We used four different approaches to identify regions under selection: two methods allowing identifying a reduction of diversity for the selected genes, two methods allowing identifying an excess of differentiation. The diversity reduction was assessed using Tajima’s D and by the ratio of cultivated to wild diversity. The excess of differentiation was assessed using the FST between cultivated and wild populations and a principal component based analysis. Tajima’s D value of each contig was calculated for the species using vcftools v0.1.13 . (1) We plotted the distribution of Tajima’s D values and then used a 1% threshold to identify extremely low values. (2) The ratio of the cultivated genetic diversity divided by the mean diversity of the two wild relative species using π  and θ . We used a 1% threshold to identify outlier contigs with extremely low ratios. (3) We estimated the differentiation index FST  between the cultivated group and each of the two wild groups for each contig using vcftools v0.1.13 . Using the cutoff of the 1% top values, contigs with extreme FST between the cultivated and both two wild relatives were selected as candidates. (4) Based on principal component analysis at the SNP level we used the program Pcadapt V2.2  to identify SNPs with extreme differentiation between the three species. The Mahalanobis distance  was calculated and we used the 5% threshold of the false discovery rate (FDR)  to detect candidate SNPs. The four selection tests were compared using a Venn diagram  to reveal the most likely candidate regions for selection. The annotation of the candidate selected genes was retrieved from a previous study .