Research

South Dakota State University

Core Winter Wheat Breeding: The major focus of our program is developing high-yielding, disease-resistant winter wheat varieties with good end-use quality for South Dakota and the Northern Great Plains.

1.       Hard red winter (HRW) wheat cultivar ‘SD PHEASANT’ (12/2023). SD Pheasant is a semi-dwarf variety with excellent milling and baking quality. SD Pheasant is a high-yielding variety with good test weight and grain protein content. It has a good disease resistance package, SD Pheasant is resistant to leaf rust and moderately resistant to stem rust, and hessian fly. Along with excellent grain yield potential, SD Pheasant has good milling and excellent baking characteristics. It was selected for Miller’s Choice ‘Best of Show’ award by the Wheat Quality Council for its excellent end-use quality.

2. Hard red winter (HRW) wheat cultivar ‘SD MIDLAND’ (12/2021). SD Midland is a semi-dwarf variety with excellent milling and baking quality and moderate resistance to stripe rust. SD Midland was selected for Miller’s Choice ‘Best of Show’ award by the Wheat Quality Council for its excellent end-use quality.

3. Hard red winter (HRW) wheat cultivar ‘SD ANDES’ (12/2020). SD Andes is a semi-dwarf variety with late maturity. It has excellent straw strength, winter hardiness, and resistance to stripe rust.

4.   Hard red winter (HRW) wheat cultivar ‘Winner’ was released by SDAES (12/2019). Winner is a semi-dwarf variety with an unusually broad adaptation to the eastern half of the            Northern Great Plains. Winner has medium height and medium maturity with higher yield potential, good baking quality, and moderate resistance to stem rust. 

5.   Hard red winter (HRW) wheat cultivar ‘Draper’ was released by SDAES (12/2019). Draper is a semi-dwarf variety with good straw strength and a limited target region,                    specifically western South Dakota. Draper has improved yield potential with average test weight, grain protein, and good milling and baking quality. It is resistant to soilborne mosaic virus.

 6. Hard red winter (HRW) wheat cultivar ‘Thompson’ was released by SDAES (11/2017). Thompson is a high-yielding taller semi-dwarf HRW variety adapted to central South            Dakota with moderate resistance to leaf rust and stem rust and acceptable milling quality.

7. Hard red winter (HRW) wheat cultivar ‘Oahe’ was released by SDAES (8/2016). Oahe offers a combination of high yield with good test weight and moderate resistance to stripe rust, leaf rust, wheat streak mosaic virus, and Fusarium head blight.

Genetics  & Genomics: Enhancing diversity in wheat crop and developing germplasm with novel sources of resistance to biotic and abiotic stresses.

Rapid estimation of DON content in wheat flour using close-range hyperspectral imaging and machine learning

Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS) are commonly used to assess DON levels in grain or flour samples and are time-consuming and expensive. Therefore, a faster, cost-effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close-range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC-MS and subsequently subjected to close-range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one-dimensional convolutional neural network (1D-CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high-throughput and cost-effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels. 

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection. 

Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.)

Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length–width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9–13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties. 

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency. 

Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat

Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains. 

Wheat powdery mildew resistance gene Pm13 encodes a mixed lineage kinase domain-like protein

Wheat powdery mildew is one of the most destructive diseases threatening global wheat production. The wild relatives of wheat constitute rich sources of diversity for powdery mildew resistance. Here, we report the map-based cloning of the powdery mildew resistance gene Pm13 from the wild wheat species Aegilops longissima. Pm13 encodes a mixed lineage kinase domain-like (MLKL) protein that contains an N-terminal-domain of MLKL (MLKL_NTD) domain in its N-terminus and a C-terminal serine/threonine kinase (STK) domain. The resistance function of Pm13 is validated by mutagenesis, gene silencing, transgenic assay, and allelic association analyses. The development of introgression lines with significantly reduced chromosome segments of Ae. longissima encompassing Pm13 enables widespread deployment of this gene into wheat cultivars. The cloning of Pm13 may provide valuable insights into the molecular mechanisms underlying Pm13-mediated powdery mildew resistance and highlight the important roles of kinase fusion proteins (KFPs) in wheat immunity.

