The availability of information on the genetic diversity and population structure in wheat (Triticum aestivum L.) breeding lines will help wheat breeders to better use their genetic resources and manage genetic variation in their breeding program. The recent advances in sequencing technology provide the opportunity to identify tens or hundreds of thousands of single nucleotide polymorphism (SNPs) in large genome species (e.g., wheat). These SNPs can be utilized for understanding genetic diversity and performing genome wide association studies (GWAS) for complex traits. In this study, the genetic diversity and population structure were investigated in a set of 230 genotypes (F3:6) derived from various crosses as a prerequisite for GWAS and genomic selection. Genotyping-by-sequencing provided 25,566 high-quality SNPs. The polymorphism information content (PIC) across chromosomes ranged from 0.09 to 0.37 with an average of 0.23. The distribution of SNPs markers on the 21 chromosomes ranged from 319 on chromosome 3D to 2,370 on chromosome 3B. The analysis of population structure revealed three subpopulations (G1, G2, and G3). Analysis of molecular variance identified 8% variance among and 92% within subpopulations. Of the three subpopulations, G2 had the highest level of genetic diversity based on three genetic diversity indices: Shannon’s information index (I) = 0.494, diversity index (h) = 0.328 and unbiased diversity index (uh) = 0.331, while G3 had lowest level of genetic diversity (I = 0.348, h = 0.226 and uh = 0.236). This high genetic diversity identified among the subpopulations can be used to develop new wheat cultivars.

Background Improving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments. Results A set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents. Conclusion Although G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

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Wheat (Triticum aestivum L.) is one of the most important crops in the world. Four spike-related traits, namely, spike weight (SW), spike length (SL), the total number of spikelets per spike (TSNS), total kernels per spike (TKNS), and thousand-kernel weight (TKW), were evaluated in 270 F3:6 Nebraska winter wheat lines in two environments (Lincoln and North Platte, NE, USA). All genotypes in both locations exhibited high genetic variation for all yield traits. High positive correlations were observed among all yield-related traits in each location separately. No or low correlation in yield-related traits was observed between the two environments. The broad-sense heritability estimates were 72.6, 72.3, 71.2, 72.3, and 56.1% for SW, SL, TSNS, TKNS, and TKW, respectively. A genome-wide association study (GWAS) was used to identify SNPs associated with yield traits. In the Lincoln environment, 44 markers were found to be significantly associated with spike-related traits (SW, SL, TSNS, TKNS, and TKW), while 41 were detected in North Platte. Due to the strong significant genotype x environment, no common SNP markers were found between the two locations. Gene annotation of the significant markers revealed candidate genes encoded for important proteins that are associated directly or indirectly with yield traits. Such high genetic variation among genotypes is very useful for selection to improve yield traits in each location separately.

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