Multi‐trait genomic prediction (MTGP) can improve selection accuracy for economically valuable ‘primary’ traits by incorporating data on correlated secondary traits. Resistance to Fusarium head blight (FHB), a fungal disease of wheat ( Triticum aestivum L.) and barley ( Hordeum vulgare L.), is evaluated using four genetically correlated traits: incidence (INC), severity (SEV), Fusarium damaged kernels (FDK), and deoxynivalenol content (DON). Both FDK and DON are primary traits; DON evaluation is expensive and usually requires several months for wheat breeders to get results from service laboratories performing the evaluations. We evaluated MTGP for DON using three soft red winter wheat breeding datasets: two diversity panels from the University of Illinois (IL) and Purdue University (PU) and a dataset consisting of 2019–2020 University of Illinois breeding cohorts. For DON, relative to single‐trait (ST) genomic prediction, MTGP including phenotypic data for secondary traits on both validation and training sets, resulted in 23.4 and 10.6% higher predictive abilities in IL and PU panels, respectively. The MTGP models were advantageous only when secondary traits were included in both training and validation sets. In addition, MTGP models were more accurate than ST models only when FDK was included, and once FDK was included in the model, adding additional traits hardly improved accuracy. Evaluation of MTGP models across testing cohorts indicated that MTGP could increase accuracy by more than twofold in the early stages. Overall, we show that MTGP can increase selection accuracy for resistance to DON accumulation in wheat provided FDK is evaluated on the selection candidates.

We report genomic regions that significantly control resistance to scald, net form (NFNB) and spot form net blotch (SFNB) in barley. Barley genotypes from Ethiopia, ICARDA, and the United States were evaluated in Ethiopia and North Dakota State University (NDSU). Genome-wide association studies (GWAS) were conducted using 23,549 single nucleotide polymorphism (SNP) markers for disease resistance in five environments in Ethiopia. For NFNB and SFNB, we assessed seedling resistance in a glasshouse at NDSU. A large proportion of the Ethiopian landraces and breeding genotypes were resistant to scald and NFNB. Most of genotypes resistant to SFNB were from NDSU. We identified 17, 26, 7, and 1 marker-trait associations (MTAs) for field-scored scald, field-scored net blotch, greenhouse-scored NFNB, and greenhouse-scored SFNB diseases, respectively. Using the genome sequence and the existing literature, we compared the MTAs with previously reported loci and genes for these diseases. For leaf scald, only a few of our MTAs overlap with previous reports. However, the MTAs found for field-scored net blotch as well as NFNB and SFNB mostly overlap with previous reports. We scanned the barley genome for identification of candidate genes within 250 kb of the MTAs, resulting in the identification of 307 barley genes for the 51 MTAs. Some of these genes are related to plant defense responses such as subtilisin-like protease, chalcone synthase, lipoxygenase, and defensin-like proteins.

Optimizing in vitro shoot regeneration conditions in wheat is one of the important steps in successful micropropagation and gene transformation. Various factors such as genotypes, explants, and phytohormones affect in vitro regeneration of wheat, hindering the ability to tailor genotype-independent protocols. Novel computational approaches such as artificial neural networks (ANNs) can facilitate modeling and predicting outcomes of tissue culture experiments and thereby reduce large experimental treatments and combinations. In this study, generalized regression neural network (GRNN) were used to model and forecast in vitro shoot regeneration outcomes of wheat on the basis of 10 factors including genotypes, explants, and different concentrations of 6-benzylaminopurine (BAP), kinetin (Kin), 2,4-dichlorophenoxyacetic acid (2,4-D), indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), zeatin, and CuSO₄. In addition, GRNN was linked to a genetic algorithm (GA) to identify an optimized solution for maximum shoot regeneration. Results indicated that GRNN could accurately predict the shoot regeneration frequency in the validation set with a coefficient determination of 0.78. Sensitivity analysis demonstrated that shoot regeneration frequency was more sensitive to variables in the order of 2,4-D > explant > genotype < zeatin < NAA. Results of this study suggest that GRNN-GA can be used as a tool, besides experimental approaches, to develop and optimize in vitro genotype-independent regeneration protocols.

Although kernel weight (KW) is a major component of grain yield, its contribution to yield genetic gain during breeding history has been minimal. This highlights an untapped potential for further increases in yield via improving KW. We investigated variation and genetics of KW and kernel length (KL) via genome-wide association studies (GWAS) using a historical and contemporary soft red winter wheat population representing 200 years of selection and breeding history in the United States. The observed changes of KW and KL over time did not show any conclusive trend. The population showed a structure, which was mainly explained by the time and location of germplasm development. Cluster sharing by germplasm from more than one breeding population was suggestive of episodes of germplasm exchange. Using 2 years of field-based phenotyping, we detected 26 quantitative trait loci (QTL) for KW and 27 QTL for KL with –log₁₀(p) > 3.5. The search for candidate genes near the QTL on the wheat genome version IWGSCv1.0 has resulted in over 500 genes. The predicted functions of several of these genes are related to kernel development, photosynthesis, sucrose and starch synthesis, and assimilate remobilization and transport. We also evaluated the effect of allelic polymorphism of genes previously reported for KW and KL by using Kompetitive Allele Specific PCR (KASP) markers. Only TaGW2 showed significant association with KW. Two genes, i.e., TaSus2-2B and TaGS-D1 showed significant association with KL. Further physiological studies are needed to decipher the involvement of these genes in KW and KL development.

Plants deploy several ammonium transporter (AMT) and nitrate transporter (NRT) genes to acquire NH₄⁺and NO₃⁻from the soil into the roots and then transport them to other plant organs. Coding sequences of wheat genes obtained from ENSEMBL were aligned to known AMT and NRT sequences ofArabidopsis, barley, maize, rice, and wheat to retrieve homologous genes. Bayesian phylogenetic relationships among these genes showed distinct classification of sequences with significant homology toNRT1,NRT2, andNRT3(NAR2). Inter-species gene duplication analysis showed that eight AMT and 77 NRT genes were orthologous to the AMT and NRT genes of aforementioned plant species. Expression patterns of these genes were studied via whole transcriptome sequencing of 21-day old seedlings of five spring wheat lines. Eight AMT and 52 NRT genes were differentially expressed between root and shoot; and 131 genes did not express neither in root nor in shoot of 21-day old seedlings. Homeologous genes in the A, B, and D genomes, characterized by high sequence homology, revealed that their counterparts exhibited different expression patterns. This complement and evolutionary relationship of wheat AMT and NRT genes is expected to help in development of wheat germplasm with increased efficiency in nitrogen uptake and usage.