Aegilops tauschii, the diploid wild progenitor of the D subgenome of bread wheat, is a reservoir of genetic diversity for improving bread wheat performance and environmental resilience. Here we sequenced 242 Ae. tauschii accessions and compared them to the wheat D subgenome to characterize genomic diversity. We found that a rare lineage of Ae. tauschii geographically restricted to present-day Georgia contributed to the wheat D subgenome in the independent hybridizations that gave rise to modern bread wheat. Through k-mer-based association mapping, we identified discrete genomic regions with candidate genes for disease and pest resistance and demonstrated their functional transfer into wheat by transgenesis and wide crossing, including the generation of a library of hexaploids incorporating diverse Ae. tauschii genomes. Exploiting the genomic diversity of the Ae. tauschii ancestral diploid genome permits rapid trait discovery and functional genetic validation in a hexaploid background amenable to breeding.

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.

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

Unmanned aerial systems (UASs) have increased our capacity for collecting finer spatiotemporal resolution data that were previously unobtainable through conventional methods. The use of UAS for obtaining high‐throughput phenotyping (HTP) data in plant breeding programs has gained popularity in recent years. The integrity and quality of the raw data are essential for ensuring the accuracy of predictive tools and proper interpretation of the data. This paper summarizes the standard operation procedures for high‐quality UAS data collection, processing, and analysis for UAS‐based HTP (UAS‐HTP). Plant breeders can follow these procedures to implement a UAS‐HTP system in their germplasm enhancement and cultivar development programs.