In this study, 129 wheat genotypes from globally diverse origins were genotyped using DArTseq (SilicoDArT and SNP) markers. After filtering markers for quality-filtering, 14,270 SilicoDArTs and 6484 SNPs were retained and used for genetic diversity, population structure and linkage disequilibrium analyses. The highest number of SilicoDArT and SNP markers mapped on genome A and B compared to genome D. In both marker types, polymorphism information content (PIC) values ranged from 0.1 to 0.5, while > 0.80% of SilicoDArTs and > 0.44% SNPs showed PIC value more than median (0.25%). Un-weighted Neighbor Joining cluster analysis and Bayesian-based model population structure grouped wheat genotypes into three and four clusters, respectively. Principal component analysis and discriminant analysis of principal component results showed highly match with cluster and population structure analysis. Linkage disequilibrium (LD) was more extensive in both marker types, while graphical display of LD decay for both marker types showed that LD declined in the region close to 15 kbp, where r2-values corresponded to r2 = 0.16. Overall, our genetic diversity analysis showed high level of variation in studied wheat genotypes, even though there was no relationship between wheat grouping and origins. This might be attributed to admixture level that occurred during long-term natural selection of wheat genotypes in different parts of the world. Highly diverse wheat genotypes used in this study may possess unique genes and are useful sources in breeding programs to improve grain yield and quality.
Characterization of genetic diversity, population structure, and linkage disequilibrium is a prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes, including advanced “Kabuli” breeding lines and Iranian landrace “Desi” chickpea genotypes, were genotyped using DArTseq-Based single nucleotide polymorphism (SNP) markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that was covered by SNPs varied from 16,236.36 kbp (LG8) to 67,923.99 kbp (LG5), while LG4 showed a higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6, and LG8 showed higher mean PIC value than average. Unweighted neighbor joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and discriminant analysis of principal component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r2 ≥ 0.8, while 2961 pairs of markers showed complete LD (r2 = 1), and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggest the presence of a high genetic diversity among the studied chickpea genotypes. This study also demonstrates the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits, such as seed yield, abiotic, and biotic stresses, and therefore can be efficiently used in breeding programs to improve chickpea.