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.