Interactions between Glu-1 and Glu-3 loci and associations of selected molecular markers with quality traits in winter wheat (Triticum aestivum L.) DH lines

QTL detection for bread wheat processing quality in a nested association mapping population of semi-wild and domesticated wheat varieties

BACKGROUND

Wheat processing quality is an important factor in evaluating overall wheat quality, and dough characteristics are important when assessing the processing quality of wheat. As a notable germplasm resource, semi-wild wheat has a key role in the study of wheat processing quality.

RESULTS

In this study, four dough rheological characteristics were collected in four environments using a nested association mapping (NAM) population consisting of semi-wild and domesticated wheat varieties to identify quantitative trait loci (QTL) for wheat processing quality. A total of 49 QTL for wheat processing quality were detected, explaining 0.36-10.82% of the phenotypic variation. These QTL were located on all wheat chromosomes except for 2D, 3A, 3D, 6B, 6D and 7D. Compared to previous studies, 29 QTL were newly identified. Four novel QTL, QMlPH-1B.4, QMlPH-3B.4, QWdEm-1B.2 and QWdEm-3B.2, were stably identified in three or more environments, among which QMlPH-3B.4 was a major QTL. Moreover, eight important genetic regions for wheat processing quality were identified on chromosomes 1B, 3B and 4D, which showed pleiotropy for dough characteristics. In addition, out of 49 QTL, 15 favorable alleles came from three semi-wild parents, suggesting that the QTL alleles provided by the semi-wild parent were not utilized in domesticated varieties.

CONCLUSIONS

The results show that semi-wild wheat varieties can enrich the existing wheat gene pool and provide broader variation resources for wheat genetic research.

Allelic Variation at Glutenin Loci (Glu-1, Glu-2 and Glu-3) in a Worldwide Durum Wheat Collection and Its Effect on Quality Attributes

Durum wheat grains (Triticum turgidum L. ssp. durum) are the main source for the production of pasta, bread and a variety of products consumed worldwide. The quality of pasta is mainly defined by the rheological properties of gluten, an elastic network in wheat endosperms formed of gliadins and glutenins. In this study, the allelic variation at five glutenin loci was analysed in 196 durum wheat genotypes. Two loci (Glu-A1 and Glu-B1), encoding for high-molecular-weight glutenin subunits (HMW-GS), and three loci (Glu-B2, Glu-A3 and Glu-B3), encoding for low molecular weight glutenin subunits (LMW-GS), were assessed by SDS-PAGE. The SDS-sedimentation test was used and the grain protein content was evaluated. A total of 32 glutenin subunits and 41 glutenin haplotypes were identified. Four novel alleles were detected. Fifteen haplotypes represented 85.7% of glutenin loci variability. Some haplotypes carrying the 7 + 15 and 7 + 22 banding patterns at Glu-B1 showed a high gluten strength similar to those that carried the 7 + 8 or 6 + 8 alleles. A decreasing trend in grain protein content was observed over the last 85 years. Allelic frequencies at the three main loci (Glu-B1, Glu-A3 and Glu-B3) changed over the 1915-2020 period. Gluten strength increased from 1970 to 2020 coinciding with the allelic changes observed. These results offer valuable information for glutenin haplotype-based selection for use in breeding programs.

Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat

BACKGROUND

Glutenin contents and compositions are crucial factors influencing the end-use quality of wheat. Although the composition of glutenin fractions is well known, there has been relatively little research on the genetic basis of glutenin fractions in wheat.

RESULTS

To elucidate the genetic basis for the contents of glutenin and its fractions, a population comprising 196 recombinant inbred lines (RILs) was constructed from two parents, Luozhen No.1 and Zhengyumai 9987, which differ regarding their total glutenin and its fraction contents (except for the By fraction). Forty-one additive Quantitative Trait Loci (QTL) were detected in four environments over two years. These QTL explained 1.3% - 53.4% of the phenotypic variation in the examined traits. Forty-three pairs of epistatic QTL (E-QTL) were detected in the RIL population across four environments. The QTL controlling the content of total glutenin and its seven fractions were detected in clusters. Seven clusters enriched with QTL for more than three traits were identified, including a QTL cluster 6AS-3, which was revealed as a novel genetic locus for glutenin and related traits. Kompetitive Allele-Specific PCR (KASP) markers developed from the main QTL cluster 1DL-2 and the previously developed KASP marker for the QTL cluster 6AS-3 were validated as significantly associated with the target traits in the RIL population and in natural varieties.

CONCLUSIONS

This study identified novel genetic loci related to glutenin and its seven fractions. Additionally, the developed KASP markers may be useful for the marker-assisted selection of varieties with high glutenin fraction content and for identifying individuals in the early developmental stages without the need for phenotyping mature plants. On the basis of the results of this study and the KASP markers described herein, breeders will be able to efficiently select wheat lines with favorable glutenin properties and develop elite lines with high glutenin subunit contents.

