Quantitative trait loci influencing endosperm texture, dough-mixing strength, and bread-making properties of the hard red spring wheat breeding lines

CRISPR/Cas9-Mediated Disruption of Xylanase inhibitor protein (XIP) Gene Improved the Dough Quality of Common Wheat

The wheat dough quality is of great significance for the end-use of flour. Some genes have been cloned for controlling the protein fractions, grain protein content, starch synthase, grain hardness, etc. Using a unigene map of the recombinant inbred lines (RILs) for "TN 18 × LM 6," we mapped a quantitative trait locus (QTL) for dough stability time (ST) and SDS-sedimentation values (SV) on chromosome 6A (QSt/Sv-6A-2851). The peak position of the QTL covered two candidate unigenes, and we speculated that TraesCS6A02G077000 (a xylanase inhibitor protein) was the primary candidate gene (named the TaXip gene). The target loci containing the three homologous genes TaXip-6A, TaXip-6B, and TaXip-6D were edited in the variety "Fielder" by clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR/Cas9). Two mutant types in the T_2:3 generation were obtained (aaBBDD and AAbbdd) with about 120 plants per type. The SVs of aaBBDD, AAbbdd, and WT were 31.77, 27.30, and 20.08 ml, respectively. The SVs of the aaBBDD and AAbbdd were all significantly higher than those of the wild type (WT), and the aaBBDD was significantly higher than the AAbbdd. The STs of aaBBDD, AAbbdd, and WT were 2.60, 2.24, and 2.25 min, respectively. The ST for the aaBBDD was significantly higher than that for WT and was not significantly different between WT and AAbbdd. The above results indicated that XIP in vivo can significantly affect wheat dough quality. The selection of TaXip gene should be a new strategy for developing high-quality varieties in wheat breeding programs.

Genetic Dissection of the Mixing Properties of Wheat Flour (Triticum aestivum L.) Using Unconditional and Conditional QTL Mapping

Wheat (Triticum aestivum L.) flour mixing properties are essential quality parameters in the dough development process. Limited research on superior alleles for mixing properties has restricted their molecular improvement, and other factors related to the complex traits have been ignored. A molecular map of 9576 polymorphic markers in the RIL population (F_8:9) (Shannong01-35/Gaocheng9411) was constructed to evaluate mixing property effects in three environments. The parents were selected with markedly distinct high-molecular-weight glutenin subunits (HMW-GS). This study not only evaluated mixing properties using conventional unconditional QTL mapping but also evaluated the relationships between protein-related traits using conditional QTL mapping. The analyses identified most additive QTLs for major mixing properties on chromosomes 1A, 1B, and 1D. Two major loci (1A.1-15 and 1D-1) associated with mixing properties have confirmed the important contributions of Glu-A1 and Glu-D1 to wheat quality at the QTL level, which were mainly affected by the gluten index. Another important locus, 1B.1-24 (associated with midline peak value and midline peak width, with high phenotypic variations explained), might represent a new variation distinct from Glu-B1. The favored alleles came from Gaocheng9411. Several mixing properties shared the same QTLs (1B.1-6 and 1A.1-15), indicating tight linkage or pleiotropism. Genotype-by-environment (G×E) interactions were also investigated in the present study. The QTL results in our study may improve our understanding of the genetic interrelationships between mixing properties and protein-related traits.

Identification and Validation of Stable Quantitative Trait Loci for SDS-Sedimentation Volume in Common Wheat (Triticum aestivum L.)

Sodium dodecyl sulfate-sedimentation volume is an important index to evaluate the gluten strength of common wheat and is closely related to baking quality. In this study, a total of 15 quantitative trait locus (QTL) for sodium dodecyl sulfate (SDS)-sedimentation volume (SSV) were identified by using a high-density genetic map including 2,474 single-nucleotide polymorphism (SNP) markers, which was constructed with a doubled haploid (DH) population derived from the cross between Non-gda3753 (ND3753) and Liangxing99 (LX99). Importantly, four environmentally stable QTLs were detected on chromosomes 1A, 2D, and 5D, respectively. Among them, the one with the largest effect was identified on chromosome 1A (designated as QSsv.cau-1A.1) explaining up to 39.67% of the phenotypic variance. Subsequently, QSsv.cau-1A.1 was dissected into two QTLs named as QSsv.cau-1A.1.1 and QSsv.cau-1A.1.2 by saturating the genetic linkage map of the chromosome 1A. Interestedly, favorable alleles of these two loci were from different parents. Due to the favorable allele of QSsv.cau-1A.1.1 was from the high-value parents ND3753 and revealed higher genetic effect, which explained 25.07% of the phenotypic variation, mapping of this locus was conducted by using BC3F1 and BC3F2 populations. By comparing the CS reference sequence, the physical interval of QSsv.cau-1A.1.1 was delimited into 14.9 Mb, with 89 putative high-confidence annotated genes. SSVs of different recombinants between QSsv.cau-1A.1.1 and QSsv.cau-1A.1 detected from DH and BC3F2 populations showed that these two loci had an obvious additive effect, of which the combination of two favorable loci had the high SSV, whereas recombinants with unfavorable loci had the lowest. These results provide further insight into the genetic basis of SSV and QSsv.cau-1A.1.1 will be an ideal target for positional cloning and wheat breeding programs.

