Wheat cultivars ‘TAM 111’ and ‘TAM 112’ have been dominantly grown in the Southern U.S. Great Plains for many years due to their high yield and drought tolerance. To identify the molecular basis and genetic control of drought tolerance in these two landmark cultivars, RNA-seq analysis was conducted to compare gene expression difference in flag leaves under fully irrigated (wet) and water deficient (dry) conditions. A total of 2254 genes showed significantly altered expression patterns under dry and wet conditions in the two cultivars. TAM 111 had 593 and 1532 dry–wet differentially expressed genes (DEGs), and TAM 112 had 777 and 1670 at heading and grain-filling stages, respectively. The two cultivars have 1214 (53.9%) dry–wet DEGs in common, which agreed with their excellent adaption to drought, but 438 and 602 dry–wet DEGs were respectively shown only in TAM 111 and TAM 112 suggested that each has a specific mechanism to cope with drought. Annotations of all 2254 genes showed 1855 have functions related to biosynthesis, stress responses, defense responses, transcription factors and cellular components related to ion or protein transportation and signal transduction. Comparing hierarchical structure of biological processes, molecule functions and cellular components revealed the significant regulation differences between TAM 111 and TAM 112, particularly for genes of phosphorylation and adenyl ribonucleotide binding, and proteins located in nucleus and plasma membrane. TAM 112 showed more active than TAM 111 in response to drought and carried more specific genes with most of them were up-regulated in responses to stresses of water deprivation, heat and oxidative, ABA-induced signal pathway and transcription regulation. In addition, 258 genes encoding predicted uncharacterized proteins and 141 unannotated genes with no similar sequences identified in the databases may represent novel genes related to drought response in TAM 111 or TAM 112. This research thus revealed different drought-tolerance mechanisms in TAM 111 and TAM 112 and identified useful drought tolerance genes for wheat adaption. Data of gene sequence and expression regulation from this study also provided useful information of annotating novel genes associated with drought tolerance in the wheat genome.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10⁻¹⁴) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Two drought-tolerant wheat cultivars, ‘TAM 111’ and ‘TAM 112’, have been widely grown in the Southern Great Plains of the U.S. and used as parents in many wheat breeding programs worldwide. This study aimed to reveal genetic control of yield and yield components in the two cultivars under both dryland and irrigated conditions. A mapping population containing 124 F_5:7 recombinant inbred lines (RILs) was developed from the cross of TAM 112/TAM 111. A set of 5,948 SNPs from the wheat 90K iSelect array and double digest restriction-site associated DNA sequencing was used to construct high-density genetic maps. Data for yield and yield components were obtained from 11 environments. QTL analyses were performed based on 11 individual environments, across all environments, within and across mega-environments. Thirty-six unique consistent QTL regions were distributed on 13 chromosomes including 1A, 1B, 1D, 2A, 2D, 3D, 4B, 4D, 6A, 6B, 6D, 7B, and 7D. Ten unique QTL with pleiotropic effects were identified on four chromosomes and eight were in common with the consistent QTL. These QTL increased dry biomass grain yield by 16.3 g m^-2, plot yield by 28.1 g m^-2, kernels spike^-1 by 0.7, spikes m^-2 by 14.8, thousand kernel weight by 0.9 g with favorable alleles from either parent. TAM 112 alleles mainly increased spikes m^-2 and thousand kernel weight while TMA 111 alleles increased kernels spike^-1, harvest index and grain yield. The saturated genetic map and markers linked to significant QTL from this study will be very useful in developing high throughput genotyping markers for tracking the desirable haplotypes of these important yield-related traits in popular parental cultivars.