Improving the baking quality of bread wheat by genomic selection in early generations

Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines

The aim of the this study was to identify SNP markers associated with five important wheat quality traits (grain protein content, Zeleny sedimentation, test weight, thousand-kernel weight, and falling number), and to investigate the predictive abilities of GBLUP and Bayesian Power Lasso models for genomic prediction of these traits. In total, 635 winter wheat lines from two breeding cycles in the Danish plant breeding company Nordic Seed A/S were phenotyped for the quality traits and genotyped for 10,802 SNPs. GWAS were performed using single marker regression and Bayesian Power Lasso models. SNPs with large effects on Zeleny sedimentation were found on chromosome 1B, 1D, and 5D. However, GWAS failed to identify single SNPs with significant effects on the other traits, indicating that these traits were controlled by many QTL with small effects. The predictive abilities of the models for genomic prediction were studied using different cross-validation strategies. Leave-One-Out cross-validations resulted in correlations between observed phenotypes corrected for fixed effects and genomic estimated breeding values of 0.50 for grain protein content, 0.66 for thousand-kernel weight, 0.70 for falling number, 0.71 for test weight, and 0.79 for Zeleny sedimentation. Alternative cross-validations showed that the genetic relationship between lines in training and validation sets had a bigger impact on predictive abilities than the number of lines included in the training set. Using Bayesian Power Lasso instead of GBLUP models, gave similar or slightly higher predictive abilities. Genomic prediction based on all SNPs was more effective than prediction based on few associated SNPs.

The influence of Glu-1 and Glu-3 loci on dough rheology and bread-making properties in wheat (Triticum aestivum L.) doubled haploid lines

BACKGROUND

The major determinants of wheat quality are Glu-1 and Glu-3 glutenin loci and environmental factors. Additive effects of alleles at the Glu-1 and Glu-3 loci, as well as their interactions, were evaluated for dough rheology and baking properties in four groups of wheat doubled haploid lines differing in high- and low-molecular-weight glutenin composition.

RESULTS

Flour quality, Reomixer (Reologica Instruments, Lund, Sweden), dough extension, Farinograph (Brabender GmbH, Duisburg, Germany) and baking parameters were determined. Groups of lines with the alleles Glu-A3b and Glu-B3d were characterized by higher values of dough and baking parameters compared to those with the Glu-A3e and Glu-B3a alleles. Effects of interactions between allelic variants at the Glu-1 and Glu-3 loci on Reomixer parameters, dough extension tests and baking parameters were significant, although additive effects of individual alleles were not always significant.

CONCLUSION

The allelic variants at Glu-B3 had a much greater effect on dough rheological parameters than the variants at Glu-A3 or Glu-D3 loci. The effect of allelic variations at the Glu-D3 loci on rheological parameters and bread-making quality was non-significant, whereas their interactions with a majority of alleles at the other Glu-1 × Glu-3 loci were significant. © 2017 Society of Chemical Industry.

Exotic QTL improve grain quality in the tri-parental wheat population SW84

Developing the tri-parental exotic wheat population SW84

Genetic diversity of cultivated wheat was markedly reduced, first, during domestication and, second, since the onset of modern elite breeding. There is an increasing demand for utilizing genetic resources to increase genetic diversity and, simultaneously, to improve agronomic performance of cultivated wheat. To locate favorable effects of exotic wheat alleles, we developed the tri-parental wheat population SW84. The population was derived from crossing the hexaploid spring wheat cultivars Triso and Devon with one synthetic exotic donor accession, Syn084L, followed by two rounds of backcrossing and three rounds of selfing. SW84 consists of 359 BC₂F₄ lines, split into two families, D84 (Devon*Syn084L) and T84 (Triso*Syn084L).

Studying the genetic control of grain quality in SW84

As a case study, grain quality of SW84 was studied in replicated field trials. Transgressive segregation was observed for all studied grain quality traits by evaluating SW84 for two years at two locations under low and high nitrogen supply. Subsequently, a genome-wide association study (GWAS) was carried out based on genomic data derived from a 90k Infinium iSELECT single nucleotide polymorphism (SNP) array. In total, GWAS yielded 37 marker-trait associations, summarized to 16 quantitative trait loci (QTL). These SNPs indicate genetic regulators of grain protein content, grain hardness, sedimentation value and sedimentation ratio. The majority of exotic QTL alleles (75%) exerted favorable effects, increasing grain protein content and sedimentation value in ten and two cases, respectively. For instance, two exotic QTL alleles were associated with a substantial increase of grain protein content and sedimentation value by 1.09% and 7.31 ml, respectively. This finding confirms the potential of exotic germplasm to improve grain quality in cultivated wheat. So far, the molecular nature of most of the detected QTL is unknown. However, two QTL correspond to known genes controlling grain quality: The major QTL on chromosome 6B, increasing grain protein content by 0.70%, on average, co-localizes with the NAM-B1 gene, known to control grain protein content as well as iron and zinc content. Likewise, the major QTL on chromosome 5D, reducing grain hardness by 8.98%, on average, co-localizes with the gene for puroindoline b (Pinb-D1) at the Ha locus. In total, 13 QTL were detected across families, whereas one and three QTL were exclusively detected in families D84 and T84, respectively. Likewise, ten QTL were detected across nitrogen treatments, whereas one and five QTL were exclusively detected under low and high N treatments, respectively. Our data indicate that most effects in SW84 act across families and N levels. Merging of data from two families or two N treatments may, thus, be considered in association studies to increase sample size and, as a result, QTL detection power.

Utilizing favorable exotic QTL alleles in wheat breeding

Our study serves as a model how favorable exotic QTL alleles can be located in exotic germplasm of wheat. In future, the localized favorable exotic QTL alleles will be utilized in wheat breeding programs to simultaneously improve grain quality and selectively expand genetic diversity of the elite wheat gene pool.