Winter Wheat Breeding Programs Research Articles

Application of SSR markers for assessment of genetic similarity and genotype identification in local winter wheat breeding program

Background. Simple sequence repeat (SSR) markers are widely used for genetic analysis in plant breeding, allowing for the investigation of genetic divergence and similarity of genotypes, identification of unique alleles and determination of levels of genetic diversity. Materials and Methods. Analysis of 42 wheat cultivars and lines from the bree­ding program of Poltava State Agrarian University was carried out using 11 SSR mar­kers located on different chromosomes. A set of 11 microsatellite single locus primer pairs was used in this study (Xgwm 11, Xgwm 44, Xgwm 46, Xgwm 135, Xgwm 174, Xgwm 186, Xgwm 194, Xgwm 219, Xgwm 312, Xgwm 372, Xgwm 389). Amplification of 11 loci was performed using the Kapa2G FastHotStart PCR Kit (Kapa Biosystems, Boston, USA). The mixture for PCR amplification contained 1.5 x Kapa2G buffer, 0.5 mM dNTP mix, 0.5 μM of each primer (Sigma-Aldrich), 1 unit of Kapa2G FastHotStart DNA Polymerase and 11.8 ng of template DNA in a volume of 25 μl. Fragment lengths were determined using GeneMapper 4.0 software (Applied Biosystems). Dendrogram was constructed using UPGMA (unweighted pair-group method with arithmetic average) in DarWin 6.0 software (Perrier and Jacquemoud-Collet 2006) for clustering analysis. Results and Discussion. The number of alleles detected per locus varied from 5 (Xgwm 11, Xgwm 135, Xgwm 219) to 12 (Xgwm 174). A total of 80 alleles were identified for the 11 loci studied. Among these, 25 unique alleles were found, each of which was present in only one genotype. The polimorphism information content (PIC) values ranged from 0.48 to 0.87. The markers Xgwm 174 (PIC = 0.87), Xgwm 389 (PIC = 0.84) and Xgwm 372 (PIC = 0.83) were the most polymorphic in our study. We obtained a distribution of cultivars and lines by genetic similarity into five clusters. Conclusion. The use of SSR markers made it possible to identify rare alleles within the varieties presented. The study of the genetic similarity of the presented geno­types showed their relationship according to their origin. It was shown that unique alleles tended to occur in certain local breeding genotypes. This study has shown that genotypes representing the local Ukrainian breeding program often have the same allelic variants and at the same time some genotypes have unique allelic variants. The results obtained from the study of 42 winter wheat genotypes based on 11 SSR markers showed that molecular markers can be very useful in assessing genetic similarity and identifying genotypes in the local breeding program.

Genomic selection for agronomic traits in a winter wheat breeding program.

rAMP-seq based genomic selection for agronomic traits has been shown to be a useful tool for winter wheat breeding programs by increasing the rate of genetic gain. Genomic selection (GS) is an effective strategy to employ in a breeding program that focuses on optimizing quantitative traits, which results in the ability for breeders to select the best genotypes. GS was incorporated into a breeding program to determine the potential for implementation on an annual basis, with emphasis on selecting optimal parents and decreasing the time and costs associated with phenotyping large numbers of genotypes. The design options for applying repeat amplification sequencing (rAMP-seq) in bread wheat were explored, and a low-cost single primer pair strategy was implemented. A total of 1870 winter wheat genotypes were phenotyped and genotyped using rAMP-seq. The optimization of training to testing population size showed that the 70:30 ratio provided the most consistent prediction accuracy. Three GS models were tested, rrBLUP, RKHS and feed-forward neural networks using the University of Guelph Winter Wheat Breeding Program (UGWWBP) and Elite-UGWWBP populations. The models performed equally well for both populations and did not differ in prediction accuracy (r) for most agronomic traits, with the exception of yield, where RKHS performed the best with an r = 0.34 and 0.39 for each population, respectively. The ability to operate a breeding program where multiple selection strategies, including GS, are utilized will lead to higher efficiency in the program and ultimately lead to a higher rate of genetic gain.

Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program

Key messageWe used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods.Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008–2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive ability and genetic variance overall, but differences between models were small. Including QTL as covariates improved predictive ability in some years where highly significant QTL had been detected via genome-wide association analysis. Predictive ability was moderate within years (cross-validated) but poor in cross-year frameworks.

Effects of Fhb1, Fhb2 and Fhb5 on Fusarium Head Blight Resistance and the Development of Promising Lines in Winter Wheat.

