Background: Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. Here, we used a genome-wide set of 1536 SNPs to study linkage disequilibrium (LD) and population structure in a panel of 478 spring and winter wheat cultivars (Triticum aestivum) from 17 populations across the United States and Mexico.Results: Most of the wheat oligo pool assay (OPA) SNPs that were polymorphic within the complete set of 478 cultivars were also polymorphic in all subpopulations. Higher levels of genetic differentiation were observed among wheat lines within populations than among populations. A total of nine genetically distinct clusters were identified, suggesting that some of the pre-defined populations shared significant proportion of genetic ancestry. Estimates of population structure (F_ST) at individual loci showed a high level of heterogeneity across the genome. In addition, seven genomic regions with elevated F_STwere detected between the spring and winter wheat populations. Some of these regions overlapped with previously mapped flowering time QTL. Across all populations, the highest extent of significant LD was observed in the wheat D-genome, followed by lower LD in the A- and B-genomes. The differences in the extent of LD among populations and genomes were mostly driven by differences in long-range LD ( > 10 cM).Conclusions: Genome- and population-specific patterns of genetic differentiation and LD were discovered in the populations of wheat cultivars from different geographic regions. Our study demonstrated that the estimates of population structure between spring and winter wheat lines can identify genomic regions harboring candidate genes involved in the regulation of growth habit. Variation in LD suggests that breeding and selection had a different impact on each wheat genome both within and among populations. The higher extent of LD in the wheat D-genome versus the A- and B-genomes likely reflects the episodes of recent introgression and population bottleneck accompanying the origin of hexaploid wheat. The assessment of LD and population structure in this assembled panel of diverse lines provides critical information for the development of genetic resources for genome-wide association mapping of agronomically important traits in wheat.

Spike architecture influences grain yield in wheat. We report the map-based cloning of a gene determining the number of spikelet nodes per spike in common wheat. The cloned gene is named TaCOL-B5 and encodes a CONSTANS-like protein that is orthologous to COL5 in plant species. Constitutive overexpression of the dominant TaCol-B5 allele but without the region encoding B-boxes in a common wheat cultivar increases the number of spikelet nodes per spike and produces more tillers and spikes, thereby enhancing grain yield in transgenic plants under field conditions. Allelic variation in TaCOL-B5 results in amino acid substitutions leading to differential protein phosphorylation by the protein kinase Ta K4. The TaCol-B5 allele is present in emmer wheat but is rare in a global collection of modern wheat cultivars.

One of the reasons hard red winter wheat cultivar ‘Duster’ (PI 644016) is widely grown in the southern Great Plains is that it confers a consistently high level of resistance to biotype GP of Hessian fly (Hf). However, little is known about the genetic mechanism underlying Hf resistance in Duster. This study aimed to unravel complex structures of the Hf region on chromosome 1AS in wheat by using genotyping-by-sequencing (GBS) markers and single nucleotide polymorphism (SNP) markers. Doubled haploid (DH) lines generated from a cross between two winter wheat cultivars, ‘Duster’ and ‘Billings’ , were used to identify genes in Duster responsible for effective and consistent resistance to Hf. Segregation in reaction of the 282 DH lines to Hf biotype GP fit a one-gene model. The DH population was genotyped using 2,358 markers developed using the GBS approach. A major QTL, explaining 88% of the total phenotypic variation, was mapped to a chromosome region that spanned 178 cM and contained 205 GBS markers plus 1 SSR marker and 1 gene marker, with 0.86 cM per marker in genetic distance. The analyses of GBS marker sequences and further mapping of SSR and gene markers enabled location of the QTL-containing linkage group on the short arm of chromosome 1A. Comparative mapping of the common markers for the gene for QHf.osu-1A d in Duster and the Hf-resistance gene for QHf.osu-1A 74 in cultivar ‘2174’ showed that the two Hf resistance genes are located on the same chromosome arm 1AS, only 11.2 cM apart in genetic distance. The gene at QHf.osu-1A d in Duster has been delimited within a 2.7 cM region. Two distinct resistance genes exist on the short arm of chromosome 1A as found in the two hard red winter cultivars, 2174 and Duster. Whereas the Hf resistance gene in 2174 is likely allelic to one or more of the previously mapped resistance genes (H9, H10, H11, H16, or H17) in wheat, the gene in Duster is novel and confers a more consistent phenotype than 2174 in response to biotype GP infestation in controlled-environment assays.

Uncrewed aerial vehicles (UAVs) are a highly successful and efficient method for phenotyping in plant breeding programs. This study explored using UAVs equipped with hyperspectral sensors to expedite breeders' decision‐making in selecting winter wheat (Triticum aestivum L.) genotypes with improved growth, biomass, leaf area, and canopy cover (CC). The hyperspectral image processing pipeline utilized for image analysis was described. The study was conducted with 2145 genotypes of winter wheat, and UAV‐based hyperspectral measurements were used to predict the phenotype. Reflectance measurements were taken at narrow wavelength intervals, spanning 400–2500 nm. For ground truthing, samples were collected from different parts of the field. These samples were used to evaluate various plant attributes, including CC, leaf area index, plant height, and dry biomass. The hyperspectral data were employed for computation of multiple vegetation indices (VIs), and to improve the prediction of plant traits, we employed partial least squares regression (PLSR) and random forest (RF) regression techniques on both the complete set of hyperspectral variables and the top 10 derived VIs. Our results show that using complete hyperspectral variables results in superior r, R², and lower root mean square error for both models. We conclude that relying solely on linear regression models with VIs may not always result in accurate predictions of plant traits in winter wheat. However, combining these indices with the RF and PLSR algorithm significantly enhances the prediction accuracy. However, the performance of both PLSR and RF models shows minimal disparity, with only slight differences observed. This highlights the importance of utilizing appropriate machine learning algorithms for improved prediction of plant traits.