bioRxivEvolutionary BiologyPreprint
Genomic clustering by geography not species in taxonomically complex British and Irish eyebrights (Euphrasia)
Y. Ding, C. Metherell, W. Huang, P. M. Hollingsworth, A. D. Twyford
Abstract
Genomic studies of incipient speciation are fundamental to understand the origin and establishment of species. However, a wide range of evolutionary processes and complex evolutionary interactions remain to be explored outside of genetically tractable evolutionary and ecological model systems. Here, we study taxonomically complex British and Irish eyebrights (Euphrasia), as a test case for how different evolutionary factors influence species boundaries across geographic space. Euphrasia is a plant genus that has remarkable diversity in ploidy, mating system and ecology. There are 21 British and Irish Euphrasia species, but with species that are exceptionally difficult to identify based on morphology or DNA barcoding. Here, we test the hypothesis that species boundaries are highly permeable, and taxa experience extensive gene flow despite potential barriers such as ploidy and contrasting mating systems. To understand geographic genetic structure and the nature of species differences, we applied genotyping-by-sequencing (GBS) and spatial-aware clustering methods to 378 population samples from 18 British and Irish species. We find the selfing heathland specialist E. micrantha demonstrates genome-wide divergence in Northern Scotland, indicative of a distinct post-glacial colonisation history and the role of a highly selfing mating system in divergence. In contrast, all other genetic clusters correspond to geographic regions, with extensive gene flow between species and a complete absence of species-specific SNPs. Our results reveal the highly permeable species boundaries present in a recently diverging group, with an overriding signal of geographic genetic structure over and above genetic clustering by species.
- organism: plant (other)
- assay: DNA methylation
- research focus: clustering
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- approach: statistical model
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