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An SNP analysis is performed using the iSelect90K SNP array to identify the genetic diversity of synthetic hexaploid wheat lines of Kyoto University (Japan) bred by crossing durum wheat variety Langdon (Triticum durum, 2n = 4x = 28, BBAA) with accessions of Aegilops tauschii (2n = 2x = 14, DD) of different ecological origin. The level of genomic variability is calculated by using the Emma approach (Efficient Mixed-Model Association) and genomic relationship matrix (G). The genetic diversity of the genome D of synthetic lines is greater than that of genomes A and B by 50%. The greatest genetic polymorphism is observed in the line Langdon/Ku-2105 from Pahlavi province on the southern coast of Caspian Sea and the line Langdon/Ig 131606 from Kyrgyzstan. Lines from India (Langdon/Ig 48042), Iranian provinces Sari and Babolsar (Langdon/Ku-2088, Langdon/Ku-2093, Langdon/Ku-2096), and Turkmenistan (Langdon/Ig 26387) are characterized by a lower genetic diversity of the genome D. Thus, the involvement of synthetic hexaploid wheat on the basis of Ae. tauschii accessions from the Caspian basin into hybridization will extend the genetic diversity of wheat varieties and increase the efficiency of genetic resources.

Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 $$\times $$ × AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments.Supplementary materials accompanying this paper appear online.

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

Background Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.

Spot blotch, caused by Bipolaris sorokiniana, is a potent biotic constraint to wheat crop leading to substantial yield abatement in warm humid South Asia (e.g. India, Nepal and Bangladesh) and other major wheat-growing countries such as Canada, the United States, Brazil and Australia. A set of 98 bread wheat genotypes were evaluated over two growing seasons to investigate the relationship among foliar trichome, stomata density, and susceptibility to the spot blotch pathogen. The higher density of trichomes (90-140 mm−2) on the adaxial surface of flag leaves entrapped 32-74 mm−2 pathogen spores. It restricted the dew droplets size (0.15–0.3 mm2) on the flag leaves and thus prevented direct contact between the spores and the leaf epidermis. Trichome density was negatively associated with the disease development and spore germination but positively associated with grain size and the number of spores entrapped on trichomes per mm2. Conversely, stomata density was positively associated with the development of the disease. Cluster and principal component analyses divided trichome density, the number of spores trapped on trichome per mm2, and thousand grain weight into one group and stomata density, the area under disease progress curve, percent spore germination, and dew scale into another group. The results from this study provided a novel insight to breeders where the selection of bread wheat genotypes with high trichome (90-140 mm−2) and low stomata density (72-90 mm−2) on foliage could enhance spot blotch resistance in wheat.

Background Chile peppers (Capsicum spp.) are among the most important horticultural crops in the world due to their number of uses. They are considered a major cultural and economic crop in the state of New Mexico in the United States. Evaluating genetic diversity in current New Mexican germplasm would facilitate genetic improvement for different traits. This study assessed genetic diversity, population structure, and linkage disequilibrium (LD) among 165 chile pepper genotypes using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Results A GBS approach identified 66,750 high-quality SNP markers with known map positions distributed across the 12 chromosomes of Capsicum. Principal components analysis revealed four distinct clusters based on species. Neighbor-joining phylogenetic analysis among New Mexico State University (NMSU) chile pepper cultivars showed two main clusters, where the C. annuum genotypes grouped together based on fruit or pod type. A Bayesian clustering approach for the Capsicum population inferred K = 2 as the optimal number of clusters, where the C. chinense and C. frutescens grouped in a single cluster. Analysis of molecular variance revealed majority of variation to be between the Capsicum species (76.08 %). Extensive LD decay (~ 5.59 Mb) across the whole Capsicum population was observed, demonstrating that a lower number of markers would be required for implementing genome wide association studies for different traits in New Mexican type chile peppers. Tajima’s D values demonstrated positive selection, population bottleneck, and balancing selection for the New Mexico Capsicum population. Genetic diversity for the New Mexican chile peppers was relatively low, indicating the need to introduce new alleles in the breeding program to broaden the genetic base of current germplasm. Conclusions Genetic diversity among New Mexican chile peppers was evaluated using GBS-derived SNP markers and genetic relatedness on the species level was observed. Introducing novel alleles from other breeding programs or from wild species could help increase diversity in current germplasm. We present valuable information for future association mapping and genomic selection for different traits for New Mexican chile peppers for genetic improvement through marker-assisted breeding.