Seed germination is a critical first stage of plant development but can be arrested by factors including dormancy and environmental conditions. Strategies to enhance germination are of interest to plant breeders to ensure the ability to utilize the genetic potential residing inside a dormant seed. In this study, seed germination in two sugarbeet (Beta vulgaris ssp. vulgaris L.) lines F1004 and F1015 through incubating seeds in hydrogen peroxide (H₂O₂) solution was improved over 70% relative to germinating seeds through water incubation. It was further found that low germination from water incubation was caused by physical dormancy in F1015 seeds with initial seed imbibition blocked by the seed pericarp, and physiological dormancy in F1004 seeds with germination compromised due to the physiological condition of the embryo. To identify genes that are differentially expressed in response to cellular activities promoted by H₂O₂ during overcoming different type of dormancies, an RNA-Seq study was carried out and found H₂O₂ treatment during germination accelerated the degradation of seed stored mRNAs that were synthesized before or during seed storage to provide protections and maintain the dormant state. Comparison of transcripts in H₂O₂-treated seeds between the two sugarbeet lines identified differentially expressed genes (DEGs) that were higher in F1004 for alleviating physiological dormancy were known to relative to gene expression regulation. The research established that H₂O₂ overcomes both physical and physiological dormancies by hastening the transition of seeds from dormancy into germination. More DEGs related to gene expression regulation were involved in relieving physiological dormancy which provides new knowledge about the role of exogenous H₂O₂ as a signaling molecule for regulating gene activities during germination. Moreover, the protocol using H₂O₂ to promote germination will be useful for rescuing plant germplasms with poor germination.

Sugar beet (Beta vulgaris L. ssp. vulgaris Doell.) was originally selected from white fodder beet in the 1780s and was then specifically bred for sucrose production. The relatively recent inception of the crop has led to a narrow genetic base that has bottlenecked sustainable improvement. To evaluate the potential of publicly available germplasm for sugar beet improvement, genetic diversity analysis with SNPs (single‐nucleotide polymorphisms) covering the whole genome of sugar beet was conducted using 1936 publicly available germplasm lines in the United States. The results confirmed the narrow genetic base of sugar beet and identified germplasm accessions with inherently greater diversity that were mostly accessions of wild sea beet (B. vulgaris ssp. maritima (L.) Arcang.), the progenitor species of white fodder beet. These wild accessions displayed a distinct genetic relationship from cultivated sugar beet lines, indicating their high potential for broadening sugar beet genetic diversity. Analysis of historic resistance evaluations also suggested a higher potential of B. vulgaris ssp. maritima accessions to be used as sources of resistance to major diseases and insects of sugar beet. A genome‐wide association study using historic evaluation data identified genomic regions significantly associated with disease and insect resistance. However, genomic regions associated with resistance to nematode, insects, or diseases vectored by insects were of low significance, indicating the need for additional research to allow for a more precise evaluation of germplasm responses to insects. The research confirms that accessions of B. vulgaris ssp. maritima are potentially valuable for improving biotic stress resistance and broadening the genetic base of sugar beet.

Cultivated beet (Beta vulgaris L. ssp. vulgaris) originated from sea beet (B. vulgaris ssp. maritima (L.) Arcang), a wild beet species widely distributed along the coasts of the Mediterranean Sea and Atlantic Ocean, as well as northern Africa. Understanding the evolution of sea beet will facilitate its efficient use in sugarbeet improvement. We used SNPs (single nucleotide polymorphisms) covering the whole genome to analyze 599 sea beet accessions collected from the north Atlantic Ocean and Mediterranean Sea coasts. All B. maritima accessions can be grouped into eight clusters with each corresponding to a specific geographic region. Clusters 2, 3 and 4 with accessions mainly collected from Mediterranean coasts are genetically close to each other as well as to Cluster 6 that contained mainly cultivated beet. Other clusters were relatively distinct from cultivated beets with Clusters 1 and 5 containing accessions from north Atlantic Ocean coasts, Clusters 7 and Cluster 8 mainly have accessions from northern Egypt and southern Europe, and northwest Morocco, respectively. Distribution of B. maritima subpopulations aligns well with the direction of marine currents that was considered a main dynamic force in spreading B. maritima during evolution. Estimation of genetic diversity indices supported the formation of B. maritima subpopulations due to local genetic drift, historic migration, and limited gene flow. Our results indicated that B. maritima originated from southern Europe and then spread to other regions through marine currents to form subpopulations. This research provides vital information for conserving, collecting, and utilizing wild sea beet to sustain sugarbeet improvement.