The development of Fusarium head blight (FHB)-resistant winter wheat cultivars using the gene Fhb1 has been conducted in northern China. Sumai 3, a Chinese FHB-resistant spring wheat cultivar, carries three FHB resistance genes: Fhb1, Fhb2 and Fhb5. To better use these genes for increasing FHB resistance in northern China, it is necessary to elucidate the pyramiding effects of Fhb1, Fhb2 and Fhb5 in winter wheat backgrounds. Eight gene combinations involving Fhb1, Fhb2 and Fhb5 were identified in a double haploid (DH) population, and the effects on FHB resistance were evaluated in six tests. At the single gene level, Fhb1 was more efficient than the other two genes in single-floret inoculation tests, whereas Fhb5 showed better resistance than Fhb1 and Fhb2 under a natural infection test. Pyramiding Fhb1, Fhb2 and Fhb5 showed better FHB resistance than the other gene combinations. Forty-nine DH lines showing consistently better resistance than the moderately susceptible control Huaimai 20 in multiple tests were evaluated for main agronomic traits, and no difference in grain yield was found between the mean values of DH lines and the recipient parents Lunxuan 136 and Lunxuan 6, which are higher than those of recipient parent Zhoumai 16 and the donor parent Sumai 3 (p < 0.05). Based on the phenotypic and genomic composition analyses, five promising DH lines fully combined the FHB resistance of donor Sumai 3 and the elite agronomic traits from the recipient parents. This study elucidates the pyramiding effects of three FHB resistance genes and that the promising DH lines with resistance to FHB can be directly applied in wheat production or as parents in winter wheat breeding programs.

Registration of the PI 173438/WA 8137 wheat doubled haploid mapping population

AbstractA doubled haploid mapping population was developed from a cross between the hard red winter wheat (Triticum aestivum L. subsp. aestivum) landrace PI 173438 and WA 8137, a soft white winter wheat breeding line developed by the Washington State University winter wheat breeding program in Pullman, WA. The PI 173438/WA 8137 (Reg. no. MP‐16, NSL 543858 MAP) population consists of 437 individuals, with 358 of these individuals used to identify quantitative trait loci associated with snow mold (Typhula spp.) tolerance in PI 173438. Genotypic information was gathered on these individuals using genotype‐by‐sequencing and processed using a pipeline developed by the USDA Eastern Regional Small Grains Genotyping Laboratory. Twenty‐three linkage groups covering the whole genome with groups for the long and short arms of chromosomes 3D and 7D were constructed using 4,029 single nucleotide polymorphisms from the sequenced individuals. Quantitative trait loci associated with snow mold tolerance and snow mold recovery attributed to PI 173438 were successfully identified in this population. PI 173438 has also been used to identify other traits associated with snow mold tolerance, such as freezing tolerance and carbohydrate reserves, and has been important in research investigating dwarf and common bunt. This population may continue to expand current knowledge in these areas, and, given the landrace status of PI 173438, the population may be useful in identifying other novel traits of interest.

Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat.

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled 1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.

The contribution of stay green traits to the breeding progress of the pannonian wheat

The aim of the study was to examine the variation and breeding effects on the stay-green traits, and to identify the relationship between the different growing seasons and grain yield of the Pannonian winter wheat. The experiment was carried out with a historical set of 25 winter wheat cultivars during four growing seasons, in a typical Pannonian location at the experimental field of the Institute of Field and Vegetable Crops, Novi Sad, Serbia. The results demonstrated a linear wheat grain yield progress at the rate of 45.7 kg ha −1 yr −1 , along with significant variations observed in most stay-green traits due to increased capability of modern wheat cultivars to retain prolonged crop greenness. Furthermore, significant positive correlations between the onset and mid senescence and grain yield in three out of four studied seasons showed that the prolonged senescence led to grain yield increase. Modern wheat cultivars showed an increase of crop greenness at anthesis, onset and mid senescence, and the stay-green interval, as well as a faster senescence rate. Moreover, the stay-green interval was positively correlated with grain yield in each growing season, while the senescence rate was significantly positively correlated with grain yield in the three growing seasons. Furthermore, all examined traits, except the endpoint of senescence (N10), were significantly negatively correlated with anthesis time during all growing seasons. Knowledge about the positive correlations between the prolonged stay-green traits and grain yield could be used for adjusting post-anthesis senescence traits and implemented in winter wheat breeding programs under the climate conditions of the Pannonian Plain. • Significant progress in grain yield over the year of cultivar release. • Modern cultivars have higher NDVI values and prolonged stay green duration compared to the older cultivars. • Close relationship between the stay-green duration indicators with grain yield. • Anthesis date is negatively related to the duration of senescence stages.

Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.

Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G × E). Phenomic selection is supposed to be efficient for modelling the G × E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G × E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G × E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G × E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G × E.

