RP07: Natural DNA methylation variation in European Thlaspi arvense populations

  • PhD Supervisor(s): Oliver Bossdorf, Niek Scheepens
  • Host Institution: Eberhard Karls University of Tübingen, Tübingen, Germany
  • Duration: 36 months
  • Fixed start date: 1 April 2018
  • Planned secondment(s): ecSeq Bioinformatics, Leipzig (GE); Gregor Mendel Institute, Vienna (AT); Philips University Marburg (GE)

There is currently much speculation about the extent, stability and ecological relevance of epigenetic variation in natural plant populations. How much epigenetic variation is there, and how is it structured? Do patterns of epigenetic variation mirror those of genetic variation, or do they tell a different story? How much of the variation observed in the field is plastic responses to different environments, and how much is stably inherited and thus potentially adaptive? So far, we cannot answer these questions well enough because there have been too few systematic large-scale surveys of epigenetic variation in natural plant populations.

The aim of this research project will be to conduct such a study in the annual plant and common crop weed Thlaspi arvense (field pennycress). The PhD student will take part in a sampling campaign across Europe and set up a garden experiment, will screen DNA methylation in field and garden plants to assess epigenetic population structure and the stability of DNA methylation differences, and will analyse relationships between epigenetic variation, climate and other environmental factors. The project will be carried out in close collaboration with the group of Claude Becker at the Gregor Mendel Institute in Vienna (RP12).

A unique aspect of this project is the breadth of its methods and of the training that the student will receive. Besides the training in field and experimental ecology, and the training opportunities through the central EpiDiverse summer schools, there will be several intensive training stays (secondments) with different EpiDiverse partners to learn modern bioinformatic and statistical methods for epigenome analyses.

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