Organisms need to adapt quickly to changes in their environment. Mutations in the DNA sequence of genes can lead to new adaptations, but this can take many generations. Instead, altering how genes are switched on by changing how the DNA is packaged in cells can allow organisms to adapt within and between generations. One way that genes are controlled in organisms is by a process known as DNA methylation, where ‘methyl’ tags are added to DNA and act as markers for other proteins involved in activating genes.

DNA is made of four different molecules called ‘nucleotides’ that are arranged in different orders to produce a vast variety of DNA sequences. One type of DNA methylation can happen at sites where a nucleotide called cytosine is followed by two other non-cytosine nucleotides. Another type of methylation can take place at sites where a cytosine is followed by a guanine nucleotide. However, it is not clear how big a role DNA methylation plays in allowing organisms to adapt to their changing environment.

Here, Dubin, Zhang, Meng, Remigereau et al. studied DNA methylation in a plant called Arabidopsis thaliana. Several different varieties of A. thaliana plants from Sweden were grown at two different temperatures. The experiments showed that the A. thaliana plants grown at higher temperatures were more likely to have methyl tags attached to sections of DNA called transposons, which are able to move around the genome. There was a lot of variety in the levels of this DNA methylation in the different plants, and some of it was shown to be associated with variation in a gene that is involved in DNA methylation.

However, not all of the DNA methylation in these plants was sensitive to the temperature the plants were grown in. Dubin, Zhang, Meng, Remigereau et al. show that the pattern of a type of DNA methylation that is found within genes depends on how far north in Sweden the plants' ancestors came from rather than the temperature the plants were grown in. Plants that originated from colder regions, farther north, had more DNA methylation within many genes and these genes were more active.

These findings suggest that genetic differences in these plants strongly influence the levels of DNA methylation, and they provide the first direct link between DNA methylation and adaption to the environment. Future studies should reveal how DNA methylation is regulated in these plants, and whether it plays a key role in adaptation, or merely reflects other changes in the genome.

To better understand how genotype and environment interact to affect DNA methylation and transcription, we grew 150 Arabidopsis thaliana accessions from Sweden (Long et al., 2013) in two different environments, 10°C and 16°C, chosen because they lead to very different flowering behavior (Atwell et al., 2010). Relying on existing genome sequence information (Long et al., 2013), methylome- and transcriptome-sequencing data were generated (see ‘Materials and methods’).

In plants, DNA methylation occurs on cytosines in the CG, CHG, and CHH contexts (where H is any nucleotide except for C), each of which is catalyzed by independent pathways (Finnegan et al., 1998; Stroud et al., 2014). Consistent with previous results (Vaughn et al., 2007; Eichten et al., 2013; Schmitz et al., 2013; Li et al., 2014; Seymour et al., 2014; Hagmann et al., 2015) we found considerable variation between accessions regardless of context, even at the level of genome-wide averages (Figure 1A). Temperature, on the other hand, did not appear to affect genome-wide CG or CHG methylation, but had a significant effect on CHH methylation, levels of which were 14% higher at 16°C than at 10°C, on average (Figure 1A). To investigate the genetic basis of DNA methylation, we performed genome-wide association studies (GWAS) using different facets of average methylation as the phenotype. For global CG and CHG methylation, no associations reached genome-wide significance, while for CHH methylation a clear peak of association was observed on chromosome 4 (Figure 1—figure supplement 1). The association was even more significant when restricting attention to average CHH methylation of large transposons (Figure 1B), in agreement with the notion that this type of methylation mostly occurs in transposons in Arabidopsis (Stroud et al., 2013).

Figure 1 with 2 supplements see all

Download asset Open asset

The association centered around a SNP at 10,459,127 on chromosome 4, 38 kb downstream from the locus AT4G19020, which encodes a homolog of the CHG methyltransferase chromo-methylase-3 (Lindroth et al., 2001) that has recently been shown to catalyze both CHH and CHG methylation on transposons, and is thus an excellent candidate (Zemach et al., 2013; Stroud et al., 2014). A multi-locus mixed model (Segura et al., 2012) that included the identified SNP (CMT2a) as a fixed effect revealed another SNP downstream of CMT2, at position 10,454,628 (CMT2b), 4.5 kb closer to CMT2 than CMT2a, and in complete linkage disequilibrium with it (i.e., the non-reference alleles at CMT2a and CMT2b are never seen together). Repeating the GWAS with both CMT2a and CMT2b as cofactors identified no further loci (Figure 1—figure supplement 2). Both non-reference alleles are common in southern Sweden, but are also found in the north (22.6% vs 9.5% and 30.6% vs 7.9% for CMT2a and CMT2b, respectively). Accessions with the non-reference CMT2a allele have on average more CHH methylation on transposons than those with the reference haplotype (p = 1.1e-03), while those with the non-reference CMT2b allele have lower levels of CHH methylation than the reference haplotype (p = 8.1e-03; Figure 1C). The associations were readily confirmed using an F2 population generated by crossing accessions with the CMT2a and CMT2b non-reference alleles (Figure 2). No significant differences in CMT2 mRNA levels were observed between the alleles in our data and limited Sanger sequencing of cDNA showed no evidence of splicing variants (although, as will be discussed below, we did detect a putative rare null allele). Several non-synonymous polymorphisms in the methyltransferase and BAH domains of CMT2 were detected (Supplementary files 1 and 2) but they do not explain the phenotype as well as the CMT2a and CMT2b SNPs.

