Abstract
Background: Wheat (Triticum aestivium L.) is an important crop globally which has a complex genome. To identify
the parents with useful agronomic characteristics that could be used in the various breeding programs, it is very
important to understand the genetic diversity among global wheat genotypes. Also, understanding the genetic
diversity is useful in breeding studies such as marker-assisted selection (MAS), genome-wide association studies
(GWAS), and genomic selection.
Results: To understand the genetic diversity in wheat, a set of 103 spring wheat genotypes which represented five
different continents were used. These genotypes were genotyped using 36,720 genotyping-by-sequencing derived
SNPs (GBS-SNPs) which were well distributed across wheat chromosomes. The tested 103-wheat genotypes
contained three different subpopulations based on population structure, principle coordinate, and kinship analyses.
A significant variation was found within and among the subpopulations based on the AMOVA. Subpopulation 1
was found to be the more diverse subpopulation based on the different allelic patterns (Na, Ne, I, h, and uh). No
high linkage disequilibrium was found between the 36,720 SNPs. However, based on the genomic level, D genome
was found to have the highest LD compared with the two other genomes A and B. The ratio between the number
of significant LD/number of non-significant LD suggested that chromosomes 2D, 5A, and 7B are the highest LD
chromosomes in their genomes with a value of 0.08, 0.07, and 0.05, respectively. Based on the LD decay, the D
genome was found to be the lowest genome with the highest number of haplotype blocks on chromosome 2D.
Conclusion: The recent study concluded that the 103-spring wheat genotypes and their GBS-SNP markers are very
appropriate for GWAS studies and QTL-mapping. The core collection comprises three different subpopulations.
Genotypes in subpopulation 1 are the most diverse genotypes and could be used in future breeding programs if
they have desired traits. The distribution of LD hotspots across the genome was investigated which provides useful
information on the genomic regions that includes interesting genes.