Developing high-quality value-added cereals for organic systems in the US Upper Midwest: hard red winter wheat (Triticum aestivum L.) breeding

There is an increased demand for food-grade grains grown sustainably. Hard red winter wheat has comparative advantages for organic farm rotations due to fall soil cover, weed competition, and grain yields. However, limitations of currently available cultivars such as poor disease resistance, winter hardiness, and baking quality, challenges its adoption and use. Our goal was to develop a participatory hard red winter wheat breeding program for the US Upper Midwest involving farmers, millers, and bakers. Specifically, our goals include (1) an evaluation of genotype-by-environment interaction (GEI) and genotypic stability for both agronomic and quality traits, and (2) the development of on-farm trials as well as baking and sensory evaluations of genotypes to include farmers, millers, and bakers’ perspectives in the breeding process. Selection in early generations for diseases and protein content was followed by multi-environment evaluations for agronomic, disease, and quality traits in three locations during five years, on-farm evaluations, baking trials, and sensory evaluations. GEI was substantial for most traits, but no repeatable environmental conditions were significant contributors to GEI making selection for stability a critical trait. Breeding lines had similar performance in on-station and on-farm trials compared to commercial checks, but some breeding lines were more stable than the checks for agronomic, quality traits, and baking performance. These results suggest that stable lines can be developed using a participatory breeding approach under organic management. Crop improvement explicitly targeting sustainable agriculture practices for selection with farm to table participatory perspectives are critical to achieve long-term sustainable crop production.Key messageWe describe a hard red winter wheat breeding program focused on developing genotypes adapted to organic systems in the US Upper Midwest for high-end artisan baking quality using participatory approaches.

Genomic prediction of Fusarium head blight resistance in early stages using advanced breeding lines in hard winter wheat

Fusarium head blight (FHB), also known as scab, is a devastating fungal disease of wheat that causes significant losses in grain yield and quality. Quantitative inheritance and cumbersome phenotyping make FHB resistance a challenging trait for direct selection in wheat breeding. Genomic selection to predict FHB resistance traits has shown promise in several studies. Here, we used univariate and multivariate genomic prediction models to evaluate the prediction accuracy (PA) for different FHB traits using 476 elite and advanced breeding lines developed by South Dakota State University hard winter wheat breeding program. These breeding lines were assessed for FHB disease index (DIS), and percentage of Fusarium damaged kernels (FDK) in three FHB nurseries in 2018, 2019, and 2020 (TP18, TP19, and TP20) and were evaluated as training populations (TP) for genomic prediction (GP) of FHB traits. We observed a moderate PA using univariate models for DIS (0.39 and 0.35) and FDK (0.35 and 0.37) using TP19 and TP20, respectively, while slightly higher PA was observed (0.41 for DIS and 0.38 for FDK) when TP19 and TP20 (TP19 + 20) were combined to leverage the advantage of a large training population. Although GP with multivariate approach including plant height and days to heading as covariates did not significantly improve PA for DIS and FDK over univariate models, PA for DON increased by 20% using DIS, FDK, DTH as covariates using multi-trait model in 2020. Finally, we used TP19, TP20, and TP19 + 20 in forward prediction to calculate genomic-estimated breeding values (GEBVs) for DIS and FDK in preliminary breeding lines at an early stage of the breeding program. We observed moderate PA of up to 0.59 for DIS and 0.54 for FDK, demonstrating the promise in genomic prediction for FHB resistance in earlier stages using advanced lines. Our results suggest GP for expensive FHB traits like DON and FDK can facilitate the rejection of highly susceptible materials at an early stage in a breeding program.

Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection.

Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.

Incorporating Molecular Markers and Causal Structure among Traits Using a Smith-Hazel Index and Structural Equation Models

The goal in breeding programs is to choose candidates that produce offspring with the best phenotypes. In conventional selection, the best candidate is selected with high genotypic values (unobserved), in the assumption that this is related to the observed phenotypic values for several traits. Multi-trait selection indices are used to identify superior genotypes when a number of traits are to be considered simultaneously. Often, the causal relationship among the traits is well known. Structural equation models (SEM) have been used to describe the causal relationships among variables in many biological systems. We present a method for multi-trait genomic selection that incorporates causal relationships among traits by coupling SEM with a Smith–Hazel index that incorporates markers. The method was applied to field data from a Nebraska winter wheat breeding program. We found that the correlation and the relative efficiency increased for the proposed Smith–Hazel indices when the total causal information among traits was accounted for by the vector of weights (b), which includes the causal path coefficients in the causal matrix (Λ). On the other hand, when selection was based on a primary trait, for example yield, the proposed SI increased the mean yield of the best 28 (Top 10%) genotypes to 7%.

