There is mounting evidence that climate change affects biological and ecological processes and puts strong selective pressure on natural. This raises two main questions regarding crop plants:
- how will climate change affect the phenotypic and genetic diversity of crop species and their wild relatives,
- what type of material should we produce to withstand the new climate regime.
These questions are particularly important in developing countries where human populations mainly rely on traditional rain fed cropping systems. To accurately predict responses of crop plants and their wild relatives to future environmental changes, we need to learn more about the genetic architecture of adaptation but also about the adaptive trajectory of natural/artificial populations under heterogeneous and/or changing environments. For instance, the role of specific evolutionary factors such as mutation, migration and recombination in adaptation remains to be understood. Moreover, as several crop species reproduce through selfing, understanding the specific impact of the mating system on adaptation routes and genetic mechanisms also constitutes an important issue.
The rise of genomics paves the way to tackle these challenges. For an increasing number of plant species, new sequencing and high-throughput genotyping technologies allow the study of patterns of genetic variation at hundreds of loci. Such data enable to make inferences about population structure and demographic processes (genetic drift, migration, frequency of recombination) and provide a multilocus null distribution of variation across the genome that can be used to reliably detect footprints of selection in candidate genes. Applied to samples comparing cultivated forms and their wild progenitors, this genome scan approach has enabled tremendous progress in our knowledge about the genetic architecture of domestication, which can be considered as an example of adaptation to cultivation and human needs. Few applications of this approach to samples collected on climatic or environmental gradients were available at the beginning of this project, asking for both empirical data and theoretical developments in this area.