Figure 2

Download asset Open asset

The effect of genetic variation on local CHH methylation was examined by calculating the methylation level in 200 bp sliding windows across the genome (100 bp overlap between windows) and running GWAS for the 200,000 differentially methylated regions (DMRs; see ‘Materials and methods’) that varied most between individuals. 36023 DMRs had at least one genome-wide significant association (Bonferroni-corrected p-value of 0.05; 7273 remain after correcting for multiple GWAS using an FDR of 0.05). 45% (15,031) of the DMRs had a significant cis-association within 100 kb, while the rest showed evidence of trans-regulation, including the dramatic effect of CMT2 on chromosome 4 which accounted for approximately 21% (7392) of all significant associations (Figure 3A).

Figure 3

Download asset Open asset

To quantify the regulation of DMRs, we partitioned the variance of CHH methylation into environmental (E), CMT2, CMT2 X E, cis, cis X E, trans, and trans X E using a mixed model (Figure 3B). The analysis confirmed substantial cis and trans associations, with the environment modulating the genetic effects rather than having a major direct effect. At least for the cis associations, a possible explanation is that SNPs tag polymorphic TE insertions, with the insertion allele being silenced in a temperature-sensitive manner.

The effect of temperature on CHH methylation could also be seen at the local level. We defined ‘temperature DMRs’ by looking for windows that differed significantly between temperatures. Comparing 16°C–10°C, each accession on average gained CHH methylation at ∼400 temperature DMRs and lost it at ∼200 temperature DMRs (false discovery rate = 0.05). CHH methylation is associated with transposable elements (TEs; Finnegan et al., 1998), and in agreement with this, 79% of the temperature DMRs where methylation was gained at 16°C were located within 500 bp of an annotated TE (with 60% directly overlapping one). These temperature DMRs were enriched in a small subset of TEs (835, or 2.7% of total, permutation based p-value = 0.05) that were more highly methylated than other transposons, but with lower methylation levels immediately adjacent (Figure 4A). Compared to TEs without temperature DMRs, these ‘variable’ TEs also tended to be euchromatic (Figure 4B), highly expressed (Figure 4C), and recently inserted (‘evolutionarily young’ TE insertions for which orthologs are not present in Arabidopsis lyrata [Zhong et al., 2012] comprised 75% of the variable TEs vs 68% of non-variable TEs). At the super-family level, members of the SINE, SINE-like, Helitrons and Mutator-like DNA TE superfamilies were over-represented among the variable transposons, and at the family level, 36 families were over-represented, including the AtREP, Vandal and HAT DNA transposons, as well as COPIA78/ONSEN and META1 retroelements (Table 1). Interestingly, COPIA78 has been shown to become active in response to heat stress (Pecinka et al., 2010; Ito et al., 2011) apparently due to heat-shock promoter elements in its LTR regions (Cavrak et al., 2014).

Figure 4

Download asset Open asset

In order to gain further insight into the mechanisms responsible for variation in CHH methylation, we bisulfite-sequenced knockout lines of CMT2 (SAIL_906_G03) and DCL3 (dcl3-5 [Daxinger et al., 2009], a component of the RdDM pathway), and identified 10,138 DCL3-dependant DMRs and 33,422 CMT2-dependent DMRs as described in section ‘DMR calling on DNA methylation mutants’ of the ‘Materials and methods’. As expected under the assumption that CMT2 is responsible for the massive GWAS peak on chromosome 4, the GWAS peak at this locus remains if we consider only the CMT2-dependent DMRs, but not for DCL3-dependent DMRs (Figure 5—figure supplement 1). Furthermore, while CHH methylation varied with temperature at both DCL3- and CMT2-dependent DMRs (Figure 5), DCL3-dependent DMRs were much more strongly associated with previously identified temperature DMRs (4703 out of 10,138 DCL3-dependent DMRs, or 46%, overlapped temperature-sensitive DMRs, whereas the corresponding numbers for CMT2-dependent DMRs were 2299 out of 33,422, or 7%; Fisher's exact p-value < 2.2e-16), suggesting that much of the temperature variation in CHH methylation is due to components of the RdDM pathway. This result is consistent with previous findings showing that RNA silencing is less active at lower temperatures (Romon et al., 2013).

Figure 5 with 2 supplements see all

Download asset Open asset

Interestingly, we observed one accession from northern Sweden, TAA-03, with almost undetectable levels of CHH methylation at CMT2-dependant DMRs (Figure 5). Further investigation suggested that it has a deletion or rearrangement in CMT2, as we were unable to map reads between positions 2813 and 4944 (intron 7 to exon 16, Figure 5—figure supplement 2). Sanger-sequencing indicates the insertion of a stretch of TC dinucleotide repeats of at least 330 bp. The same deletion appears to be present in three more accessions from northern Sweden (TAA-14, TAA-18, and Gro-3) a situation reminiscent of the homologous CMT1 gene, which seems to be non-functional in most Arabidopsis accessions (Henikoff and Comai, 1998). Although CMT2 null alleles have no obvious phenotype, the gene is highly conserved in plants (with the exception of maize; Zemach et al., 2013; West et al., 2014).

