Data from: Range-wide phenotypic and genetic differentiation in wild sunflower
McAssey, Edward V.; Corbi, Jonathan; Burke, John M. (2017), Data from: Range-wide phenotypic and genetic differentiation in wild sunflower, Dryad, Dataset, https://doi.org/10.5061/dryad.6p1c4
Background: Divergent phenotypes and genotypes are key signals for identifying the targets of natural selection in locally adapted populations. Here, we used a combination of common garden phenotyping for a variety of growth, plant architecture, and seed traits, along with single-nucleotide polymorphism (SNP) genotyping to characterize range-wide patterns of diversity in 15 populations of wild sunflower (Helianthus annuus L.) sampled along a latitudinal gradient in central North America. We analyzed geographic patterns of phenotypic diversity, quantified levels of within-population SNP diversity, and also determined the extent of population structure across the range of this species. We then used these data to identify significantly over-differentiated loci as indicators of genomic regions that likely contribute to local adaptation.
Results: Traits including flowering time, plant height, and seed oil composition (i.e., percentage of saturated fatty acids) were significantly correlated with latitude, and thus differentiated northern vs. southern populations. Average pairwise FST was found to be 0.21, and a STRUCTURE analysis identified two significant clusters that largely separated northern and southern individuals. The significant FST outliers included a SNP in HaFT2, a flowering time gene that has been previously shown to co-localize with flowering time QTL, and which exhibits a known cline in gene expression.
Conclusions: Latitudinal differentiation in both phenotypic traits and SNP allele frequencies is observed across wild sunflower populations in central North America. Such differentiation may play an important adaptive role across the range of this species, and could facilitate adaptation to a changing climate.
National Science Foundation, Award: DBI-1444522