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.

QTL mapping for quality traits using a high-density genetic map of wheat

Protein- and starch-related quality traits, which are quantitatively inherited and significantly influenced by the environment, are critical determinants of the end-use quality of wheat. We constructed a high-density genetic map containing 10,739 loci (5,399 unique loci) using a set of 184 recombinant inbred lines (RILs) derived from a cross of 'Tainong 18 × Linmai 6' (TL-RILs). In this study, a quantitative trait loci (QTLs) analysis was used to examine the genetic control of grain protein content, sedimentation value, farinograph parameters, falling number and the performance of the starch pasting properties using TL-RILs grown in a field for three years. A total of 106 QTLs for 13 quality traits were detected, distributed on the 21 chromosomes. Of these, 38 and 68 QTLs for protein- and starch-related traits, respectively, were detected in three environments and their average values (AV). Twenty-six relatively high-frequency QTLs (RHF-QTLs) that were detected in more than two environments. Twelve stable QTL clusters containing at least one RHF-QTL were detected and classified into three types: detected only for protein-related traits (type I), detected only for starch-related traits (type II), and detected for both protein- and starch-related traits (type III). A total of 339 markers flanked with 11 QTL clusters (all except C6), were found to be highly homologous with 282 high confidence (HC) and 57 low confidence (LC) candidate genes based on IWGSC RefSeq v 1.0. These stable QTLs and RHF-QTLs, especially those grouped into clusters, are credible and should be given priority for QTL fine-mapping and identification of candidate genes with which to explain the molecular mechanisms of quality development and inform marker-assisted breeding in the future.

Deciphering the Genetics of Major End-Use Quality Traits in Wheat

Improving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.

Genetic and transcriptional variations in NRAMP-2 and OPAQUE1 genes are associated with salt stress response in wheat

SNP alleles on chromosomes 4BL and 6AL are associated with sensitivity to salt tolerance in wheat and upon validation can be exploited in the development of salt-tolerant wheat varieties. The dissection of the genetic and molecular components of salt stress response offers strong opportunities toward understanding and improving salt tolerance in crops. In this study, GWAS was employed to identify a total of 106 SNP loci (R² = 0.12-63.44%) linked to salt stress response in wheat using leaf chlorophyll fluorescence, grain quality and shoot ionic (Na⁺ and K⁺ ions) attributes. Among them, 14 SNP loci individually conferred pleiotropic effects on multiple independent salinity tolerance traits including loci at 99.04 cM (R² ≥ 14.7%) and 68.45 cM (R² ≥ 4.10%) on chromosomes 6AL and 4BL, respectively, that influenced shoot Na⁺-uptake, shoot K⁺/Na⁺ ratio, and specific energy fluxes for absorption (ABS/RC) and dissipation (DIo/RC). Analysis of the open reading frame (ORF) containing the SNP markers revealed that they are orthologous to genes involved in photosynthesis and plant stress (salt) response. Further transcript abundance and qRT-PCR analyses indicated that the genes are mostly up-regulated in salt-tolerant and down-regulated in salt-sensitive wheat genotypes including NRAMP-2 and OPAQUE1 genes on 4BL and 6AL, respectively. Both genes showed highest differential expression between contrasting genotypes when expressions of all the genes within their genetic intervals were analyzed. Possible cis-acting regulatory elements and coding sequence variation that may be involved in salt stress response were also identified in both genes. This study identified genetic and molecular components of salt stress response that are associated with Na⁺-uptake, shoot Na⁺/K⁺ ratio, ABS/RC, DIo/RC, and grain quality traits and upon functional validation would facilitate the development of gene-specific markers that could be deployed to improve salinity tolerance in wheat.