It has recently been suggested that natural variation in CMT2 is associated with adaptation to climate (Shen et al., 2014), although the alleles identified in that study do not overlap with the ones identified here. Given the sensitivity of CHH methylation to growth temperature observed here, we next investigated the correlation between DNA methylation and the climate of origin (Hancock et al., 2011). While CHH methylation was moderately correlated with photosynthetically active radiation (PAR) in spring (Pearson's r = 0.38), and CHG showed correlation with aridity (r = 0.35) and the number of frost-free days (Pearson's r = 0.30), by far the strongest signal was a strong positive correlation between CG methylation and latitude (Pearson's r = 0.70), as well as with a number of environmental variables that co-vary with latitude in our sample, such as minimum temperature and daytime length (Table 2, Figure 6A). As a result of the strong latitudinal correlation, accessions originating from northern Sweden (minimum temperature below −10°C) had on average 11% higher global CG methylation compared to those from the south (Figure 6A). The correlation appears to be driven by gene body methylation (GBM): as the correlation for CG methylation on transposons was much weaker (Figure 6A, Figure 6—figure supplement 1). Because the methylation variation observed for genes with average CG methylation below 5% appeared mostly to be noise (Figure 6—figure supplement 2, see also the ‘Materials and methods’ section), we classified genes into ‘unmethylated’ and ‘having GBM’ using this as a cutoff (5%). We also eliminated genes showing a transposon-like pattern of methylation in which not only CG, but also the CHH and CHG contexts are highly methylated (Zemach et al., 2013). In what follows, we use GBM to refer only to gene body CG methylation for this filtered set. In order to better understand the observed variation in GBM, we examined CG methylation at the single nucleotide resolution within GBM containing genes. Although methylation was detectable (using a cut-off of 1%) at a similar number of sites in the north and south (1085292 vs 1079443 CG sites), those in the north showed a distinct skew towards higher methylation levels (Figure 6—figure supplement 3). Likewise when the difference between average methylation levels in the north and south was calculated individually for each of the cytosines, the majority of cytosines showed a small increase in the north compared to the south (Figure 6—figure supplement 4). From this we concluded that there is a general small increase in methylation of most CG dinucleotides in GBM genes, rather than large changes in a specific subset. GBM primarily occurs on long, evolutionary conserved genes that tend to be moderately-to-highly expressed, and is positively correlated with gene expression (Zilberman et al., 2007; Takuno and Gaut, 2012). Genes with higher GBM tended to be more highly expressed in our data as well, and—more interestingly—accessions with higher average GBM showed slightly higher average expression of methylated genes (although the correlation was weak, Figure 6—figure supplement 5). Given that northern accessions had higher GBM, this meant that genes with GBM were on average more highly expressed in northern than in southern accessions, while unmethylated genes showed little difference (Figure 6B). GBM has previously been shown to be anti-correlated with temperature-dependent gene expression (Kumar and Wigge, 2010). While no large-scale north-south expression differences were observed between 10°C and 16°C in our data, northern accessions showed considerably less variation in expression between the two temperatures for genes with GBM (Wilcoxon p-value = 1.2e-05), while no such difference was observed for genes without it (Figure 6—figure supplement 6).

Figure 6 with 6 supplements see all

Download asset Open asset

As for CHH DMRs, the genetic basis of GBM was examined using a variance-component approach (Figure 7A). The results were dramatically different: relative to CHH methylation, the trans effects for GBM are massive, and the environment appears to have no effect (in agreement with the observation that only CHH methylation levels vary with temperature, see Figure 1A). To identify the genes responsible, we also performed GWAS for each gene with GBM (Figure 7B). A total of 3241 significant associations were found for 2315 genes. 43% of these genes had a significant cis-association (within 100 kb of the gene of interest), which could represent local variants affecting methylation directly, or indirectly by affecting gene expression (Gutierrez-Arcelus et al., 2013). No evidence for major trans-acting loci like CMT2 was found, but 69% of all significant associations were in trans. A comparison of the direction of the effect of GBM-associated SNPs in cis and trans revealed a striking pattern (Figure 7C). While the non-reference alleles of cis-SNPs were 1.18 times more likely to be associated with decreased rather than increased GBM (p = 2.01e-04), the non-reference alleles of trans-SNPs were 3.48 times more likely to be associated with increased GBM (p = 2.2e-16), and the non-reference alleles at the 15 trans-SNPs that were associated with GBM at five or more genes were always positively correlated (Figure 7C). Furthermore, while cis-SNPs showed a wide distribution of allele frequencies similar to random SNPs, trans-SNPs showed a much more limited distribution of frequencies (Figure 8A) and were also much more strongly correlated with latitude (Figure 8B,C). The correlation between GBM and latitude thus appears mostly to be due to trans-acting SNPs.