Recurrent selection and intercrossing between best of the best parents in each generation of breeding cycle resulted in a narrower genetic diversity in elite wheat (Triticum aestivum L.) germplasm. Therefore, we investigated diverse source of 143 synthetic and bread wheat accessions for identifying potentially rich genetic resources for improving the genetic diversity in wheat. This study identified 47,526 genotyping-by-sequencing-derived SNP markers that were nearly evenly distributed across three genomes of wheat. The population structure analysis identified three distinct clusters (Japan synthetics, CIMMYT synthetics, and bread wheat) of wheat genotypes on the basis of type and geographical origin of wheat accessions. Population differentiation using analysis of molecular variance indicated 21% of the total genetic variance among subgroups and the remainder within subgroups. This study also identified that the Japan synthetic group was the most divergent group compared with other subgroups. The genetic diversity comparisons between synthetic and bread wheat lines showed that the gene diversity of synthetic wheat was 33% higher than bread wheat accessions, indicating the potential use of these lines for broadening the genetic diversity of modern wheat cultivars. The results from this study will be helpful in further understanding genomic features of wheat and facilitate their use in wheat breeding programs.

BackgroundSelective breeding pressures have led to gradual genomic changes in Asian commercial rice, which have shaped selection footprints on its genome level. Tracing genomic selection footprints might be illuminative for better understanding of recent selection breeding objectives, and how breeding strategies have formed the Asian commercial rice genome. ResultsIn this study, the genotypic information (HDRA 700K) of four Asian commercial rice subspecies including Indica (n=498), Aus (n=187), Temperate japonica (n=241), and Tropical japonica (n=361) were downloaded from Rice Diversity Project database (http://www.ricediversity.org) to detect selection signatures by employing the Z-transformed of fixation index and Tajima’s D test, based on a sliding window approach. Although we could not identify overrepresented genomic regions underlying selection pressure among all aforementioned Asian commercial rice subspecies, interestingly, our findings revealed four overrepresented biological processes underlying selection pressure including proteolysis (GO:0006508), phosphorylation (GO:0016310), protein catabolic process (GO:0030163), and transmembrane transport (GO:0055085) that might be associated with immunity, senescing leaves, transporting, and absorption of ions. ConclusionsThese results can provide knowledge on how breeding efforts shaped the Asian commercial rice subspecies genome, and which genomic regions of these subspecies have been targeted in recent decades.

Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multi-dimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum Aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a twodimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments.However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1×AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials accompanying this paper appear online.

Foliar diseases limit winter wheat (Triticum aestivum L.) production in the Great Plains of the United States. Farmers often use foliar fungicide application to counter yield loss. Two-year field experiments were conducted at two locations in Nebraska to investigate the effects of foliar fungicide (Prosaro 421 SC [Bayer Crop Science, Leverkusen, Germany]: prothioconazole + tebuconazole) at the flag leaf stage on yield and agronomic characteristics of winter wheat. Foliar fungicide application increased grain yield, grain protein content, seed weight, grain volume weight, leaf greenness, and leaf area by up to 42, 1, 16, 6, 48, and 35%, respectively. Foliar fungicide at the flag leaf stage in the spring reduced disease severity (up to 84%), area under disease progress curve (up to 87%), and agronomic characteristics that resulted in economic returns of up to US$204 ha–¹ excluding the premium farmers get from increased protein. Susceptible to moderately susceptible genotypes were more responsive to foliar fungicide on controlling disease severity and improving grain yield compared to moderately resistant to resistant genotype when disease pressure was high. This study showed that the economic benefit for all genotypes obtained from improved grain yield, grain volume weight, grain protein content, and grain size due to one-time application of foliar fungicide at the flag leaf stage in winter wheat could have significant benefit to the farmer’s economy and wheat industry.