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.

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.

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

The quality of wheat depends on a large complex of genes and environmental factors. The objective of this study was to identify quantitative trait loci controlling technological quality traits and their stability across environments, and to assess the impact of interaction between alleles at loci Glu-1 and Glu-3 on grain quality. DH lines were evaluated in field experiments over a period of 4 years, and genotyped using simple sequence repeat markers. Lines were analysed for grain yield (GY), thousand grain weight (TGW), protein content (PC), starch content (SC), wet gluten content (WG), Zeleny sedimentation value (ZS), alveograph parameter W (APW), hectolitre weight (HW), and grain hardness (GH). A number of QTLs for these traits were identified in all chromosome groups. The Glu-D1 locus influenced TGW, PC, SC, WG, ZS, APW, GH, while locus Glu-B1 affected only PC, ZS, and WG. Most important marker-trait associations were found on chromosomes 1D and 5D. Significant effects of interaction between Glu-1 and Glu-3 loci on technological properties were recorded, and in all types of this interaction positive effects of Glu-D1 locus on grain quality were observed, whereas effects of Glu-B1 locus depended on alleles at Glu-3 loci. Effects of Glu-A3 and Glu-D3 loci per se were not significant, while their interaction with alleles present at other loci encoding HMW and LMW were important. These results indicate that selection of wheat genotypes with predicted good bread-making properties should be based on the allelic composition both in Glu-1 and Glu-3 loci, and confirm the predominant effect of Glu-D1d allele on technological properties of wheat grains.

Genome-Wide QTL Mapping for Wheat Processing Quality Parameters in a Gaocheng 8901/Zhoumai 16 Recombinant Inbred Line Population

Dough rheological and starch pasting properties play an important role in determining processing quality in bread wheat (Triticum aestivum L.). In the present study, a recombinant inbred line (RIL) population derived from a Gaocheng 8901/Zhoumai 16 cross grown in three environments was used to identify quantitative trait loci (QTLs) for dough rheological and starch pasting properties evaluated by Mixograph, Rapid Visco-Analyzer (RVA), and Mixolab parameters using the wheat 90 and 660 K single nucleotide polymorphism (SNP) chip assays. A high-density linkage map constructed with 46,961 polymorphic SNP markers from the wheat 90 and 660 K SNP assays spanned a total length of 4121 cM, with an average chromosome length of 196.2 cM and marker density of 0.09 cM/marker; 6596 new SNP markers were anchored to the bread wheat linkage map, with 1046 and 5550 markers from the 90 and 660 K SNP assays, respectively. Composite interval mapping identified 119 additive QTLs on 20 chromosomes except 4D; among them, 15 accounted for more than 10% of the phenotypic variation across two or three environments. Twelve QTLs for Mixograph parameters, 17 for RVA parameters and 55 for Mixolab parameters were new. Eleven QTL clusters were identified. The closely linked SNP markers can be used in marker-assisted wheat breeding in combination with the Kompetitive Allele Specific PCR (KASP) technique for improvement of processing quality in bread wheat.

Puroindoline allelic diversity in Indian wheat germplasm and identification of new allelic variants

Grain hardness is an important quality trait that influences product development in wheat. This trait is governed by variation in puroindoline proteins (PINA and PINB). Our study evaluated 551 Indian wheat germplasm lines for diversity in Pina and Pinb genes. Eighty-two lines were shortlisted for full length sequencing and grain hardness studies. Sequencing studies identified six unknown alleles: two for the Pina gene and four for the Pinb gene. Five of them were novel with non-synonymous changes in the corresponding amino acid sequences. Identified mutations in the deduced mature proteins and their pre- and pro-peptides influenced the hardness characteristics of the grain. We classified these 82 varieties into different hardness categories with reference to international and Indian systems of classification. The majority of Indian wheat varieties were categorized as hard. This study revealed that unexplored Indian wheat germplasm can be a good source of genetic variability for both Pina and Pinb genes, helping in marker-assisted breeding and in obtaining wheat with different textural properties.