Figure 7

Download asset Open asset

Figure 8 with 1 supplement see all

Download asset Open asset

The 15 highly associated trans-SNPs were largely limited to northern Sweden, and were in strong linkage disequilibrium with each other (Figure 8—figure supplement 1). A. thaliana from northern Sweden show signs of multiple strong selective sweeps (Long et al., 2013) and harbors many polymorphisms that appear to be involved in local adaptation (specifically to minimum temperature; Hancock et al., 2011). The 15 SNPs were more than ninefold over-represented in the previously identified sweep regions (empirical p-value = 5.1e-03) and over fivefold over-represented within 2 kb of SNPs in the 1% tail of those associated with minimum temperature (Hancock et al., 2011) (empirical p-value = 3.1e-04), (Table 3). The ancestral state could be determined for 10 of the 15 SNPs, and in 8 of these cases, the non-reference allele was derived, further supporting sweeps in northern Sweden.

That the difference in GBM between north and south is likely to reflect local adaptation is also clear from its relative magnitude. The north vs south divide explains a much higher fraction of the additive genetic variance for GBM (Qst = 0.772; see ‘Materials and methods’) than of the SNP variance (Fst = 0.187). This strongly suggest that the phenotypic differentiation is driven by selection rather than genetic drift (Leinonen et al., 2013).

Identifying the causal variants is challenging, a gene-ontology analysis of genes within 100 kb (the average size of the sweep regions, Long et al., 2013), of the 15 trans-SNPs found enrichment of loci associated with mRNA transcription (GO0009299, p-value = 2.62e-03). Genes involved in epigenetic processes are not captured well by standard gene-ontology, but we found that genes from the plant chromatin database (www.chromdb.org/) were significantly overrepresented in these regions as well (permutation p-value = 0.012; Table 4).

We also looked for genes whose expression variation is consistent with a causal role. We identified 68 genes within 100 kB of one of the 15-trans SNPs whose expression is highly correlated (Wilcoxon test p < 0.001) with the adjacent SNP after correction for population structure (Table 5). No significant enrichment of Gene Ontology terms was observed among these genes, and manual inspection identified no proteins directly involved in DNA methylation. Instead, a number of proteins involved in the regulation of gene expression and/or chromatin accessibility were present (Table 5). This may suggest that the increased gene-body methylation observed in the north is not directly due to increased DNA methylation, but may be caused by increases in gene expression driven either by differences in transcription factors networks or chromatin compaction. Identification of the causal variants behind this phenomenon should provide insight into how plants adapt to their local environment.

In conclusion, genes with GBM are generally up-regulated and more heavily methylated in northern accessions, and the change appears to be due to trans-acting polymorphisms that have been subject to directional selection. The candidate regions show an overrepresentation of genes involved in transcriptional processes. We also found that CHH methylation of TEs is temperature sensitive, and identified a major trans-acting controller, CMT2. Taken together, these observations suggest that local adaptation in A. thaliana involves genome-wide changes in fundamental mechanisms of gene regulation, perhaps as a form of temperature compensation.

1. 1. Dubin MJ
   2. Nordborg M

   (2014) DNA methylation variation in Arabidopsis has a genetic basis and appears to be involved in local adaptation

   Publicly available at NCBI Gene Expression Omnibus GSE54292.

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54292
2. 1. Osborne EJ
   2. Zhang P
   3. Remigereau MS
   4. Drewe P
   5. Rätsch G
   6. Nordborg M
   7. Clark RM

   (2014) DNA methylation variation in Arabidopsis has a genetic basis and appears to be involved in local adaptation

   Publicly available at NCBI Gene Expression Omnibus GSE54680.

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54680
3. 1. Dubin MJ
   2. Nordborg M

   (2014) Population epigenomics of Swedish Arabidopsis

   Publicly available at NCBI Gene Expression Omnibus GSE65685.

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65685
4. 1. Dubin MJ
   2. Nordborg M

   (2014) Effect of Chromomethylase-2 and Dicer-like-3 knockouts on DNA methylation

   Publicly available at NCBI Gene Expression Omnibus GSE66017.

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66017
5. 1. Long Q
   2. Rabanal FA
   3. Meng D
   4. Huber CD
   5. Farlow A
   6. Platzer A
   7. Zhang Q
   8. Vilhjálmsson BJ
   9. Korte A
   10. Nizhynska V
   11. Voronin V
   12. Korte P
   13. Sedman L
   14. Mandáková T
   15. Lysak MA
   16. Seren Ü
   17. Hellmann I
   18. Nordborg M

   (2013) Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden

   Publicly available at Gregor Mendel Institute of Molecular Plant Biology.

   http://plone.gmi.oeaw.ac.at/downloads/nordborg/data-release-for-massive-genomic-variation-and-strong-selection-in-arabidopsis-thaliana-lines-from-sweden

1. 1. Akalin A
   2. Kormaksson M
   3. Li S
   4. Garrett-Bakelman FE
   5. Figueroa ME
   6. Melnick A
   7. Mason CE

   (2012) methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

   Genome Biology 13:R87.

   https://doi.org/10.1186/gb-2012-13-10-r87

   • Google Scholar
2. 1. Anders S
   2. Huber W

   (2010) Differential expression analysis for sequence count data

   Genome Biology 11:R106.

