Genetic Dissection of End-Use Quality Traits in Adapted Soft White Winter Wheat

A Genome-Wide Association Study Approach to Identify Novel Major-Effect Quantitative Trait Loci for End-Use Quality Traits in Soft Red Winter Wheat

Wheat is used for making many food products due to its diverse quality profile among different wheat classes. Since laboratory analysis of these end-use quality traits is costly and time-consuming, genetic dissection of the traits is preferential. This study used a genome-wide association study (GWAS) of ten end-use quality traits, including kernel protein, flour protein, flour yield, softness equivalence, solvent's retention capacity, cookie diameter, and top-grain, in soft red winter wheat (SRWW) adapted to US southeast. The GWAS included 266 SRWW genotypes that were evaluated in two locations over two years (2020-2022). A total of 27,466 single nucleotide markers were used, and a total of 80 significant marker-trait associations were identified. There were 13 major-effect quantitative trait loci (QTLs) explaining >10% phenotypic variance, out of which, 12 were considered to be novel. Five of the major-effect QTLs were found to be stably expressed across multiple datasets, and four showed associations with multiple traits. Candidate genes were identified for eight of the major-effect QTLs, including genes associated with starch biosynthesis and nutritional homeostasis in plants. These findings increase genetic comprehension of these end-use quality traits and could potentially be used for improving the quality of SRWW.

Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

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.

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat

This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.

Genetic architecture of end-use quality traits in soft white winter wheat

Background

Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States.

Results

This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection.

Conclusions

In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08676-5.

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction

In hard-winter wheat (Triticum aestivum L.) breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard-winter wheat. Advanced breeding lines (n = 462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25-0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.

Environment characterization and genomic prediction for end-use quality traits in soft white winter wheat

End-use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end-use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi-environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end-use quality traits. Genetic correlations between environments based on factor analytic models showed low-to-moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single-nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end-use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1-11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.

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

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.

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014-2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Toward the Genetic Basis and Multiple QTLs of Kernel Hardness in Wheat

Kernel hardness is one of the most important single traits of wheat seed. It classifies wheat cultivars, determines milling quality and affects many end-use qualities. Starch granule surfaces, polar lipids, storage protein matrices and Puroindolines potentially form a four-way interaction that controls wheat kernel hardness. As a genetic factor, Puroindoline polymorphism explains over 60% of the variation in kernel hardness. However, genetic factors other than Puroindolines remain to be exploited. Over the past two decades, efforts using population genetics have been increasing, and numerous kernel hardness-associated quantitative trait loci (QTLs) have been identified on almost every chromosome in wheat. Here, we summarize the state of the art for mapping kernel hardness. We emphasize that these steps in progress have benefitted from (1) the standardized methods for measuring kernel hardness, (2) the use of the appropriate germplasm and mapping population, and (3) the improvements in genotyping methods. Recently, abundant genomic resources have become available in wheat and related Triticeae species, including the high-quality reference genomes and advanced genotyping technologies. Finally, we provide perspectives on future research directions that will enhance our understanding of kernel hardness through the identification of multiple QTLs and will address challenges involved in fine-tuning kernel hardness and, consequently, food properties.

Genome-based prediction of multiple wheat quality traits in multiple years

Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait ALVPL. In contrast, under Pearson's correlation, the best predictions depended on the data set. The results obtained suggest that the BMTME model should be preferred for multi-trait prediction analyses. This model allows to obtain not only the correlation among traits, but also the correlation among environments, helping to increase the prediction accuracy.

Prospects of GWAS and predictive breeding for European winter wheat's grain protein content, grain starch content, and grain hardness

Grain quality traits determine the classification of registered wheat (Triticum aestivum L.) varieties. Although environmental factors and crop management practices exert a considerable influence on wheat quality traits, a significant proportion of the variance is attributed to the genetic factors. To identify the underlying genetic factors of wheat quality parameters viz., grain protein content (GPC), grain starch content (GSC), and grain hardness (GH), we evaluated 372 diverse European wheat varieties in replicated field trials in up to eight environments. We observed that all of the investigated traits hold a wide and significant genetic variation, and a significant negative correlation exists between GPC and GSC plus grain yield. Our association analyses based on 26,694 high-quality single nucleotide polymorphic markers revealed a strong quantitative genetic nature of GPC and GSC with associations on groups 2, 3, and 6 chromosomes. The identification of known Puroindoline-b gene for GH provided a positive analytic proof for our studies. We report that a locus QGpc.ipk-6A controls both GPC and GSC with opposite allelic effects. Based on wheat's reference and pan-genome sequences, the physical characterization of two loci viz., QGpc.ipk-2B and QGpc.ipk-6A facilitated the identification of the candidate genes for GPC. Furthermore, by exploiting additive and epistatic interactions of loci, we evaluated the prospects of predictive breeding for the investigated traits that suggested its efficient use in the breeding programs.

