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

Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities

Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.

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.

Developing core marker sets for effective genomic-assisted selection in wheat and barley breeding programs

Wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) are widely cultivated temperate crops. In breeding programs with these crops in Japan, effective genomic-assisted selection was performed by selecting core marker sets from thousands of genome-wide amplicon sequencing markers. The core sets consist of 768 and 960 markers for barley and wheat, respectively. These markers are distributed evenly across the genomes and effectively detect widely distributed polymorphisms in the chromosomes. The core set utility was assessed using 1,032 barley and 1,798 wheat accessions across the country. Minor allele frequency and chromosomal distributions showed that the core sets could effectively capture polymorphisms across the entire genome, indicating that the core sets are applicable to highly-related advanced breeding materials. Using the core sets, we also assessed the trait value predictability. As observed via fivefold cross-validation, the prediction accuracies of six barley traits ranged from 0.56-0.74 and 0.62 on average, and the corresponding values for eight wheat traits ranged from 0.44-0.83 and 0.65 on average. These data indicate that the established core marker sets enable breeding processes to be accelerated in a cost-effective manner and provide a strong foundation for further research on genomic selection in crops.

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.