New QTL alleles for quality-related traits in spring wheat revealed by RIL population derived from supernumerary × non-supernumerary spikelet genotypes

A population developed from an exotic line with supernumerary spikelets was genetically dissected for eight quality traits, discovering new genes/alleles with potential use in wheat breeding programs. Identifying new QTLs and alleles in exotic germplasm is paramount for further improvement of quality traits in wheat. In the present study, an RIL population developed from a cross of an elite wheat line (WCB414) and an exotic genotype with supernumerary spikelets (SS) was used to identify QTLs and new alleles for eight quality traits. Composite interval mapping for 1,000 kernels weight (TKW), kernel volume weight (KVW), grain protein content (GPC), percent of flour extraction (FE) and four mixograph-related traits identified a total of 69 QTLs including 19 stable QTLs. These QTLs were located on 18 different chromosomes (except 4D, 5D, and 6D). Thirteen of these QTLs explained more than 15% of phenotypic variation (PV) and were considered as major QTLs. In this study, we identified 11 QTLs for TKW (R (2) = 7.2-17.1 %), 10 for KVW (R (2) = 6.7-22.5%), 11 for GPC (R (2) = 4.7-16.9%), 6 for FE (R (2) = 4.8-19%) and 31 for mixograph-related traits (R (2) = 3.2-41.2%). In this population, several previously identified QTLs for SS, nine spike-related and ten agronomic traits were co-located with the quality QTLs, suggesting pleiotropic effects or close linkage among loci. The traits GPC and mixogram-related traits were positively correlated with SS. Indeed, several loci for quality traits were co-located with QTL for SS. The exotic parent contributed positive alleles that increased PV of the traits at 56% of loci demonstrating the suitability of germplasm with SS to improve quality traits in wheat.

Dissecting genetic architecture underlying seed traits in multiple environments

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits.

Genetic dissection reveals effects of interaction between high-molecular-weight glutenin subunits and waxy alleles on dough-mixing properties in common wheat

The glutenin and waxy loci of wheat are important determinants of dough quality. This study was conducted to evaluate the effects of high-molecular-weight glutenin (HMW-GS) and waxy alleles on dough-mixing properties. Molecular mapping was used to investigate these effects on Mixograph properties in a population of 290 (Nuomai1 x Gaocheng8901) recombinant inbred lines (RILs) from three environments in the harvest years 2008, 2009 and 2011. The results indicated the following: (i) the Glu-A1 and Glu-D1 loci have greater impacts on Mixograph properties compared to the Wx-1 loci and the effects of Glu-D1d and Glu-D1h on dough mixing are better than those of Glu-D1f and Glu-D1new1 in this population; (ii) the interactions between the Glu-1 and Wx-1 loci affected some traits, especially the midline peak value (MPV), and the lack of Wx-B1 or Wx-D1 led to increased MPV for all types of Glu-1 loci; and (iii) 30 quantitative-trait loci (QTL) over nine wheat chromosomes were identified with ICIM analysis based on the genetic map of 498 loci. Eight major QTL and 16 QTL in the Glu-1 loci from the three environments were found. The major QTL clusters were associated with the Glu-1 loci, and also were found in two regions on chromosome 3B and one region on chromosome 6A, which is one of the novel chromosome regions influencing dough-mixing strength. The two QTL for MPV are located around Wx-B1 on chromosome 4A. QMPT-1D.1, QMPI-1D.1 and Q8MW-1D.1 were stable in different environments and could potentially be used in molecular marker-assisted breeding.

The relationship between grain hardness, dough mixing parameters and bread-making quality in winter wheat

The influence of grain hardness, determined by using molecular markers and physical methods (near-infrared (NIR) technique and particle size index—PSI) on dough characteristics, which in turn were determined with the use of a farinograph and reomixer, as well as bread-making properties were studied. The material covered 24 winter wheat genotypes differing in grain hardness. The field experiment was conducted at standard and increased levels of nitrogen fertilization. Results of molecular analyses were in agreement with those obtained by the use of physical methods for soft-grained lines. Some lines classified as hard (by physical methods) appeared to have the wild-type Pina and Pinb alleles, similar to soft lines. Differences in dough and bread-making properties between lines classified as hard and soft on the basis of molecular data appeared to be of less significance than the differences between lines classified as hard and soft on the basis of physical analyses of grain texture. Values of relative grain hardness at the increased nitrogen fertilization level were significantly higher. At both fertilization levels the NIR parameter determining grain hardness was significantly positively correlated with the wet gluten and sedimentation values, with most of the rheological parameters and bread yield. Values of this parameter correlated with quality characteristics in a higher degree than values of particle size index.