Development of winter wheat (Triticum aestivum) synthetics started at CIMMYT-Mexico in 2004, when winter durum wheat (Triticum turgidum) germplasm from Ukraine and Romania was crossed with Aegilops tauschii accessions from the Caspian Sea region. Chromosomes were doubled after pollination and embryo rescue, but chromosome number and cytological validation was not performed. F2 populations were grown in Mexico and were shipped to Turkey in 2008. During 2009–2015, these populations were subjected to rigorous pedigree selection under dry, cold, disease-affected environments of the Central Anatolian Plateau. The wide segregation and partial sterility observed in 2009 gradually decreased and, by 2016, most of the F8 single spike progenies demonstrated good fertility and agronomic performance. Since 2013, lines have been selected from synthetic populations and evaluated at multiple sites. Superior lines were characterized for resistance to leaf, stripe and stem rust, plant height, and reaction to common bunt and soil-borne pathogens. Thousand kernel weight of many lines exceeded 50 g, compared with the check varieties that barely reached 40 g. Threshability of synthetic lines varied from 0 to 95%, demonstrating genetic variation for this important domestication trait. Screening against Hessian fly, sunny pest and Russian wheat aphid identified several resistant genotypes. Both durum and Aegilops parents affected synthetic wheat traits. Several studies are underway to reveal the genetic diversity of synthetic lines and the basis of resistance to diseases and insects. This synthetic germplasm represents a new winter bread wheat parental pool. It is available upon request to interested breeding/research programmes.

Genetic resistance against biotic stress is a major goal in many wheat breeding programs. However, modern wheat cultivars have a limited genetic variation for disease and pest resistance and there is always a possibility of the evolution of new diseases and pests to overcome previously identified resistance genes. A total of 125 synthetic hexaploid wheats (SHWs, 2n = 6x = 42, AABBDD, Triticum aestivum L.) were characterized for resistance to fungal pathogens that cause wheat rusts (leaf, Puccinia triticina, stem, P. graminis f.sp. tritici, and stripe, P. striiformis f.sp. tritici) and crown rot (Fusarium spp.), cereal cyst nematode (Heterodera spp.), and Hessian fly (Mayetiola destructor). A wide range of genetic variation was observed among SHWs for multiple (two to five) biotic stresses and 17 SHWs that were resistant to more than two stresses. The genomic regions and potential candidate genes conferring resistance to these biotic stresses were identified from a genome-wide association study (GWAS). This GWAS study identified 124 significant marker-trait associations (MTAs) for multiple biotic stresses and 33 of these were found within genes. Furthermore, 16 of the 33 MTAs present within genes had annotations suggesting their potential role in disease resistance. These results will be valuable for pyramiding novel genes/genomic regions conferring resistance to multiple biotic stresses from SHWs into elite bread wheat cultivars and providing further insights on a wide range of stress resistance in wheat.

As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.

To develop more productive and resilient crops that are capable of feeding 10 billion people by 2050, we must accelerate the rate of genetic improvement in plant breeding programs. Speed breeding manipulates the growing environment by regulating light and temperature for the purpose of rapid generation advance. Protocols are now available for a range of short-day and long-day species and the approach is highly compatible with other cutting-edge breeding tools such as genomic selection. Here, we highlight how speed breeding hijacks biological processes for applied plant breeding outcomes and provide a case study examining wheat growth and development under speed breeding conditions. The establishment of speed breeding facilities worldwide is expected to provide benefits for capacity building, discovery research, pre-breeding, and plant breeding to accelerate the development of productive and robust crops.

Breeding programs for purple wheat are underway in many countries but there is a lack of information on the effects of Pp (purple pericarp) genes on agronomic and quality traits in variable environments and along the product chain (grain-flour-bread). This study was based on unique material: two pairs of isogenic lines in a spring wheat cv. Saratovskaya-29 (S29) background differing only in Pp genes and grain color. In 2017, seven experiments were conducted in Kazakhstan, Russia, and Turkey with a focus on genotype and environment interaction and, in 2018, one experiment in Turkey with a focus on grain, flour, and bread quality. The effect of environment was greater compared to genotype for the productivity and quality traits studied. Nevertheless, several important traits, such as grain color and anthocyanin content, are closely controlled by genotype, offering the opportunity for selection. Phenolic content in purple-grained lines was not significantly higher in whole wheat flour than in red-colored lines. However, this trait was significantly higher in bread. For antioxidant activities, no differences between the genotypes were detected in both experiments. Comparison of two sources of Pp genes demonstrated that the lines originating from cv. Purple Feed had substantially improved productivity and quality traits compared to those from cv. Purple.

