Genetic analysis of soft white wheat end-use quality traits in a club by common wheat cross

Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration

Grain quality traits are the key factors that determine the economic value of wheat and are largely influenced by genetics and the environment. In this study, using a meta-analysis of quantitative trait loci (QTLs) and a comprehensive in silico transcriptome assessment, we identified key genomic regions and putative candidate genes for the grain quality traits protein content, gluten content, and test weight. A total of 508 original QTLs were collected from 41 articles on QTL mapping for the three quality traits in wheat published from 2003 to 2021. When these original QTLs were projected onto a high-density consensus map consisting of 14,548 markers, 313 QTLs resulted in the identification of 64 MQTLs distributed across 17 of the 21 chromosomes. Most of the meta-QTLs (MQTLs) were distributed on sub-genomes A and B. Compared with the original QTLs, the confidence interval (CI) of the MQTLs was smaller, with an average CI of 4.47 cM, while the projected QTLs CI was 11.13 cM (2.49-fold lower). The corresponding physical length of the MQTL ranged from 0.45 to 239.01 Mb. Thirty-one of these 64 MQTLs were validated in at least one genome-wide association study. In addition, five of the 64 MQTLs were selected and designated as core MQTLs. The 211 quality-related genes from rice were used to identify wheat homologs in MQTLs. In combination with transcriptional and omics analyses, 135 putative candidate genes were identified from 64 MQTL regions. The findings should contribute to a better understanding of the molecular genetic mechanisms underlying grain quality and the improvement of these traits in 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.

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.

Genome-wide association study of six quality-related traits in common wheat (Triticum aestivum L.) under two sowing conditions

We identified genomic regions associated with six quality-related traits in wheat under two sowing conditions and analyzed the effects of multienvironment-significant SNPs on the stability of these traits. Grain quality affects the nutritional and commercial value of wheat (Triticum aestivum L.) and is a critical factor influencing consumer preferences for specific wheat varieties. Climate change is predicted to increase environmental stress and thereby reduce wheat quality. Here, we performed a genotyping assay involving the use of the wheat 90 K array in a genome-wide association study of six quality-related traits in 486 wheat accessions under two sowing conditions (normal and late sowing) over 4 years. We identified 64 stable quantitative trait loci (QTL), including 10 for grain protein content, 9 for wet gluten content, 4 for grain starch content, 14 for water absorption, 15 for dough stability time and 12 for grain hardness in wheat under two sowing conditions. These QTL harbored 175 single nucleotide polymorphisms (SNPs), explaining approximately 3-13% of the phenotypic variation in multiple environments. Some QTL on chromosomes 6A and 5D were associated with multiple traits simultaneously, and two (QNGPC.cau-6A, QNGH.cau-5D) harbored known genes, such as NAM-A1 for grain protein content and Pinb for grain hardness, whereas other QTL could facilitate gene discovery. Forty-three SNPs that were detected under late or both normal and late sowing conditions appear to be related to phenotypic stability. The effects of these SNP alleles were confirmed in the association population. The results of this study will be useful for further dissecting the genetic basis of quality-related traits in wheat and developing new wheat cultivars with desirable alleles to improve the stability of grain quality.

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.

Association Mapping for 24 Traits Related to Protein Content, Gluten Strength, Color, Cooking, and Milling Quality Using Balanced and Unbalanced Data in Durum Wheat [Triticum turgidum L. var. durum (Desf).]

Durum wheat [Triticum durum (Desf).] is mostly used to produce pasta, couscous, and bulgur. The quality of the grain and end-use products determine its market value. However, quality tests are highly resource intensive and almost impossible to conduct in the early generations in the breeding program. Modern genomics-based tools provide an excellent opportunity to genetically dissect complex quality traits to expedite cultivar development using molecular breeding approaches. This study used a panel of 243 cultivars and advanced breeding lines developed during the last 20 years to identify SNPs associated with 24 traits related to nutritional value and quality. Genome-wide association study (GWAS) identified a total of 179 marker-trait associations (MTAs), located in 95 genomic regions belonging to all 14 durum wheat chromosomes. Major and stable QTLs were identified for gluten strength on chromosomes 1A and 1B, and for PPO activity on chromosomes 1A, 2B, 3A, and 3B. As a large amount of unbalance phenotypic data are generated every year on advanced lines in all the breeding programs, the applicability of such a dataset for identification of MTAs remains unclear. We observed that ∼84% of the MTAs identified using a historic unbalanced dataset (belonging to a total of 80 environments collected over a period of 16 years) were also identified in a balanced dataset. This suggests the suitability of historic unbalanced phenotypic data to identify beneficial MTAs to facilitate local-knowledge-based breeding. In addition to providing extensive knowledge about the genetics of quality traits, association mapping identified several candidate markers to assist durum wheat quality improvement through molecular breeding. The molecular markers associated with important traits could be extremely useful in the development of improved quality durum wheat cultivars using marker-assisted selection (MAS).

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

Soft white wheat is used in domestic and foreign markets for various end products requiring specific quality profiles. Phenotyping for end-use quality traits can be costly, time-consuming and destructive in nature, so it is advantageous to use molecular markers to select experimental lines with superior traits. An association mapping panel of 469 soft white winter wheat cultivars and advanced generation breeding lines was developed from regional breeding programs in the U.S. Pacific Northwest. This panel was genotyped on a wheat-specific 90 K iSelect single nucleotide polymorphism (SNP) chip. A total of 15,229 high quality SNPs were selected and combined with best linear unbiased predictions (BLUPs) from historical phenotypic data of the genotypes in the panel. Genome-wide association mapping was conducted using the Fixed and random model Circulating Probability Unification (FarmCPU). A total of 105 significant marker-trait associations were detected across 19 chromosomes. Potentially new loci for total flour yield, lactic acid solvent retention capacity, flour sodium dodecyl sulfate sedimentation and flour swelling volume were also detected. Better understanding of the genetic factors impacting end-use quality enable breeders to more effectively discard poor quality germplasm and increase frequencies of favorable end-use quality alleles in their breeding populations.