proportion of DMS GWAS genes in this set is larger than
in 95% of 10,000 randomly sampled sets of ngenes. Genes are randomly
sampled from all genes that have at least one methylation probe in the Human-
Methylation450 BeadChip, and are matched to the observed number of probes per
gene observed in the tested set. We also tested if sets of DMS genes were enriched
in genes associated to individual diseases/traits. Pvalues were obtained by
resampling. Only diseases/traits that were associated with more than five DMS
genes were considered.
The second SNP-based approach evaluates if meQTLs correspond to, or are in
strong linkage disequilibrium (r240.8) with, SNPs reported as best GWAS hits.
For each set of meQTLs, we first removed all SNPs in LD using plink (‘--indep-
pairwise 50 5 0.8’)69. We next retrieved SNPs in strong linkage disequilibrium with
any of these SNPs, using the correlation coefficient implemented in plink calculated
in our imputed genotyping data set. We then obtained the proportion of GWAS
best signals among meQTLs and SNPs in LD with them. To test for enrichments in
GWAS hits, we estimated this proportion, using the same procedure, in 10,000
random samples of independent SNPs that were selected to be close to methylation
probes.
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Acknowledgements
We thank Vincent Colot, Etienne Danchin, Jean-Philippe Fortin, Tatiana Giraud, Aure
´lie
Labbe, Guillaume Laval and Carla Saleh for feedback on data analyses and reading of the
manuscript. We also thank Martin Sikora and Carlos Bustamante for providing the
variant calling of whole-genome sequencing data. We are grateful to all the study par-
ticipants for their generous contributions of DNA. This study was funded by the Institut
Pasteur, the CNRS, a CNRS ‘MIE’ (Maladies Infectieuses et Environnement) Grant, and a
Foundation Simone & Cino del Duca Research Grant (L.Q.-M.), and the Canadian
Institute for Advanced Research (CIFAR) (M.S.K.). M.J.J. was supported by a Mininig for
Miracles post-doctoral fellowship from the Child and Family Research Institute. L.B.B is
supported by the Canada Research Chairs Program. M.S.K. is the Canada Research Chair
in Social Epigenetics and a Senior Fellow of CIFAR.
Author contributions
L.Q.-M. conceived and supervised the study. M.F. designed the analysis strategy and
analysed the data, with input from E.P., M.R., M.J.J., M.S.K., L.B.B and L.Q.-M. T.F., M.R.
M.J.J. and K.J.S. provided support for the analysis strategy and statistical methods. J.L.M.,
L.M.M. and M.S.K. contributed DNA methylation data and performed targeted pyr-
osequencing. H.Q. and C.H. assisted with the genetic analyses. A.F., E.H., A.G., E.B.,
P.M.-D., J.-M.H., G.H.P and L.B.B contributed to sample collection. L.B.B contributed
FACS data. M.F, E.P. and L.Q.-M. wrote the manuscript, with input from all authors.
Additional information
Accession codes: The genotyping data generated in this study have been deposited in the
European Genome-Phenome Archive under accession codes EGAS00001000605,
EGAS00001000908 and EGAS00001001066. The DNA methylation data generated in
this study have been deposited in the European Genome-Phenome Archive under
accession code EGAS00001001066.
Supplementary Information accompanies this paper at http://www.nature.com/
naturecommunications
Competing financial interests: The authors declare no competing financial interests.
Reprints and permission information is available online at http://npg.nature.com/
reprintsandpermissions/
How to cite this article: Fagny, M et al. The epigenomic landscape of African rainforest
hunter-gatherers and farmers. Nat. Commun. 6:10047 doi: 10.1038/ncomms10047
(2015).
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