Genetic resistance to common bunt is a cost-effective, environmentally friendly, and sustainable approach to controlling the disease. To date, 16 race specific common bunt resistance genes (Bt1-Bt15 and Btp) have been reported in wheat. However, a limited number have been mapped and few markers have been identified, which limits the usage of molecular markers in a marker-assisted breeding program. A total of 125 synthetic hexaploid wheats (SHWs) were evaluated for reactions to a mixture of common bunt races under field conditions in Turkey in 2016 and 2017. The objectives of this study were to identify common bunt resistant genotypes, identify genomic regions conferring resistance to common bunt using 35,798 genotyping-by-sequencing derived single nucleotide polymorphisms (SNPs), and investigate the significant SNPs present within genes using the functional annotations of the underlying genes. We found 29 resistant SHWs that can be used in wheat breeding. The genome-wide association study identified 15 SNPs associated with common bunt resistance and a haplotype block comprising three SNPs in perfect linkage disequilibrium. Five of them were novel and were located on chromosomes 2A, 3D, and 4A. Furthermore, seven of the 15 SNPs were found within genes and had annotations suggesting potential role in disease resistance. This study identified several favorable alleles that decreased common bunt incidence up to 26% in SHWs. These resistant SHWs and candidate genomic regions controlling common bunt resistance will be useful for wheat genetic improvement and could assist in further understanding of the genetic architecture of common bunt resistance.

Synthetic hexaploid wheat (SHW, Triticum durum L. &times, Aegilops tauschii Coss.) is a means of introducing novel genes/genomic regions into bread wheat (T. aestivum L.) and a potential genetic resource for improving grain mineral concentrations. We quantified 10 grain minerals (Ca, Cd, Cu, Co, Fe, Li, Mg, Mn, Ni, and Zn) using an inductively coupled mass spectrometer in 123 SHWs for a genome-wide association study (GWAS). A GWAS with 35,648 single nucleotide polymorphism (SNP) markers identified 92 marker-trait associations (MTAs), of which 60 were novel and 40 were within genes, and the genes underlying 20 MTAs had annotations suggesting a potential role in grain mineral concentration. Twenty-four MTAs on the D-genome were novel and showed the potential of Ae. tauschii for improving grain mineral concentrations such as Ca, Co, Cu, Li, Mg, Mn, and Ni. Interestingly, the large number of novel MTAs (36) identified on the AB genome of these SHWs indicated that there is a lot of variation yet to be explored and to be used in the A and B genome along with the D-genome. Regression analysis identified a positive correlation between a cumulative number of favorable alleles at MTA loci in a genotype and grain mineral concentration. Additionally, we identified multi-traits and stable MTAs and recommended 13 top 10% SHWs with a higher concentration of beneficial grain minerals (Cu, Fe, Mg, Mn, Ni, and Zn), a large number of favorable alleles compared to low ranking genotypes and checks that could be utilized in the breeding program for the genetic biofortification. This study will further enhance our understanding of the genetic architecture of grain minerals in wheat and related cereals.

Synthetic hexaploid wheat (SHW, 2n = 6x = 42, AABBDD, Triticum aestivum L.) is produced from an interspecific cross between durum wheat (2n = 4x = 28, AABB, T. turgidum L.) and goat grass (2n = 2x = 14, DD, Aegilops tauschii Coss.) and is reported to have significant novel alleles-controlling biotic and abiotic stresses resistance. A genome-wide association study (GWAS) was conducted to unravel these loci [marker&ndash, trait associations (MTAs)] using 35,648 genotyping-by-sequencing-derived single nucleotide polymorphisms in 123 SHWs. We identified 90 novel MTAs (45, 11, and 34 on the A, B, and D genomes, respectively) and haplotype blocks associated with grain yield and yield-related traits including root traits under drought stress. The phenotypic variance explained by the MTAs ranged from 1.1% to 32.3%. Most of the MTAs (120 out of 194) identified were found in genes, and of these 45 MTAs were in genes annotated as having a potential role in drought stress. This result provides further evidence for the reliability of MTAs identified. The large number of MTAs (53) identified especially on the D-genome demonstrate the potential of SHWs for elucidating the genetic architecture of complex traits and provide an opportunity for further improvement of wheat under rapidly changing climatic conditions.

