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Spontaneous epigenetic variation in the Arabidopsis thaliana methylome

Nature volume 480, pages 245249 (08 December 2011) | Download Citation


Heritable epigenetic polymorphisms, such as differential cytosine methylation, can underlie phenotypic variation1,2. Moreover, wild strains of the plant Arabidopsis thaliana differ in many epialleles3,4, and these can influence the expression of nearby genes1,2. However, to understand their role in evolution5, it is imperative to ascertain the emergence rate and stability of epialleles, including those that are not due to structural variation. We have compared genome-wide DNA methylation among 10 A. thaliana lines, derived 30 generations ago from a common ancestor6. Epimutations at individual positions were easily detected, and close to 30,000 cytosines in each strain were differentially methylated. In contrast, larger regions of contiguous methylation were much more stable, and the frequency of changes was in the same low range as that of DNA mutations7. Like individual positions, the same regions were often affected by differential methylation in independent lines, with evidence for recurrent cycles of forward and reverse mutations. Transposable elements and short interfering RNAs have been causally linked to DNA methylation8. In agreement, differentially methylated sites were farther from transposable elements and showed less association with short interfering RNA expression than invariant positions. The biased distribution and frequent reversion of epimutations have important implications for the potential contribution of sequence-independent epialleles to plant evolution.

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Data deposits

DNA sequencing data are deposited in the European Nucleotide Archive (http://www.ebi.ac.uk/ena/data/view/ERP000902) under accession number ERP000902.


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We are grateful to R. Shaw for providing seeds of the mutation accumulation lines. We thank C. Lanz for help with Illumina sequencing, J. Fitz for help with the Gbrowse implementation, K. Schneeberger for help with DNA mutation analysis, D. Zilberman for providing bisulphite sequencing protocols, and R. Clark, M. Pellegrini, R. Neher and J. Paszkowski for discussion. This work was supported by an HFSP Long-Term Fellowship (D.K.), the Volkswagen Foundation (O.S.), FP7 Collaborative Project AENEAS (contract KBBE-2009-226477), a Gottfried Wilhelm Leibniz Award of the DFG, and the Max Planck Society (D.W.).

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Author notes

    • Claude Becker
    •  & Jörg Hagmann

    These authors contributed equally to this work.


  1. Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany

    • Claude Becker
    • , Jörg Hagmann
    • , Jonas Müller
    • , Daniel Koenig
    •  & Detlef Weigel
  2. Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany

    • Oliver Stegle
    •  & Karsten Borgwardt


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C.B., J.H. and D.W. conceived the study; C.B. performed the experiments; C.B., J.H., J.M., D.K. and O.S. analysed the data; K.B. provided advice on statistical analysis; and C.B. and D.W. wrote the paper with contributions from all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Detlef Weigel.

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