   https://doi.org/10.1186/gb-2010-11-10-r106

   • Google Scholar
3. 1. Arends D
   2. Prins P
   3. Jansen RC
   4. Broman KW

   (2010) R/qtl: high-throughput multiple QTL mapping

   Bioinformatics 26:2990–2992.

   https://doi.org/10.1093/bioinformatics/btq565

   • Google Scholar
4. 1. Atwell S
   2. Huang YS
   3. Vilhjalmsson BJ
   4. Willems G
   5. Horton M
   6. Li Y
   7. Meng D
   8. Platt A
   9. Tarone AM
   10. Hu TT
   11. Jiang R
   12. Muliyati NW
   13. Zhang X
   14. Amer MA
   15. Baxter I
   16. Brachi B
   17. Chory J
   18. Dean C
   19. Debieu M
   20. de Meaux J
   21. Ecker JR
   22. Faure N
   23. Kniskern JM
   24. Jones JD
   25. Michael T
   26. Nemri A
   27. Roux F
   28. Salt DE
   29. Tang C
   30. Todesco M
   31. Traw MB
   32. Weigel D
   33. Marjoram P
   34. Borevitz JO
   35. Bergelson J
   36. Nordborg M

   (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines

   Nature 465:627–631.

   https://doi.org/10.1038/nature08800

   • Google Scholar
5. 1. Bhatia G
   2. Patterson N
   3. Sankararaman S
   4. Price AL

   (2013) Estimating and interpreting FST: the impact of rare variants

   Genome Research 23:1514–1521.

   https://doi.org/10.1101/gr.154831.113

   • Google Scholar
6. 1. Broman KW
   2. Wu H
   3. Sen S
   4. Churchill GA

   (2003) R/qtl: QTL mapping in experimental crosses

   Bioinformatics 19:889–890.

   https://doi.org/10.1093/bioinformatics/btg112

   • Google Scholar
7. 1. Cavrak VV
   2. Lettner N
   3. Jamge S
   4. Kosarewicz A
   5. Bayer LM
   6. Mittelsten Scheid O

   (2014) How a retrotransposon exploits the plant's heat stress response for its activation

   PLOS Genetics 10:e1004115.

   https://doi.org/10.1371/journal.pgen.1004115

   • Google Scholar
8. 1. Chen GB

   (2014) Estimating heritability of complex traits from genome-wide association studies using IBS-based Haseman-Elston regression

   Frontiers in Genetics 5:107.

   https://doi.org/10.3389/fgene.2014.00107

   • Google Scholar
9. 1. Daxinger L
   2. Kanno T
   3. Bucher E
   4. van der Winden J
   5. Naumann U
   6. Matzke AJ
   7. Matzke M

   (2009) A stepwise pathway for biogenesis of 24-nt secondary siRNAs and spreading of DNA methylation

   The EMBO Journal 28:48–57.

   https://doi.org/10.1038/emboj.2008.260

   • Google Scholar
10. 1. Dinh HQ
    2. Dubin M
    3. Sedlazeck FJ
    4. Lettner N
    5. Mittelsten Scheid O
    6. von Haeseler A

    (2012) Advanced methylome analysis after bisulfite deep sequencing: an example in Arabidopsis

    PLOS ONE 7:e41528.

    https://doi.org/10.1371/journal.pone.0041528

    • Google Scholar
11. 1. Eichten SR
    2. Briskine R
    3. Song J
    4. Li Q
    5. Swanson-Wagner R
    6. Hermanson PJ
    7. Waters AJ
    8. Starr E
    9. West PT
    10. Tiffin P
    11. Myers CL
    12. Vaughn MW
    13. Springer NM

    (2013) Epigenetic and genetic influences on DNA methylation variation in maize populations

    The Plant Cell 25:2783–2797.

    https://doi.org/10.1105/tpc.113.114793

    • Google Scholar
12. 1. Finnegan EJ
    2. Genger RK
    3. Peacock WJ
    4. Dennis ES

    (1998) DNA methylation in plants

    Annual Review of Plant Physiology and Plant Molecular Biology 49:223–247.

    https://doi.org/10.1146/annurev.arplant.49.1.223

    • Google Scholar
13. 1. Gutierrez-Arcelus M
    2. Lappalainen T
    3. Montgomery SB
    4. Buil A
    5. Ongen H
    6. Yurovsky A
    7. Bryois J
    8. Giger T
    9. Romano L
    10. Planchon A
    11. Falconnet E
    12. Bielser D
    13. Gagnebin M
    14. Padioleau I
    15. Borel C
    16. Letourneau A
    17. Makrythanasis P
    18. Guipponi M
    19. Gehrig C
    20. Antonarakis SE
    21. Dermitzakis ET

    (2013) Passive and active DNA methylation and the interplay with genetic variation in gene regulation

    eLife 2:e00523.

    https://doi.org/10.7554/eLife.00523

    • Google Scholar
14. 1. Hagmann J
    2. Becker C
    3. Müller J
    4. Stegle O
    5. Meyer RC
    6. Wang G
    7. Schneeberger K
    8. Fitz J
    9. Altmann T
    10. Bergelson J
    11. Borgwardt K
    12. Weigel D