Background Synthetic hexaploid wheat (SHW) is a reconstitution of hexaploid wheat from its progenitors (Triticum turgidum ssp. durum L.; AABB x Aegilops tauschii Coss.; DD) and has novel sources of genetic diversity for broadening the genetic base of elite bread wheat (BW) germplasm (T. aestivum L). Understanding the diversity and population structure of SHWs will facilitate their use in wheat breeding programs. Our objectives were to understand the genetic diversity and population structure of SHWs and compare the genetic diversity of SHWs with elite BW cultivars and demonstrate the potential of SHWs to broaden the genetic base of modern wheat germplasm. Results The genotyping-by-sequencing of SHW provided 35,939 high-quality single nucleotide polymorphisms (SNPs) that were distributed across the A (33%), B (36%), and D (31%) genomes. The percentage of SNPs on the D genome was nearly same as the other two genomes, unlike in BW cultivars where the D genome polymorphism is generally much lower than the A and B genomes. This indicates the presence of high variation in the D genome in the SHWs. The D genome gene diversity of SHWs was 88.2% higher than that found in a sample of elite BW cultivars. Population structure analysis revealed that SHWs could be separated into two subgroups, mainly differentiated by geographical location of durum parents and growth habit of the crop (spring and winter type). Further population structure analysis of durum and Ae. parents separately identified two subgroups, mainly based on type of parents used. Although Ae. tauschii parents were divided into two sub-species: Ae. tauschii ssp. tauschii and ssp. strangulate, they were not clearly distinguished in the diversity analysis outcome. Population differentiation between SHWs (Spring_SHW and Winter_SHW) samples using analysis of molecular variance indicated 17.43% of genetic variance between populations and the remainder within populations. Conclusions SHWs were diverse and had a clearly distinguished population structure identified through GBS-derived SNPs. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and is a prerequisite for association mapping and genomic selection to unravel economically important marker-trait associations and for cultivar development. Electronic supplementary material The online version of this article (10.1186/s12864-018-4969-2) contains supplementary material, which is available to authorized users.

Wheat (Triticum aestivum L.) is one of the most commonly cultivated and consumed cereals throughout the world. Though phytochemicals and antioxidants in the cereal grains have not been studied as in fruits and vegetables, given the role of wheat in our diet plate, it is a given of primary importance to understand the chemistry of our major food, wheat. The presence of diverse polyphenols and their action against leading cause of death, including heart diseases, cancer, obesity, and diabetes, widens the scope of wheat. Phytochemicals such as phenolic acids, alkylresorcinols, flavonoids, phytosterols, and carotenoids are present in whole wheat. The majority of phytochemicals are located in the wheat bran/germ fraction, and they are the leading contributors to the health promoting activities. However, the presence of anti-nutrients and binding of phenolic acids with protein may have adverse effect on health. This review mainly focuses on studies that have been carried out in the past decade to present, emphasizing the importance of whole wheat and whole wheat based products in preventing major diseases and disease conditions, potentials threats, current lacks, and future prospects.

Fine-tuning production inputs such as seeding rate, nitrogen (N), and genotype may improve end-use quality of hard red winter wheat (Triticum aestivium L.) when growing conditions are unpredictable. Studies were conducted at the Agronomy Research Farm (ARF; Lincoln, NE, USA) and the High Plains Agricultural Laboratory (HPAL; Sidney, NE, USA) in 2014 and 2015 in Nebraska, USA, to determine the effects of genotype (6), environment (4), seeding rate (3), and flag leaf top-dressed N (0 and 34 kg N haEnd-use quality traits were influenced by environment, genotype, seeding rate, top-dressed N, and their interactions. Mixograph parameters had a strong correlation with grain volume weight and flour yield. Doubling the recommended seeding rate and N at the flag leaf stage increased grain protein content by 8.1% in 2014 and 1.5% in 2015 at ARF and 4.2% in 2014 and 8.4% in 2015 at HPAL.The key finding of this research is that increasing seeding rates up to double the current recommendations with N at the flag leaf stage improved most of the end-use quality traits. This will have a significant effect on the premium for protein a farmer could receive when marketing wheat. © 2017 Society of Chemical Industry.