Pm57 from Aegilops searsii encodes a tandem kinase protein and confers wheat powdery mildew resistance

Powdery mildew is a devastating disease that affects wheat yield and quality. Wheat wild relatives represent valuable sources of disease resistance genes. Cloning and characterization of these genes will facilitate their incorporation into wheat breeding programs. Here, we report the cloning of Pm57, a wheat powdery mildew resistance gene from Aegilops searsii. It encodes a tandem kinase protein with putative kinase-pseudokinase domains followed by a von Willebrand factor A domain (WTK-vWA), being ortholog of Lr9 that mediates wheat leaf rust resistance. The resistance function of Pm57 is validated via independent mutants, gene silencing, and transgenic assays. Stable Pm57 transgenic wheat lines and introgression lines exhibit high levels of all-stage resistance to diverse isolates of the Bgt fungus, and no negative impacts on agronomic parameters are observed in our experimental set-up. Our findings highlight the emerging role of kinase fusion proteins in plant disease resistance and provide a valuable gene for wheat breeding. 

An Aegilops longissima NLR protein with integrated CC-BED module mediates resistance to wheat powdery mildew

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), reduces wheat yields and grain quality, thus posing a significant threat to global food security. Wild relatives of wheat serve as valuable resources for resistance to powdery mildew. Here, the powdery mildew resistance gene Pm6Sl is cloned from the wild wheat species Aegilops longissima. It encodes a nucleotide-binding leucine-rich repeat (NLR) protein featuring a CC-BED module formed by a zinc finger BED (Znf-BED) domain integrated into the coiled-coil (CC) domain. The function of Pm6Sl is validated via mutagenesis, gene silencing, and transgenic assays. In addition, we develop a resistant germplasm harbouring Pm6Sl in a very small segment with no linkage drag along with the diagnostic gene marker pm6sl-1 to facilitate Pm6Sl deployment in wheat breeding programs. The cloning of Pm6Sl, a resistance gene with BED-NLR architecture, will increase our understanding of the molecular mechanisms underlying BED-NLR-mediated resistance to various pathogens. 

Kansas State University

Genome sequence is central for systematic understanding wheat biology and provides a comprehensive foundation for breeding higher yielding, drought-tolerant and disease-resistant cultivars. Major food crops species (Rice Maize, soybean, sorghum) have been sequenced in last decade however this has not been achieved in wheat and barley precluded by the size and complexity of their genomes. Efforts are underway across the globe to develop integrated physical and genetic map of wheat from last five years as first significant step in sequencing the wheat genome. Our group is working on developing an integrated physical and genetic map of four chromosomes 3A, 1D, 4D, 6D of wheat.  BAC based physical map are very valuable for cloning of economically important genes for understanding the mechanism of their function.  Resources developed under our projects have used in fine mapping and cloning of 2 genes/QTLs and are being used for several more genes.

Developing sequence-ready integrated genetic and physical map of four chromosomes of bread wheat: Leading the effort in developing sequence-ready physical maps CS 1D, 4D, 6D (project website) and 3A (project website) by fingerprinting ~500,000 BACs from chromosome- or chromosome arm-specific BAC libraries (see our database) and assembling them into BAC contigs.

Developed thousands of SNPs markers from next generation sequence analysis. Physically anchored thousands of EST-STS, SSR, SNPs markers on individual BACs and fingerprint contigs using multidimensional BAC pools. Ordering BAC contigs on the genetic maps developed using Illumina Golden Gate and Genotype By Sequencing (GBS) which would lead to BAC by BAC sequencing of four wheat chromosomes. In addition 10,000 BAC were end sequenced to develop over 1,400 chromosome specific markers for chromosome 3AS (see publications).

More than 100 agro-economic genes/QTls (see Figure) have been mapped on these chromosomes and physical maps is being used for fine mapping and cloning of several important genes like Pre-harvest sprouting, earliness per se, leaf rust resistance see publications) in several labs including WGRC.

Sequenced flow sorted chromosome 3A and selected clones from 3A MTP  for comparative genomics in grasses to study dynamics wheat genome evolution (see publications).

The importance of diverse genetic input for success of crop breeding programs is well recognized. In contrast to ‘gene introgression’ involving transfer of one or a few genes from exotic and wild donors, ‘incorporation’ of a larger set of genes is better, particularly in context of productivity traits and would broaden the genetic base of wheat crop. We need to develop resources for efficient mining of wheat relatives which are highly diverse.  The progenitor species like Aegilops tauschi Coss. the D genome donor of bread wheat and Triticum monococcum  represents a rich source of resistance and productivity traits should be the first targets of mining. Meanwhile we need to develop genetic and genomic resources efficient mining of other wild relatives of wheat.

Mining the wheat gene pool. Taking forward my PhD project to identify and introgress traits of biotic, abiotic stress tolerance and productivity (see PhD publications) from Aegilops taushii into Bread wheat. We are characterizing the genetic diversity of Ae. tauschii to identify a core set of Ae tauschii which carries >90% of the genetic diversity of this species. On the other hand we are developing genomic resources (SNP markers) for assisting rapid and precise introgression from wild species (Ae. geniculate, Ae. speltoides, Ae. umbellulata, D. villosum, Th. intermedium, Th. elongatum, E. tsukushiensis, H. chilense and my other species) into cultivated wheat.

