Association analysis of Puroindoline-D1 and Puroindoline b-2 loci with 13 quality traits in European winter wheat (Triticum aestivum L.)

Assessing Falling Number Stability Increases the Genomic Prediction Ability of Pre-Harvest Sprouting Resistance in Common Winter Wheat

Pre-harvest sprouting (PHS) resistance is a complex trait, and many genes influencing the germination process of winter wheat have already been described. In the light of interannual climate variation, breeding for PHS resistance will remain mandatory for wheat breeders. Several tests and traits are used to assess PHS resistance, i.e., sprouting scores, germination index, and falling number (FN), but the variation of these traits is highly dependent on the weather conditions during field trials. Here, we present a method to assess falling number stability (FNS) employing an after-ripening period and the wetting of the kernels to improve trait variation and thus trait heritability. Different genome-based prediction scenarios within and across two subsequent seasons based on overall 400 breeding lines were applied to assess the predictive abilities of the different traits. Based on FNS, the genome-based prediction of the breeding values of wheat breeding material showed higher correlations across seasons (r=0.505-0.548) compared to those obtained for other traits for PHS assessment (r=0.216-0.501). By weighting PHS-associated quantitative trait loci (QTL) in the prediction model, the average predictive abilities for FNS increased from 0.585 to 0.648 within the season 2014/2015 and from 0.649 to 0.714 within the season 2015/2016. We found that markers in the Phs-A1 region on chromosome 4A had the highest effect on the predictive abilities for FNS, confirming the influence of this QTL in wheat breeding material, whereas the dwarfing genes Rht-B1 and Rht-D1 and the wheat-rye translocated chromosome T1RS.1BL exhibited effects, which are well-known, on FN per se exclusively.

Genetic control and prospects of predictive breeding for European winter wheat's Zeleny sedimentation values and Hagberg-Perten falling number

KEY MESSAGE

Sedimentation values and falling number in the last decades have helped maintain high baking quality despite rigorous selection for grain yield in wheat. Allelic combinations of major loci sustained the bread-making quality while improving grain yield. Glu-D1, Pinb-D1, and non-gluten proteins are associated with sedimentation values and falling number in European wheat. Zeleny sedimentation values (ZSV) and Hagberg-Perten falling number (HFN) are among the most important parameters that help determine the baking quality classes of wheat and, thus, influence the monetary benefits for growers. We used a published data set of 372 European wheat varieties evaluated in replicated field trials in multiple environments. ZSV and HFN traits hold a wide and significant genotypic variation and high broad-sense heritability. The genetic correlations revealed positive and significant associations of ZSV and HFN with each other, grain protein content (GPC) and grain hardness; however, they were all significantly negatively correlated with grain yield. Besides, GPC appeared to be the major predictor for ZSV and HFN. Our genome-wide association analyses based on high-quality SSR, SNP, and candidate gene markers revealed a strong quantitative genetic nature of ZSV and HFN by explaining their total genotypic variance as 41.49% and 38.06%, respectively. The association of known Glutenin (Glu-1) and Puroindoline (Pin-1) with ZSV provided positive analytic proof of our studies. We report novel candidate loci associated with globulins and albumins-the non-gluten monomeric proteins in wheat. In addition, predictive breeding analyses for ZSV and HFN suggest using genomic selection in the early stages of breeding programs with an average prediction accuracy of 81 and 59%, respectively.

Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.)

We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi‑wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

Hybrid wheat: quantitative genetic parameters and heterosis for quality and rheological traits as well as baking volume

Heterosis effects for dough quality and baking volume were close to zero. However, hybrids have a higher grain yield at a given level of bread making quality compared to their parental lines. Bread wheat cultivars have been selected according to numerous quality traits to fulfill the requirements of the bread making industry. These include beside protein content and quality also rheological traits and baking volume. We evaluated 35 male and 73 female lines and 119 of their single-cross hybrids at three different locations for grain yield, protein content, sedimentation value, extensograph traits and baking volume. No significant differences (p < 0.05) were found in the mean comparisons of males, females and hybrids, except for higher grain yield and lower protein content in the hybrids. Mid-parent and better-parent heterosis values were close to zero and slightly negative, respectively, for baking volume and extensograph traits. However, the majority of heterosis values resulted in the finding that hybrids had higher grain yield than lines for a given level of baking volume, sedimentation value or energy value of extensograph. Due to the high correlation with the mid-parent values (r > 0.70), an initial prediction of hybrid performance based on line per se performance for protein content, sedimentation value, most traits of the extensograph and baking volume is possible. The low variance due to specific combining ability effects for most quality traits points toward an additive gene action requires quality selection within both heterotic groups. Consequently, hybrid wheat can combine high grain yield with high bread making quality. However, the future use of wheat hybrids strongly depends on the establishment of a cost-efficient and reliable seed production system.

