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      DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation

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          Abstract

          Epigenome modulation potentially provides a mechanism for organisms to adapt, within and between generations. However, neither the extent to which this occurs, nor the mechanisms involved are known. Here we investigate DNA methylation variation in Swedish Arabidopsis thaliana accessions grown at two different temperatures. Environmental effects were limited to transposons, where CHH methylation was found to increase with temperature. Genome-wide association studies (GWAS) revealed that the extensive CHH methylation variation was strongly associated with genetic variants in both cis and trans, including a major trans-association close to the DNA methyltransferase CMT2. Unlike CHH methylation, CpG gene body methylation (GBM) was not affected by growth temperature, but was instead correlated with the latitude of origin. Accessions from colder regions had higher levels of GBM for a significant fraction of the genome, and this was associated with increased transcription for the genes affected. GWAS revealed that this effect was largely due to trans-acting loci, many of which showed evidence of local adaptation.

          DOI: http://dx.doi.org/10.7554/eLife.05255.001

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          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.

          DOI: http://dx.doi.org/10.7554/eLife.05255.002

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          Most cited references25

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

          Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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            Estimating and interpreting F ST: The impact of rare variants

            In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F ST as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion regarding how to estimate F ST from available data. Considering this confusion, wide variation in published estimates of F ST for pairs of HapMap populations is a cause for concern. These estimates changed—in some cases more than twofold—when comparing estimates from genotyping arrays to those from sequence data. Indeed, changes in F ST from sequencing data might be expected due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of F ST that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating F ST for a single SNP, (2) combining estimates of F ST across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in F ST estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions.
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              Requirement of CHROMOMETHYLASE3 for maintenance of CpXpG methylation.

              Epigenetic silenced alleles of the Arabidopsis SUPERMAN locus (the clark kent alleles) are associated with dense hypermethylation at noncanonical cytosines (CpXpG and asymmetric sites, where X = A, T, C, or G). A genetic screen for suppressors of a hypermethylated clark kent mutant identified nine loss-of-function alleles of CHROMOMETHYLASE3 (CMT3), a novel cytosine methyltransferase homolog. These cmt3 mutants display a wild-type morphology but exhibit decreased CpXpG methylation of the SUP gene and of other sequences throughout the genome. They also show reactivated expression of endogenous retrotransposon sequences. These results show that a non-CpG DNA methyltransferase is responsible for maintaining epigenetic gene silencing.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                2050-084X
                05 May 2015
                2015
                : 4
                : e05255
                Affiliations
                [1 ]deptGregor Mendel Institute, Austrian Academy of Sciences , Vienna Biocenter , Vienna, Austria
                [2 ]deptMolecular and Computational Biology , University of Southern California , Los Angeles, United States
                [3 ]deptDepartment of Biology , University of Utah , Salt Lake City, United States
                [4 ]deptEuropean Molecular Biology Laboratory, European Bioinformatics Institute , Wellcome Trust Genome Campus , Cambridge, United Kingdom
                [5 ]deptFriedrich Miescher Laboratory , Max Planck Society , Tübingen, Germany
                [6 ]Memorial Sloan-Kettering Cancer Center , New York, United States
                [7 ]deptCenter for Cell and Genome Science , University of Utah , Salt Lake City, United States
                University of Geneva Medical School , Switzerland
                University of Geneva Medical School , Switzerland
                Author notes
                [* ]For correspondence: manu.dubin@ 123456gmi.oeaw.ac.at (MJD);
                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-7178-9748
                Article
                05255
                10.7554/eLife.05255
                4413256
                25939354
                25b6c659-c7a9-48d3-9f08-a3e950658127
                © 2015, Dubin et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 20 October 2014
                : 26 March 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, universityNational Human Genome Research Institute (NHGRI);
                Award ID: P50HG002790
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council (ERC);
                Award ID: 268962
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission;
                Award ID: Marie Curie FP7 fellowship 253524
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission;
                Award ID: European Community Framework Programme 7 283496
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, universityNational Institutes of Health (NIH);
                Award ID: GM07464
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund (FWF);
                Award ID: M1369
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Genomics and Evolutionary Biology
                Plant Biology
                Custom metadata
                2.3
                The adaptation of Arabidopsis plants to the local environment is linked to DNA methylation.

                Life sciences
                epigenetics,population genetics,local adaptation,dna methylation,arabidopsis
                Life sciences
                epigenetics, population genetics, local adaptation, dna methylation, arabidopsis

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