Identification and mapping of biotic and abiotic stress tolerance genes: Identified sources of leaf rust resistance and drought tolerance in wheat and developed several mapping populations for identification of genetic factors responsible for these agronomic traits. Mapped QTLs for drought and Karnal Bunt (see publications) and presently focusing on fine mapping of leaf rust gene Lr 42.

 It is estimated that average annual yield increase of 2% will be needed for the estimated population increase, and this must be accomplished by increasing crop productivity per unit area of land.  Wheat cropping systems also must contend with scenarios of limited water, fertilizer and the uncertainties of climate change.  There is a universal agreement that we must unlock the biology of crop plants for sustainable and profitable crop production and increase the pace of crop improvement.  Where DH and genomic selection increasing the breeding efficiency, hybrid wheat will aid in productivity and helps ensure a reliable, sustainable food supply.

Rapid advancement and yield potential. I was involved in Doubled haploid (DH) and hybrid wheat program during my doctoral studies (2001-2005). In DH program developed tiller culture protocol in wheat x maize crosses and developed thousands of DH lines. In HW program primarily made crosses for incorporating T. timopheevi cytoplasm to elite wheat lines. Restorer line breeding for T. timopheevi cytoplasm constitutes a major activity of program. Presently, working on nuclear male sterility and CMS system and transferring it to five elite winter and spring lines.

 At present complex plants genome like wheat cannot be assembled using shotgun sequences from these short reads; however these sequences can be very useful in assembling the gene space(see pubications). Effort around the world are underway to use next generation sequencing to assemble gene space of diploid tetraploid and hexaploid bread wheat. These developments have stimulated interest in rapid characterization of agro-economic genes. Concurrently we have developed for next generation sequence and a TILLING population in diploid wheat.

Developing gene discovery model in diploid wheat. Sequencing and assembling T. monococcum subsp. aegilopoides (AmAm, ~30x, wild) and collaborating in sequencing of Tritcum monococcum subsp. monococcum (AmAm ,~150x, domesticated) (with CSHL). Developed large T. monococcum /T. aegilopoides RIL population (1,453 F6) and generating a high density GBS based SNP map to anchor Triticum monococcum genome map and also developed TILLING population (1700 M2’s) in same T. monococcum accession for gene discovery (see publications). These resources are being used for fine mapping of tillering gene (tin3) and gene discovery of other agronomic traits in T. monococcum.

Other physical maps: Aegilops tauschii is another diploid wheat and is progenitor of D genome of wheat with genome size over 4Gb. Involved in developing physical maps of Aegilops tauschii the progenitor species of wheat D genome (see publications) and chromosome 2A of wheat.

Sequencing of bread wheat genome: Participated in next generation sequencing of wheat genome and comparative sequence analysis with other grasses model genomes to study the evolution of wheat and identify gene space of wheat for faster crop improvement in wheat (see publications).

Ph.D.

Introgression of agro-economic traits from Aegilops taushii into Bread wheat. Characterized ~261 Ae. tauschii accessions for several (14) morphological /descriptor characters and evaluated for economic traits including resistance to Stripe rust, Leaf rust, Karnal bunt and cereal cyst nematodes  to identify more than 10 accessions of Ae. taushii that could be donor for multiple traits.

Developed a protocol for rapid incorporation (direct cross) of economic traits from Ae. tauschii into bread wheat (see publications). This system led to recovery of a good number of F1s and BC1F1s thus, removing the major bottlenecks reported for direct cross system (Figure1). This system was employed to develop over 30 direct cross hybrids and back cross derivatives.  Important high temperature tolerance improved like cell membrane stability and chlorophyll retention were transferred from  Ae. tauschii to wheat Table 2 (see publications).

Figure1. A.  Meiotic Metaphase-I showing 14 I + 7II in Aegilops tauschii x Triticum aestivum F1 hybrid (ABDD). B. Anaphase-I showing 56 chromosomes in colchicine treated Aegilops tauschii x Triticum aestivum F1 hybrid (AABBDDDD).

Developed 100 synthetic hexaploids (T. durum x Ae. tauschii) which can be used as a bridging stock for gene transfer to bread wheat for rust resistance.

Developed NIL populations (BC5F3 and BC5F4) for mapping and understanding the genetics of Karnal bunt (KB) resistance in the two NIL populations (see publications).