Data from: Genomic clustering of adaptive loci during parallel evolution of an Australian wildflower

The buildup of the phenotypic differences that distinguish species has long intrigued biologists. These differences are often inherited as stable polymorphisms that allow the co-segregation of adaptive variation within species, and facilitate the differentiation of complex phenotypes between species. It has been suggested that the clustering of adaptive loci could facilitate this process but evidence is still scarce. Here we used QTL analysis to study the genetic basis of phenotypic differentiation between coastal populations of the Australian wildflower Senecio lautus. We found that a genomic region consistently governs variation in several of the traits that distinguish these contrasting forms. Additionally, some of the taxon specific traits controlled by this QTL cluster have evolved repeatedly during the adaptation to the same habitats, suggesting that it could mediate divergence between locally adapted forms. This cluster contains footprints of divergent natural selection across the range of S. lautus, which suggests that it could have been instrumental for the rapid diversification of this species.Genotype calls and genetic divergence from bulk segregant analysis of survivorship.We show results of the analysis of Bulk Segregant Analysis (BSA) of survivorship in reciprocal transplants between sand dune and rocky headland environments at Lennox Head (NSW, Australia) as performed with the Popoolation2 software. RADseq genotypes were mapped the a genome draft. For each SNP (defined by the position along the genomic contig where it was mapped) and genomic comparison between two pools of survivors from contrasting environments we show the identity and read counts for the major allele (maa) in the two populations (i.e. pop1 is the pool of sand dune survivors and pop2 is the pool of headland survivors). We also show the value of Fst, as calculated by Popoolation2, as well as the p-value of a fisher exact test of allelic differentiation (FET). Finally, in the last column we show if the SNPs had outlier Fst values (i.e. upper 5% tail of the distribution) and presented significantly different allelic frequencies in the FET (Bonferroni corrected p-value lower than 5%). Survivors from each environment where included in three different pools which were sequenced individually. Therefore we had three different comparisons between pools of survivors (i.e. F8-A-S-Dune_vs_F8-A-S-Headland, F8-B-S-Dune_vs_F8-B-S-Headland, and F8-C-S-Dune_vs_F8-C-S-Headland).Fst_BSA.txtGenotype calls and genetic divergence between parapatric populations.We show results of the analysis of differentiation between parapatric pairs of parapatric populations as performed with the Popoolation2 software. RADseq genotypes were mapped the a genome draft. For each SNP (defined by the position along the genomic contig where it was mapped) and genomic comparison between two parapatric populations we show the identity and read counts for the major allele (maa) in the two populations (ie.. pop1 is the dune population and pop2 is the headland population). We also show the value of Fst, as calculated by Popoolation2, as well as the p-value of a fisher exact test of allelic differentiation (FET). Finally, in the last column we show if the SNPs had outlier Fst values (i.e. upper 5% tail of the distribution) and presented significantly different allelic frequencies in the FET (Bonferroni corrected p-value lower than 5%).Fst_Parapatric.tx