Spatial and ecological drivers of population structure in Alkanna tinctoria (Boraginaceae), a polyploid medicinal herb

Background and AimsQuantifying genetic variation is fundamental to understand a species demographic trajectory and its ability to adapt to future changes. In comparison to diploids, however, genetic variation and factors fostering genetic divergence remains poorly studied in polyploids due to analytical challenges. Here, by employing a ploidy-aware framework, we investigated the genetic structure and its determinants in polyploid Alkanna tinctoria (Boraginaceae), an ancient medicinal herb that is the source of bioactive compounds known as alkannin and shikonin (A/S). From a practical perspective, such investigation can inform biodiversity management strategies. MethodsWe collected 14 populations of A. tinctoria within its main distribution range in Greece and genotyped them using restriction site-associated DNA sequencing (RAD-seq). As an outgroup, we included two populations of A. sieberi. By using a ploidy-aware genotype calling based on likelihoods, we generated a dataset of 16,107 high quality SNPs. Classical and model-based analysis was done to characterize the genetic structure within and between the sampled populations. Finally, to reveal the drivers of genetic structure, we searched for associations between allele frequencies and spatial and climatic variables. Key ResultsWe found support for a marked regional structure in A. tinctoria along a latitudinal gradient in line with phytogeographic divisions. Several analyses identified interspecific admixture affecting both mainland and island populations. Modelling of spatial and climatic variables further demonstrated a larger contribution of neutral processes and a lesser albeit significant role of selection in shaping the observed genetic structure in A. tinctoria. ConclusionsCurrent findings provide evidence of strong genetic structure in A. tinctoria mainly driven by neutral processes. The revealed natural genomic variation in Greek Alkanna can be used to further predict variation in A/S production, whereas our bioinformatics approach should prove useful for the study of other non-model polyploid species.