Using Genomic Selection to Leverage Resources among Breeding Programs: Consortium-Based Breeding

Genomic selection has many applications within individual programs. Here, we discuss the benefits of forming a GS-based breeding consortium (GSC) among programs within the context of a recently formed a GSC of soft red winter wheat breeding programs. The GSC will genotype lines from each member breeding program (MBP) and conduct cooperative phenotyping. The primary GSC benefit is that each MBP can use GS to predict the local and broad value of all germplasm from all MBPs including lines in the early stages of testing, thus increasing the effective size of each MBP without significant new investment. We identified eight breeding aspects that are essential to GSC success and analyzed how our GSC fits those criteria. We identified a core of &gt;5700 related lines from the MBPs that can serve in training populations. Germplasm from each MBP provided breeding value to other MBPs and program-specific adaption was low. GS accuracy was acceptable within programs but was low between programs when using training populations with little testing connectivity, but increased when using data from trials with high testing connectivity between MBPs. In response we initiated sparse-testing with a germplasm sharing scheme utilizing family relationship to connect our phenotyping of early-stage lines.

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models.

Simple SummaryWheat (Triticum aestivum L.) breeding programs mainly focus on improving grain yield, biotic and abiotic stress tolerance, and end-use quality traits. End-use quality and processing traits are the combinations of various predefined parameters. Genomic selection (GS) opens the potential for selecting improved end-use quality lines. This study explored the potential of the machine and deep learning-based GS models for predicting end-use quality traits in wheat. Deep models were superior to traditional statistical and Bayesian models under all the prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails.Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat

Key messageWe combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018.Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.

Root and canopy traits and adaptability genes explain drought tolerance responses in winter wheat.

Bread wheat (Triticum aestivum L) is one of the three main staple crops worldwide contributing 20% calories in the human diet. Drought stress is the main factor limiting yields and threatening food security, with climate change resulting in more frequent and intense drought. Developing drought-tolerant wheat cultivars is a promising way forward. The use of holistic approaches that include high-throughput phenotyping and genetic markers in selection could help in accelerating genetic gains. Fifty advanced breeding lines were selected from the CIMMYT Turkey winter wheat breeding program and studied under irrigated and semiarid conditions in two years. High-throughput phenotyping was done for wheat crown root traits and canopy senescence dynamics using vegetation indices (green area using RGB images and Normalized Difference Vegetation Index using spectral reflectance). In addition, genotyping by KASP markers for adaptability genes was done. Overall, under semiarid conditions yield reduced by 3.09 t ha-1 (-46.8%) compared to irrigated conditions. Genotypes responded differently under drought stress and genotypes 39 (VORONA/HD24-12//GUN/7/VEE#8//…/8/ALTAY), 18 (BiII98) and 29 (NIKIFOR//KROSHKA) were the most drought tolerant. Root traits including shallow nodal root angle under irrigated conditions and root number per shoot under semiarid conditions were correlated with increased grain yield. RGB based vegetation index measuring canopy green area at anthesis was better correlated with GY than NDVI was with GY under drought. The markers for five established functional genes (PRR73.A1 –flowering time, TEF-7A –grain size and weight, TaCwi.4A - yield under drought, Dreb1- drought tolerance, and ISBW11.GY.QTL.CANDIDATE- grain yield) were associated with different drought-tolerance traits in this experiment. We conclude that–genotypes 39, 18 and 29 could be used for drought tolerance breeding. The trait combinations of canopy green area at anthesis, and root number per shoot along with key drought adaptability makers (TaCwi.4A and Dreb1) could be used in screening drought tolerance wheat breeding lines.

GWAS revealed effect of genotype \xd7 environment interactions for grain yield of Nebraska winter wheat

BackgroundImproving grain yield in cereals especially in wheat is a main objective for plant breeders. One of the main constrains for improving this trait is the G × E interaction (GEI) which affects the performance of wheat genotypes in different environments. Selecting high yielding genotypes that can be used for a target set of environments is needed. Phenotypic selection can be misleading due to the environmental conditions. Incorporating information from phenotypic and genomic analyses can be useful in selecting the higher yielding genotypes for a group of environments.ResultsA set of 270 F3:6 wheat genotypes in the Nebraska winter wheat breeding program was tested for grain yield in nine environments. High genetic variation for grain yield was found among the genotypes. G × E interaction was also highly significant. The highest yielding genotype differed in each environment. The correlation for grain yield among the nine environments was low (0 to 0.43). Genome-wide association study revealed 70 marker traits association (MTAs) associated with increased grain yield. The analysis of linkage disequilibrium revealed 16 genomic regions with a highly significant linkage disequilibrium (LD). The candidate parents’ genotypes for improving grain yield in a group of environments were selected based on three criteria; number of alleles associated with increased grain yield in each selected genotype, genetic distance among the selected genotypes, and number of different alleles between each two selected parents.ConclusionAlthough G × E interaction was present, the advances in DNA technology provided very useful tools and analyzes. Such features helped to genetically select the highest yielding genotypes that can be used to cross grain production in a group of environments.