Characterization of 150 Wheat Cultivars by LC-MS-Based Label-Free Quantitative Proteomics Unravels Possibilities to Design Wheat Better for Baking Quality and Human Health

Wheat (Triticum aestivum ssp. aestivum) contributes to 20% of the human protein supply, delivers essential amino acids and is of fundamental importance for bread and pasta quality. Wheat proteins are also involved in adverse human reactions like celiac disease (CD), wheat allergy (WA) and non-celiac wheat sensitivity (NCWS). Using liquid chromatography-mass spectrometry (LC-MS)-based label-free quantitative (LFQ) proteomics of aqueous flour extracts, we determined 756 proteins across 150 wheat cultivars grown in three environments. However, only 303 proteins were stably expressed across all environments in at least one cultivar and only 89 proteins thereof across all 150 cultivars. This underlines the large influence of environmental conditions on the expression of many proteins. Wheat cultivars varied largely in their protein profile, shown by high coefficients of variation across different cultivars. Heritability (h2) ranged from 0-1, with 114 proteins having h² > 0.6, including important proteins for baking quality and human health. The expression of these 114 proteins should be amenable to targeted manipulation across the wheat supply chain by varietal choice and breeding for designing healthier wheat with better quality. Further technical development is urgently required to assign functions to identifiable proteins labeled yet uncharacterized in databases and speeding up detection methods to routinely use proteomics in wheat supply chains.

Genomics-Enabled Analysis of Puroindoline b2 Genes Identifies New Alleles in Wheat and Related Triticeae Species

Kernel hardness is a key trait of wheat seeds, largely controlled by two tightly linked genes Puroindoline a and b (Pina and Pinb). Genes homologous to Pinb, namely Pinb2, have been studied. Whether these genes contribute to kernel hardness and other important seed traits remains inconclusive. Using the high-quality bread wheat reference genome, we show that PINB2 are encoded by three homoeologous loci Pinb2 not syntenic to the Hardness locus, with Pinb2-7A locus containing three tandem copies. PINB2 proteins have several features conserved for the Pin/Pinb2 phylogenetic cluster but lack a structural basis of significant impact on kernel hardness. Pinb2 are seed-specifically expressed with varied expression levels between the homoeologous copies and among wheat varieties. Using the high-quality genome information, we developed new Pinb2 allele specific markers and demonstrated their usefulness by 1) identifying new Pinb2 alleles in Triticeae species; and 2) performing an association analysis of Pinb2 with kernel hardness. The association result suggests that Pinb2 genes may have no substantial contribution to kernel hardness. Our results provide new insights into Pinb2 evolution and expression and the new allele-specific markers are useful to further explore Pinb2's contribution to seed traits in wheat.

Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype-environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

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.

Genetic control of protein content and sedimentation volume in European winter wheat cultivars

Breeding of bread wheat in the last decades has maintained a high baking quality despite the intensive selection for grain yield. The quality trait sedimentation volume but not protein content is mainly controlled by the Glu - A1, Glu - B1, Glu - D1, Gli - B1 , and Pinb - D1 loci which are differentially used in varieties from different European origins. Protein content and sedimentation volume are two important quality traits in wheat breeding. In this study, we used a panel of 407 European winter wheat cultivars to dissect the genetic architecture of both traits and to assess the potential of genomics-assisted breeding. All lines were phenotyped in multi-location field trials, genotyped by a genotyping-by-sequencing approach, and assessed for the alleles at the Glu-A1, Glu-B1, Glu-D1, Gli-B1, and Pinb-D1 candidate loci. Our analyses revealed no effect of the candidate loci on protein content, but a strong effect on sedimentation volume, with Glu-B1 and Gli-B1 explaining 24.6 and 19.5 % of the genotypic variance, respectively. The genome-wide scan identified three quantitative trait loci (QTL) for protein content which jointly explained only 18.5 % of the genotypic variance. In contrast, four QTL were detected for sedimentation volume most likely identifying the Glu-B1 and Gli-B1 candidate loci and explaining approximately 60 % of the genotypic variance. We observed differences for both traits between countries of origin of the cultivars, accompanied by corresponding geographic differences in QTL allele frequencies. Furthermore, a genome-wide prediction approach resulted in a higher predictive ability for both traits as compared to marker-assisted selection based on the identified QTL. Taken together, our results illustrate a different genetic architecture of the two quality traits and show the potential of their genomics-assisted improvement.