An important step towards reducing the vulnerability of wheat in Africa and Asia to the Ug99 race of the stem rust pathogen is the substitution of current susceptible varieties with superior resistant varieties. In the 2008–2009 cropping season both seed multiplication and dissemination of Ug99 resistant varieties were initiated in Afghanistan, Bangladesh, Egypt, Ethiopia, Iran, India, Nepal and Pakistan. Ug99 resistant varieties must occupy about 5% of the area sown to wheat in each country to ensure sufficient seed to displace current popular varieties. Because of the underdeveloped seed industry and small farm sizes in most of these countries, various strategies are being applied for rapid multiplication and dissemination of resistant varieties. Approaches being used include pre-release seed multiplication while candidate resistant lines are being tested in national evaluation trials and farmer participatory selection. Resistant varieties are already released in Afghanistan, Bangladesh, Egypt and Pakistan and more varieties are expected to be released in 2010 in these and other countries. Our results show that some new Ug99 resistant lines have yield superiority over dominant local varieties. Activities and progress in seed multiplication using existing and new Ug99 resistant varieties are discussed.

Improving the level and stability of grain yield is the primary objective of wheat breeding programs in the Eastern Gangetic Plains (EGP) of South Asia. A regional wheat trial, the Eastern Gangetic Plains Yield Trial (EGPYT), was initiated by CIMMYT in collaboration with national wheat research programs in Bangladesh, Nepal, and India in 1999–2000 to identify wheat genotypes with high and stable grain yield, disease resistance, and superior agronomic traits for the EGP region. A set of 21 wheat experimental genotypes selected from a regional wheat screening nursery in South Asia, three improved widely grown cultivars (Kanchan, PBW343 and Bhrikuti), and one long-term cultivar (Sonalika) were tested at 9–11 sites in six wheat growing seasons (2000–2005) in the EGP. The 21 experimental genotypes were different in each year, whereas the four check cultivars were common. In each year, one or more of the experimental genotypes showed high and stable grain yield and acceptable maturity, plant height, and disease resistance compared to the check cultivars. Three improved cultivars have already been commercially released in the region through EGPYT and many germplasm lines have been used in the breeding programs as parents. Identification of wheat genotypes with high-grain yield in individual sites and high and stable yield across the EGP region underlines their value for regional wheat breeding programs attempting to improve grain yield and agronomic performance.

Spot blotch is an important disease of wheat (Triticum aestivum L.) in South Asia. Division of test sites for this disease into homogenous subregions is expected to contribute to more efficient evaluation and better differentiation of cultivars. Data from a collaborative regional program of South Asia conducted by CIMMYT were analyzed to group testing sites into relatively homogenous subregions for spot blotch area under the disease progress curve (AUDPC). Five-year data of eight locations from Eastern Gangetic Plains Nursery (EGPSN) and five locations of the Eastern Gangetic Plains Yield Trial (EGPYT) conducted in three countries (India, Nepal, and Bangladesh) of South Asia were used. A hierarchical cluster analysis was used to group locations on the basis of genotype x location interaction effects for spot blotch AUDPC. Cluster analysis divided South Asia into two broad regions and four subregions. This classification was not entirely consistent with the geographic distribution of locations, but clusters mostly followed general geographic-climatic locations. The locations Varanasi (India) and Bhairahawa (Nepal) were identified as the most suitable sites for evaluation of spot blotch, followed by Rampur (Nepal). The major determinant for the clustering was mean temperature. The results suggest that the major wheat region of South Asia can be divided into subregions, which may reduce the cost of resistance evaluation and aid in developing wheat with resistance to this disease.

There are many socioeconomic and technological constraints that affect the production of wheat and other staple cereals in South Asia. Wheat production is one of the economic mainstays in South Asia, but the yield gap between farmers’ fields and experimental yields is wide across the region. For the last 3 years, CIMMYT and the CAZS-NR have been collaborating with farmers, NARS, and other South Asian partners to promote improved wheat varieties and new resource conservation technologies (RCTs) in farmers’ fields. Participation fostered among farmers, scientists, extension specialists, NGOs and the private sector included variety selection (PVS), and evaluation of agronomic practices. Through PVS, several farmer-preferred technologies have been identified including wheat varieties for adverse conditions in eastern Uttar Pradesh (India) and for boron deficiency in parts of Nepal. There has been considerable improvement in the access of farmers to new varieties and technologies in the rural areas. Yield increases (15–70%) have been achieved by resource-poor farmers over the existing varieties through the adoption of new varieties and RCTs. The farmers have also made substantial cost savings and achieved higher yields through resource-conserving agronomic techniques such as zero till. Seed of the new farmer-selected cultivars has been multiplied by groups of collaborating farmers and widely distributed.