    (2015) Century-scale methylome stability in a recently diverged Arabidopsis thaliana Lineage

    PLOS Genetics 11:e1004920.

    https://doi.org/10.1371/journal.pgen.1004920

    • Google Scholar
15. 1. Hancock AM
    2. Brachi B
    3. Faure N
    4. Horton MW
    5. Jarymowycz LB
    6. Sperone FG
    7. Toomajian C
    8. Roux F
    9. Bergelson J

    (2011) Adaptation to climate across the Arabidopsis thaliana genome

    Science 334:83–86.

    https://doi.org/10.1126/science.1209244

    • Google Scholar
16. 1. Hansen KD
    2. Langmead B
    3. Irizarry RA

    (2012) BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

    Genome Biology 13:R83.

    https://doi.org/10.1186/gb-2012-13-10-r83

    • Google Scholar
17. 1. Henikoff S
    2. Comai L

    (1998)

    A DNA methyltransferase homolog with a chromodomain exists in multiple polymorphic forms in Arabidopsis

    Genetics 149:307–318.

    • Google Scholar
18. 1. Huber CD
    2. Nordborg M
    3. Hermisson J
    4. Hellmann I

    (2014) Keeping it local: evidence for positive selection in Swedish Arabidopsis thaliana

    Molecular Biology and Evolution 31:3026–3039.

    https://doi.org/10.1093/molbev/msu247

    • Google Scholar
19. 1. Ito H
    2. Gaubert H
    3. Bucher E
    4. Mirouze M
    5. Vaillant I
    6. Paszkowski J

    (2011) An siRNA pathway prevents transgenerational retrotransposition in plants subjected to stress

    Nature 472:115–119.

    https://doi.org/10.1038/nature09861

    • Google Scholar
20. 1. Jean G
    2. Kahles A
    3. Sreedharan VT
    4. De Bona F
    5. Ratsch G

    (2010)

    RNA-seq read alignments with PALMapper

    Current Protocols in Bioinformatics, Chapter 11, Unit 11 16, 10.1002/0471250953.bi1106s32.

    • Google Scholar
21. 1. Kang HM
    2. Sul JH
    3. Service SK
    4. Zaitlen NA
    5. Kong SY
    6. Freimer NB
    7. Sabatti C
    8. Eskin E

    (2010) Variance component model to account for sample structure in genome-wide association studies

    Nature Genetics 42:348–354.

    https://doi.org/10.1038/ng.548

    • Google Scholar
22. 1. Kim S
    2. Plagnol V
    3. Hu TT
    4. Toomajian C
    5. Clark RM
    6. Ossowski S
    7. Ecker JR
    8. Weigel D
    9. Nordborg M

    (2007) Recombination and linkage disequilibrium in Arabidopsis thaliana

    Nature Genetics 39:1151–1155.

    https://doi.org/10.1038/ng2115

    • Google Scholar
23. 1. Kumar SV
    2. Wigge PA

    (2010) H2A.Z-containing nucleosomes mediate the thermosensory response in Arabidopsis

    Cell 140:136–147.

    https://doi.org/10.1016/j.cell.2009.11.006

    • Google Scholar
24. 1. Leinonen T
    2. McCairns RJ
    3. O'Hara RB
    4. Merila J

    (2013) Q(ST)-F(ST) comparisons: evolutionary and ecological insights from genomic heterogeneity

    Nature Reviews Genetics 14:179–190.

    https://doi.org/10.1038/nrg3395

    • Google Scholar
25. 1. Li Q
    2. Eichten SR
    3. Hermanson PJ
    4. Springer NM

    (2014) Inheritance patterns and stability of DNA methylation variation in maize near-isogenic lines

    Genetics 196:667–676.

    https://doi.org/10.1534/genetics.113.158980

    • Google Scholar
26. 1. Lindroth AM
    2. Cao X
    3. Jackson JP
    4. Zilberman D
    5. McCallum CM
    6. Henikoff S
    7. Jacobsen SE

    (2001) Requirement of CHROMOMETHYLASE3 for maintenance of CpXpG methylation

    Science 292:2077–2080.

    https://doi.org/10.1126/science.1059745

    • Google Scholar
27. 1. Lippert C
    2. Casale FP
    3. Rakitsch B
    4. Stegle O

    (2014)

    LIMIX: genetic analysis of multiple traits

    BioRxiv.

    • Google Scholar
28. 1. Liu Y
    2. Siegmund KD
    3. Laird PW
    4. Berman BP

    (2012) Bis-SNP: combined DNA methylation and SNP calling for Bisulfite-seq data

    Genome Biology 13:R61.

    https://doi.org/10.1186/gb-2012-13-7-r61

    • Google Scholar
29. 1. Long Q
    2. Rabanal FA
    3. Meng D
    4. Huber CD
    5. Farlow A
    6. Platzer A
    7. Zhang Q
    8. Vilhjálmsson BJ
    9. Korte A
    10. Nizhynska V
    11. Voronin V
    12. Korte P
    13. Sedman L
    14. Mandáková T
    15. Lysak MA
    16. Seren Ü
    17. Hellmann I
    18. Nordborg M

