bioRxivGenomicsPreprint
Levels and patterns of genetic diversity differ between two closely related endemic Arabidopsis species
Julie JacqueminiD, Nora Hohmann, Matteo Buti, Alberto Selvaggi, Thomas Müller, Marcus A. Koch, Karl J. Schmid
Abstract
Theory predicts that a small effective population size leads to slower accumulation of mutations, increased levels of genetic drift and reduction in the efficiency of natural selection. Therefore endemic species should harbor low levels of genetic diversity and exhibit a reduced ability of adaptation to environmental changes. Arabidopsis pedemontana and Arabidopsis cebennensis, two endemic species from Italy and France respectively, provide an excellent model to study the adaptive potential of species with small distribution ranges. To evaluate the genome-wide levels and patterns of genetic variation, effective population size and demographic history of both species, we genotyped 53 A. pedemontana and 28 A. cebennensis individuals across the entire species ranges with Genotyping-by-Sequencing. SNPs data confirmed a low genetic diversity for A. pedemontana although its effective population size is relatively high. Only a weak population structure was observed over the small distribution range of A. pedemontana, resulting from an isolation-by-distance pattern of gene flow. In contrary, A. cebennensis individuals clustered in three populations according to their geographic distribution. Despite this and a larger distribution, the overall genetic diversity was even lower for A. cebennensis than for A. pedemontana. A demographic analysis demonstrated that both endemics have undergone a strong population size decline in the past, without recovery. The more drastic decline observed in A. cebennensis partially explains the very small effective population size observed in the present population. In light of these results, we discuss the adaptive potential of these endemic species in the context of rapid climate change.
- organism: Arabidopsis
- assay: DNA methylation
- research focus: variant calling
- approach: statistical model
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