The Aegilops ventricosa 2NvS segment in bread wheat: cytology, genomics and breeding

Key messageThe first cytological characterization of the 2NvS segment in hexaploid wheat; complete de novo assembly and annotation of 2NvS segment; 2NvS frequency is increasing 2NvS and is associated with higher yield.The Aegilops ventricosa 2NvS translocation segment has been utilized in breeding disease-resistant wheat crops since the early 1990s. This segment is known to possess several important resistance genes against multiple wheat diseases including root knot nematode, stripe rust, leaf rust and stem rust. More recently, this segment has been associated with resistance to wheat blast, an emerging and devastating wheat disease in South America and Asia. To date, full characterization of the segment including its size, gene content and its association with grain yield is lacking. Here, we present a complete cytological and physical characterization of this agronomically important translocation in bread wheat. We de novo assembled the 2NvS segment in two wheat varieties, ‘Jagger’ and ‘CDC Stanley,’ and delineated the segment to be approximately 33 Mb. A total of 535 high-confidence genes were annotated within the 2NvS region, with > 10% belonging to the nucleotide-binding leucine-rich repeat (NLR) gene families. Identification of groups of NLR genes that are potentially N genome-specific and expressed in specific tissues can fast-track testing of candidate genes playing roles in various disease resistances. We also show the increasing frequency of 2NvS among spring and winter wheat breeding programs over two and a half decades, and the positive impact of 2NvS on wheat grain yield based on historical datasets. The significance of the 2NvS segment in wheat breeding due to resistance to multiple diseases and a positive impact on yield highlights the importance of understanding and characterizing the wheat pan-genome for better insights into molecular breeding for wheat improvement.

Application of selection indices in the context of the winter wheat breeding program

Aim. The aim of this study is to analyze breeding progress using indices as ratios of quantitative traits, based on long-term data analysis of plant quantitative parameters that directly or indirectly affect the productivity of varieties and breeding lines. Methods. Long-term data of structural analysis of plants of wheat varieties and lines of winter Poltava breeding for 2000-2018 for a number of quantitative traits, harvest index and attraction index were taken as material for the study. Statistica 7 was used for statistical data processing. Results. A significant increase of the harvest index under the pressure of selection for the period 2000-2018 is shown. In the early breeding stages during the selection by attraction index, it is necessary to adhere to the limits of oscillation of this index, set by the breeder in accordance with their model of variety, eliminating genotypes with sharp fluctuations in the value of this index. Conclusions. In breeding programs, in order to improve the productivity of winter wheat, it is recommended to select by attraction index in early stages of breeding, to form groups of lines with attraction index of greater than one, to search for lines with the maximum value of the harvest index, to select breeding nurseries for high seed productivity, paying particular attention to the strength of the stem.&#x0D; Keywords: winter wheat, selection indices, harvest index, attraction index, traits.

Physicochemical and rheological properties of the derivatives of Ningmai 9 wheat and their relationship with sugar-snap cookie diameter

In recent years, the annual demand for soft wheat in China has exceeded six million metric tons. The development of soft wheat is an important objective in the Yangtze River Basin winter wheat region in China. Ningmai 9 is a soft wheat cultivar widely used as a parent in the soft red winter wheat breeding program. However, the physicochemical and rheological properties of Ningmai 9 and its derivatives remain unknown. In this study, Ningmai 9 and its thirty-one derivatives were grown in Nanjing in Jiangsu Province in two successive cropping seasons from 2015 to 2017. The kernel samples were analyzed for milling quality, solvent retention capacity (SRC), alveograph parameters, and sugar-snap cookie diameter. The flour protein content was not significantly associated with any other quality trait. P, L, P/L (the ratio of tenacity to extensibility), alveograph deformation work (W), and four types of SRC were significantly correlated with sugar-snap cookie diameter. Compared to Ningmai 9, seven wheat genotypes had relatively better cookie quality and lower SRC and P values. A multiple regression model with water SRC (WSRC) as a variable explained 72.5% of the total variation in cookie diameter. These results suggest that decreasing SRC and gluten strength is the key to improving soft wheat breeding programs.