    (2013) Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden

    Nature Genetics 45:884–890.

    https://doi.org/10.1038/ng.2678

    • Google Scholar
30. 1. Pecinka A
    2. Dinh HQ
    3. Baubec T
    4. Rosa M
    5. Lettner N
    6. Mittelsten Scheid O

    (2010) Epigenetic regulation of repetitive elements is attenuated by prolonged heat stress in Arabidopsis

    The Plant Cell 22:3118–3129.

    https://doi.org/10.1105/tpc.110.078493

    • Google Scholar
31. 1. Romon M
    2. Soustre-Gacougnolle I
    3. Schmitt C
    4. Perrin M
    5. Burdloff Y
    6. Chevalier E
    7. Mutterer J
    8. Himber C
    9. Zervudacki J
    10. Montavon T
    11. Zimmermann A
    12. Elmayan T
    13. Vaucheret H
    14. Dunoyer P
    15. Masson JE

    (2013) RNA silencing is resistant to low-temperature in grapevine

    PLOS ONE 8:e82652.

    https://doi.org/10.1371/journal.pone.0082652

    • Google Scholar
32. 1. Schmitz RJ
    2. Schultz MD
    3. Urich MA
    4. Nery JR
    5. Pelizzola M
    6. Libiger O
    7. Alix A
    8. McCosh RB
    9. Chen H
    10. Schork NJ
    11. Ecker JR

    (2013) Patterns of population epigenomic diversity

    Nature 495:193–198.

    https://doi.org/10.1038/nature11968

    • Google Scholar
33. 1. Sedlazeck FJ
    2. Rescheneder P
    3. von Haeseler A

    (2013) NextGenMap: fast and accurate read mapping in highly polymorphic genomes

    Bioinformatics 29:2790–2791.

    https://doi.org/10.1093/bioinformatics/btt468

    • Google Scholar
34. 1. Segura V
    2. Vilhjálmsson BJ
    3. Platt A
    4. Korte A
    5. Seren Ü
    6. Long Q
    7. Nordborg M

    (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

    Nature Genetics 44:825–830.

    https://doi.org/10.1038/ng.2314

    • Google Scholar
35. 1. Seymour DK
    2. Koenig D
    3. Hagmann J
    4. Becker C
    5. Weigel D

    (2014) Evolution of DNA methylation patterns in the brassicaceae is driven by differences in genome organization

    PLOS Genetics 10:e1004785.

    https://doi.org/10.1371/journal.pgen.1004785

    • Google Scholar
36. 1. Shen X
    2. De Jonge J
    3. Forsberg SK
    4. Pettersson ME
    5. Sheng Z
    6. Hennig L
    7. Carlborg Ö

    (2014) Natural CMT2 variation is associated with genome-wide methylation changes and temperature seasonality

    PLOS Genetics 10:e1004842.

    https://doi.org/10.1371/journal.pgen.1004842

    • Google Scholar
37. 1. Shin JH
    2. Blay S
    3. McNeney B
    4. Graham J

    (2006)

    LDheatmap: An r function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms

    Journal of Statistical Software 16:.

    • Google Scholar
38. 1. Statham AL
    2. Strbenac D
    3. Coolen MW
    4. Stirzaker C
    5. Clark SJ
    6. Robinson MD

    (2010) Repitools: an R package for the analysis of enrichment-based epigenomic data

    Bioinformatics 26:1662–1663.

    https://doi.org/10.1093/bioinformatics/btq247

    • Google Scholar
39. 1. Stroud H
    2. Do T
    3. Du J
    4. Zhong X
    5. Feng S
    6. Johnson L
    7. Patel DJ
    8. Jacobsen SE

    (2014) Non-CG methylation patterns shape the epigenetic landscape in Arabidopsis

    Nature Structural & Molecular Biology 21:64–72.

    https://doi.org/10.1038/nsmb.2735

    • Google Scholar
40. 1. Stroud H
    2. Greenberg MV
    3. Feng S
    4. Bernatavichute YV
    5. Jacobsen SE

    (2013) Comprehensive analysis of silencing mutants reveals complex regulation of the Arabidopsis methylome

    Cell 152:352–364.

    https://doi.org/10.1016/j.cell.2012.10.054

    • Google Scholar
41. 1. Takuno S
    2. Gaut BS

    (2012) Body-methylated genes in Arabidopsis thaliana are functionally important and evolve slowly

    Molecular Biology and Evolution 29:219–227.

    https://doi.org/10.1093/molbev/msr188

    • Google Scholar
42. 1. Vaughn MW
    2. Tanurdzić M
    3. Lippman Z
    4. Jiang H
    5. Carrasquillo R
    6. Rabinowicz PD
    7. Dedhia N
    8. McCombie WR
    9. Agier N
    10. Bulski A
    11. Colot V
    12. Doerge RW
    13. Martienssen RA

    (2007) Epigenetic natural variation in Arabidopsis thaliana

    PLOS Biology 5:e174.

    https://doi.org/10.1371/journal.pbio.0050174

    • Google Scholar
43. 1. West PT
    2. Li Q
    3. Ji L
    4. Eichten SR
    5. Song J
    6. Vaughn MW
    7. Schmitz RJ
    8. Springer NM

