Genome-wide association mapping of the 'super-soft' kernel texture in white winter wheat

Quantitative proteomic analysis of super soft kernel texture in soft white spring wheat

Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel development. The cultivar 'Alpowa', a soft white spring wheat, was crossed to a closely related super soft spring wheat line 'BC2SS163' to produce F6 recombinant inbred lines (RILs). Four normal soft RILs and four super soft RILs along with the parents were selected for proteomic analysis. Alpowa and the normal soft RILs showed hardness indices of 20 to 30, whereas BC2SS163 and the super soft RILs showed hardness indices of -2 to -6. Kernels were collected from normal soft and super soft genotypes at 7 days post anthesis (dpa), 14 dpa, 28 dpa, and maturity and were subject to quantitative proteomic analysis. Throughout kernel development, 175 differentially abundant proteins (DAPs) were identified. Most DAPs were observed at 7 dpa, 14 dpa, and 28 dpa. Of the 175 DAPs, 32 had higher abundance in normal soft wheat, whereas 143 DAPs had higher abundance in super soft wheat. A total of 18 DAPs were associated with carbohydrate metabolism and five DAPs were associated with lipids. The gene TraesCS4B02G091100.1 on chromosome arm 4BS, which encodes for sucrose-phosphate synthase, was identified as a candidate gene for super soft kernel texture in BC2SS163. This study enhanced our understanding of the mechanism underlying super soft kernel texture in soft white spring wheat.

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert's method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.

Genome-Wide Association Analysis of Grain Hardness in Common Wheat

The grain hardness index (HI) is one of the important reference bases for wheat quality and commodity properties; therefore, it is essential and useful to identify loci associated with the HI in wheat breeding. The grain hardness index of the natural population including 150 common wheat genotypes was measured in this study. The phenotypic data diversity of HI based on four environments and the best linear unbiased prediction (BLUP) was analyzed. The results showed that the grain HI of the natural population ranged from 15.00 to 83.00, the variation range was from 5.10% to 24.44%, and the correlation coefficient was 0.872-0.980. BLUP value was used to grade and assign the grain HI to hard wheat, mixed wheat, and soft wheat, and the assigned phenotypes were used for genome-wide association analysis. Two types of grain hardness index phenotypic values were used for genome-wide association analysis (GWAS) using a 55K SNP array. A total of five significant association loci (p < 0.001) were excavated, among which four loci could be detected in three or more environments. They were distributed on chromosomes 1A and 7D, and the phenotypic contribution rate was 7.52% to 10.66%. A total of 48 sites related to grain hardness were detected by the assignment method, among which five were stable genetic sites, distributed on chromosomes 1A(2), 3B(1), 4B(1), and 7D(1), with phenotypic contribution rates ranging from 7.63% to 11.12%. Of the five loci detected by the assignment method, two stable loci were co-located in the phenotypic mapping results of the hardness index. One of the loci was consistent with previous reports and located on chromosome 1A, and one locus was unreported on chromosome 7D. Therefore, it may be a feasible attempt to use the assignment method to conduct genome-wide association analysis of the grain hardness index. In this study, a total of five genetic loci for grain hardness stability were excavated, and two of the loci were located in the two phenotypic values, two of which were not reported.

Genome-wide association mapping for wheat morphometric seed traits in Iranian landraces and cultivars under rain-fed and well-watered conditions

Seed traits in bread wheat are valuable to breeders and farmers, thus it is important exploring putative QTLs responsible for key traits to be used in breeding programs. GWAS was carried out using 298 bread wheat landraces and cultivars from Iran to uncover the genetic basis of seed characteristics in both rain-fed and well-watered environments. The analyses of linkage disequilibrium (LD) between marker pairs showed that the largest number of significant LDs in landraces (427,017) and cultivars (370,359) was recorded in genome B, and the strongest LD was identified on chromosome 4A (0.318). LD decay was higher in the B and A genomes, compared to the D genome. Mapping by using mrMLM (LOD > 3) and MLM (0.05/m, Bonferroni) led to 246 and 67 marker-trait associations (MTAs) under rain-fed, as well as 257 and 74 MTAs under well-watered conditions, respectively. The study found that 3VmrMLM correctly detected all types of loci and estimated their effects in an unbiased manner, with high power and accuracy and a low false positive rate, which led to the identification of 140 MTAs (LOD > 3) in all environments. Gene ontology revealed that 10 and 10 MTAs were found in protein-coding regions for rain-fed and well-watered conditions, respectively. The findings suggest that landraces studied in Iranian bread wheat germplasm possess valuable alleles, which are responsive to water-limited conditions. MTAs uncovered in this study can be exploited in the genome-mediated development of novel wheat cultivars.

Genome-wide association study of grain hardness and novel Puroindoline alleles in common wheat

Grain hardness (HI) is a key trait for wheat milling and end-use quality. Puroindoline genes (PINs) are the major genes responsible for grain hardness, but other QTLs also contribute to the trait. Therefore, it is essential to identify loci associated with the HI and allelic variations of PINs in wheat. In the present study, 287 accessions from Shanxi province representing 70 years of wheat breeding were grown in one rainfed and two irrigated conditions to study grain hardness. Genome-wide association analysis (GWAS) was performed using the 15 K array, and the variability of PIN alleles was investigated. Among the accessions, hard wheat was most common. The broad-sense heritability (H2) among the three environments was 99.5%, suggesting HI was mainly affected by heredity. GWAS identified nine significant marker-trait associations (MTAs), including that PINs, which explained 7.03% to 17.70% of phenotypic variation. Four MTAs on chromosome 2A, 2B, 5A, and 7A were novel loci. As for diversity of PINs, a total of 11 PINs haplotypes were detected, composed of 12 allelic variations of the PIN gene. The most frequent haplotypes were Pina-D1a/Pinb-D1b (43.9%) and Pina-Dla/Pinb-D1p (18.8%), and both the frequency of Pina-D1a/Pinb-D1b and the HI value increased with breeding years were related to local dietary habits probably. A novel double deletion allele of the PINs haplotype was found in Donghei1206. These results will be useful not only in understanding of the genetics of the HI but also in breeding for improved grain texture.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01303-x.

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.

Novel stripe rust all-stage resistance loci identified in a worldwide collection of durum wheat using genome-wide association mapping

Durumwheat [Triticum turgidum L. ssp. durum (Desf.)] production is constrained by fungal diseases including stripe rust caused by Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst). Continuous mining of germplasm for the discovery and deployment of stripe rust resistance (Yr) genes is needed to counter the impact of this disease. In this study, we evaluated a worldwide collection of 432 durum wheat accessions to seven U.S. Pst races that carry diverse virulence and avirulence combinations on wheat Yr genes. We found that 47-82% of the durum wheat accessions were susceptible to each of the tested Pst races. A total of 32 accessions were resistant to all seven races. Genome-wide association studies (GWAS) using over 97,000 single-nucleotide polymorphism markers generated from genotyping-by-sequencing of 364 accessions identified 56 quantitative trait loci (QTL) associated with all-stage stripe rust resistance located on all 14 durum wheat chromosomes. Six of these QTL were associated with resistance to 2-4 Pst races, and none were associated with resistance to all seven races. The remaining 50 QTL were race specific. Eighteen of the 56 identified QTL had relatively large effects against at least one of the races. A map-based comparison of the discovered QTL in this study with previously published Yr genes and QTL showed that 29 were previously identified, whereas the remaining 27 QTL appeared to be novel. This study reports effective sources of stripe rust resistance to contemporary races in the United States and shows that this durum wheat collection is abundant in novel resistance loci that can be transferred into adapted durum cultivars.

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.