Multifamily QTL analysis and comprehensive design of genotypes for high-quality soft wheat

Milling properties and flour color are essential selection criteria in soft wheat breeding. However, high phenotypic screening costs restrict selection to relatively few breeding lines in late generations. To achieve marker-based selection of these traits in early generations, we performed genetic dissection of quality traits using three doubled haploid populations that shared the high-quality soft wheat variety Kitahonami as the paternal parent. An amplicon sequencing approach allowed effective construction of well-saturated linkage maps of the populations. Marker-based heritability estimates revealed that target quality traits had relatively high values, indicating the possibility of selection in early generations. Taking advantage of Chinese Spring reference sequences, joint linkage maps of the three populations were generated. Based on the maps, multifamily quantitative trait locus (QTL) analysis revealed a total of 86 QTLs for ten traits investigated. In terms of target quality traits, 12 QTLs were detected for flour yield, and 12 were detected for flour redness (a* value). Among these QTLs, six for flour yield and nine for flour a* were segregating in more than two populations. Some relationships among traits were explained by QTL collocations on chromosomes, especially group 7 chromosomes. Ten different ideotypes with various combinations of favorable alleles for the flour yield and flour a* QTLs were generated. Phenotypes of derivatives from these ideotypes were predicted to design ideal genotypes for high-quality wheat. Simulations revealed the possibility of breeding varieties with better quality than Kitahonami.

High Density Mapping of Quantitative Trait Loci Conferring Gluten Strength in Canadian Durum Wheat

Gluten strength is one of the factors that determine the end-use quality of durum wheat and is an important breeding target for this crop. To characterize the quantitative trait loci (QTL) controlling gluten strength in Canadian durum wheat cultivars, a population of 162 doubled haploid (DH) lines segregating for gluten strength and derived from cv. Pelissier × cv. Strongfield was used in this study. The DH lines, parents, and controls were grown in 3 years and two seeding dates in each year and gluten strength of grain samples was measured by sodium dodecyl sulfate (SDS)-sedimentation volume (SV). With a genetic map created by genotyping the DH lines using the Illumina Infinium iSelect Wheat 90K SNP (single nucleotide polymorphism) chip, QTL contributing to gluten strength were detected on chromosome 1A, 1B, 2B, and 3A. Two major and stable QTL detected on chromosome 1A (QGlu.spa-1A) and 1B (QGlu.spa-1B.1) explaining 13.7-18.7% and 25.4-40.1% of the gluten strength variability respectively were consistently detected over 3 years, with the trait increasing alleles derived from Strongfield. Putative candidate genes underlying the major QTL were identified. Two novel minor QTL (QGlu.spa-3A.1 and QGlu.spa-3A.2) with the trait increasing allele derived from Pelissier were mapped on chromosome 3A explaining up to 8.9% of the phenotypic variance; another three minor QTL (QGlu.spa-2B.1, QGlu.spa-2B.2, and QGlu.spa-2B.3) located on chromosome 2B explained up to 8.7% of the phenotypic variance with the trait increasing allele derived from Pelissier. QGlu.spa-2B.1 is a new QTL and has not been reported in the literature. Multi-environment analysis revealed genetic (QTL) × environment interaction due to the difference of effect in magnitude rather than the direction of the QTL. Eleven pairs of digenic epistatic QTL were identified, with an epistatic effect between the two major QTL of QGlu.spa-1A and QGlu.spa-1B.1 detected in four out of six environments. The peak SNPs and SNPs flanking the QTL interval of QGlu.spa-1A and QGlu.spa-1B.1 were converted to Kompetitive Allele Specific PCR (KASP) markers, which can be deployed in marker-assisted breeding to increase the efficiency and accuracy of phenotypic selection for gluten strength in durum wheat. The QTL that were expressed consistently across environments are of great importance to maintain the gluten strength of Canadian durum wheat to current market standards during the genetic improvement.