    (2014) Genomic distribution of H3K9me2 and DNA methylation in a maize genome

    PLOS ONE 9:e105267.

    https://doi.org/10.1371/journal.pone.0105267

    • Google Scholar
44. 1. Xi Y
    2. Li W

    (2009) BSMAP: whole genome bisulfite sequence MAPping program

    BMC Bioinformatics 10:232.

    https://doi.org/10.1186/1471-2105-10-232

    • Google Scholar
45. 1. Zemach A
    2. Kim MY
    3. Hsieh PH
    4. Coleman-Derr D
    5. Eshed-Williams L
    6. Thao K
    7. Harmer SL
    8. Zilberman D

    (2013) The Arabidopsis nucleosome remodeler DDM1 allows DNA methyltransferases to access H1-containing heterochromatin

    Cell 153:193–205.

    https://doi.org/10.1016/j.cell.2013.02.033

    • Google Scholar
46. 1. Zhong X
    2. Hale CJ
    3. Law JA
    4. Johnson LM
    5. Feng S
    6. Tu A
    7. Jacobsen SE

    (2012) DDR complex facilitates global association of RNA polymerase V to promoters and evolutionarily young transposons

    Nature Structural & Molecular Biology 19:870–875.

    https://doi.org/10.1038/nsmb.2354

    • Google Scholar
47. 1. Zhu LJ
    2. Gazin C
    3. Lawson ND
    4. Pages H
    5. Lin SM
    6. Lapointe DS
    7. Green MR

    (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

    BMC Bioinformatics 11:237.

    https://doi.org/10.1186/1471-2105-11-237

    • Google Scholar
48. 1. Zilberman D
    2. Gehring M
    3. Tran RK
    4. Ballinger T
    5. Henikoff S

    (2007) Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription

    Nature Genetics 39:61–69.

    https://doi.org/10.1038/ng1929

    • Google Scholar

Author details

1. Manu J Dubin

   Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

   Contribution

   MJD, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Pei Zhang, Dazhe Meng and Marie-Stanislas Remigereau

   For correspondence

   manu.dubin@gmi.oeaw.ac.at

   Competing interests

   The authors declare that no competing interests exist.

2. Pei Zhang

   1. Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria
   2. Molecular and Computational Biology, University of Southern California, Los Angeles, United States

   Contribution

   PZ, Acquisition of data, Analysis and interpretation of data

   Contributed equally with

   Manu J Dubin, Dazhe Meng and Marie-Stanislas Remigereau

   Competing interests

   The authors declare that no competing interests exist.

3. Dazhe Meng

   1. Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria
   2. Molecular and Computational Biology, University of Southern California, Los Angeles, United States

   Contribution

   DM, Analysis and interpretation of data

   Contributed equally with

   Manu J Dubin, Pei Zhang and Marie-Stanislas Remigereau

   Competing interests

   The authors declare that no competing interests exist.

4. Marie-Stanislas Remigereau

   Molecular and Computational Biology, University of Southern California, Los Angeles, United States

   Contribution

   M-SR, Acquisition of data

   Contributed equally with

   Manu J Dubin, Pei Zhang and Dazhe Meng

   Competing interests

   The authors declare that no competing interests exist.

5. Edward J Osborne

   Department of Biology, University of Utah, Salt Lake City, United States

   Contribution

   EJO, Analysis and interpretation of data, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

6. Francesco Paolo Casale

   European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom

   Contribution

   FPC, Analysis and interpretation of data, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

7. Philipp Drewe

   1. Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany
   2. Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   PD, Analysis and interpretation of data, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

8. André Kahles

   1. Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany
   2. Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   AK, Analysis and interpretation of data, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

9. Geraldine Jean

   1. Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany
   2. Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   GJ, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

10. Bjarni Vilhjálmsson

    Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

    Contribution

    BV, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

11. Joanna Jagoda

    Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

    Contribution

    JJ, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

12. Selen Irez

    Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

    Contribution

    SI, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

13. Viktor Voronin

    Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

    Contribution

    VV, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

14. Qiang Song

    Molecular and Computational Biology, University of Southern California, Los Angeles, United States

    Contribution

    QS, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

15. Quan Long

    Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

    Contribution

    QL, Provided pre-publication access to genome sequencing data

    Competing interests

    The authors declare that no competing interests exist.

16. Gunnar Rätsch

    1. Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany
    2. Memorial Sloan-Kettering Cancer Center, New York, United States

    Contribution

    GR, Analysis and interpretation of data, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

17. Oliver Stegle

    European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, United Kingdom

    Contribution

    OS, Analysis and interpretation of data, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

18. Richard M Clark

    1. Department of Biology, University of Utah, Salt Lake City, United States
    2. Center for Cell and Genome Science, University of Utah, Salt Lake City, United States

    Contribution

    RMC, Conception and design, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

19. Magnus Nordborg

    1. Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria
    2. Molecular and Computational Biology, University of Southern California, Los Angeles, United States

    Contribution

    MN, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    magnus.nordborg@gmi.oeaw.ac.at

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-7178-9748

• 8,851

  views

• 2,085

  downloads

• 441

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.