Genome-wide association study of six quality traits reveals the association of the TaRPP13L1 gene with flour colour in Chinese bread wheat

Flour colour, kernel hardness, grain protein content and wet gluten content are important quality properties that determine end use in bread wheat. Here, a wheat 90K genotyping assay was used for a genome-wide association study (GWAS) of the six quality-related traits in Chinese wheat cultivars in eight environments over four years. A total of 846 significant single nucleotide polymorphisms (SNPs) were identified, explaining approximately 30% of the phenotypic variation on average, and 103 multienvironment-significant SNPs were detected in more than four environments. Quantitative trait loci (QTL) mapping in the biparent population confirmed some important SNP loci. Moreover, it was determined that some important genes were associated with the six quality traits, including some known functional genes and annotated unknown functional genes. Of the annotated unknown functional genes, it was verified that TaRPP13L1 was associated with flour colour. Wheat cultivars or lines with TaRPP13L1-B1a showed extremely significantly higher flour redness and lower yellowness than those with TaRPP13L1-B1b in the Chinese wheat natural population and the doubled haploid (DH) population. Two tetraploid wheat lines with premature stop codons of the TaRPP13L1 gene mutagenized by ethyl methanesulfonate (EMS) showed extremely significantly higher flour redness and lower yellowness than wild type. Our data suggest that the TaRPP13L1 gene plays an important role in modulating wheat flour colour. This study provides useful information for further dissection of the genetic basis of flour colour and also provides valuable genes or genetic loci for marker-assisted selection to improve the process of breeding quality wheat in China.

Evaluating the Utility of Carbon Isotope Discrimination for Wheat Breeding in the Pacific Northwest

Many wheat (Triticum aestivum L.) production regions are threatened annually by drought stress. Carbon isotope discrimination (Δ) has been identified as a potentially useful trait in breeding for improved drought tolerance in certain environments. Broad use of Δ as a selection criterion is limited, however, mainly due to an inconsistent relationship observed between grain yield and Δ and, to a lesser extent, because of the high resource demand associated with phenotyping. The efficiency of selection may be improved by the identification and verification of molecular markers for use in marker-assisted selection (MAS), and a reliable relationship to grain yield may be established based on a location's total amount and distribution of precipitation over the growing season. Given the environmental variability in precipitation dynamics, it is necessary to evaluate this relationship in target breeding environments. In this study, grain Δ was collected on a panel of 480 advanced soft white winter wheat varieties grown in five Pacific Northwest environments. A genome-wide association study approach was used to evaluate the amenability of grain Δ to MAS. The genetic architecture of grain Δ was determined to be characterized by multiple, small effect marker-trait associations with limited repeatability across environments, suggesting that MAS will be ineffective at improving Δ selection efficiency. Further, the relationship between grain yield and Δ ranged from neutral (r = -0.01) to moderately positive (r = 0.44) in the target environments. Such moderate correlations, coupled with variability in this relationship, indicate that direct selection for Δ may not be beneficial.

Genetic Dissection of Snow Mold Tolerance in US Pacific Northwest Winter Wheat Through Genome-Wide Association Study and Genomic Selection

Snow mold is a yield-limiting disease of wheat in the Pacific Northwest (PNW) region of the US, where there is prolonged snow cover. The objectives of this study were to identify genomic regions associated with snow mold tolerance in a diverse panel of PNW winter wheat lines in a genome-wide association study (GWAS) and to evaluate the usefulness of genomic selection (GS) for snow mold tolerance. An association mapping panel (AMP; N = 458 lines) was planted in Mansfield and Waterville, WA in 2017 and 2018 and genotyped using the Illumina^® 90K single nucleotide polymorphism (SNP) array. GWAS identified 100 significant markers across 17 chromosomes, where SNPs on chromosomes 5A and 5B coincided with major freezing tolerance and vernalization loci. Increased number of favorable alleles was related to improved snow mold tolerance. Independent predictions using the AMP as a training population (TP) to predict snow mold tolerance of breeding lines evaluated between 2015 and 2018 resulted in a mean accuracy of 0.36 across models and marker sets. Modeling nonadditive effects improved accuracy even in the absence of a close genetic relatedness between the TP and selection candidates. Selecting lines based on genomic estimated breeding values and tolerance scores resulted in a 24% increase in tolerance. The identified genomic regions associated with snow mold tolerance demonstrated the genetic complexity of this trait and the difficulty in selecting tolerant lines using markers. GS was validated and showed potential for use in PNW winter wheat for selecting on complex traits such tolerance to snow mold.