Considering causal genes in the genetic dissection of kernel traits in common wheat

Genetic factors controlling thousand-kernel weight (TKW) were characterized for their association with other seed traits, including kernel width, kernel length, ratio of kernel width to kernel length (KW/KL), kernel area, and spike number per m² (SN). For this purpose, a genetic map was established utilizing a doubled haploid population derived from a cross between German winter wheat cultivars Pamier and Format. Association studies in a diversity panel of elite cultivars supplemented genetic analysis of kernel traits. In both populations, genomic signatures of 13 candidate genes for TKW and kernel size were analyzed. Major quantitative trait loci (QTL) for TKW were identified on chromosomes 1B, 2A, 2D, and 4D, and their locations coincided with major QTL for kernel size traits, supporting the common belief that TKW is a function of other kernel traits. The QTL on chromosome 2A was associated with TKW candidate gene TaCwi-A1 and the QTL on chromosome 4D was associated with dwarfing gene Rht-D1. A minor QTL for TKW on chromosome 6B coincided with TaGW2-6B. The QTL for kernel dimensions that did not affect TKW were detected on eight chromosomes. A major QTL for KW/KL located at the distal tip of chromosome arm 5AS is being reported for the first time. TaSus1-7A and TaSAP-A1, closely linked to each other on chromosome 7A, could be related to a minor QTL for KW/KL. Genetic analysis of SN confirmed its negative correlation with TKW in this cross. In the diversity panel, TaSus1-7A was associated with TKW. Compared to the Pamier/Format bi-parental population where TaCwi-A1a was associated with higher TKW, the same allele reduced grain yield in the diversity panel, suggesting opposite effects of TaCwi-A1 on these two traits.

Genome-wide association mapping of preharvest sprouting resistance in a diversity panel of European winter wheats

Global wheat production will benefit from cultivars showing genetic resistance to preharvest sprouting (PHS). Working on PHS resistance is still challenging due to the lack of simple protocols for the provocation of symptoms for appropriate trait differentiation under highly variable environmental conditions. Therefore, the availability of molecular markers for enhancing PHS resistance in breeding lines is of utmost importance. Genome-wide association mapping was performed to unravel the genetics of PHS resistance in a diversity panel of 124 winter wheat genotypes using both random and targeted marker locus approaches. Data for grain germination tests, spike wetting treatments, and field sprouting damage measurements of grains were collected in 11, 12, and four environments, respectively. Twenty-two quantitative trait loci (QTL) linked with 40 markers were detected for the three traits commonly used for assessing the PHS resistance of cultivars. All but five QTL on chromosomes 1B, 1D (two QTL), 3D, and 5D showed locations similar to previous studies, including prominent QTL on chromosomes 2BS, 3AS, and 4AL. The highest retrieval rate across environments was found for QTL on chromosomes 1D, 2BS, 3D, 4AL, and 7B. The study identified genomic signatures useful for marker-assisted improvement of PHS resistance not only in European breeding programs, but of global significance.

Genetic control of grain yield and grain physical characteristics in a bread wheat population grown under a range of environmental conditions

Genetic analysis of the yield and physical quality of wheat revealed complex genetic control, including strong effects of photoperiod-sensitivity loci. Environmental conditions such as moisture deficit and high temperatures during the growing period affect the grain yield and grain characteristics of bread wheat (Triticum aestivum L.). The aim of this study was to map quantitative trait loci (QTL) for grain yield and grain quality traits using a Drysdale/Gladius bread wheat mapping population grown under a range of environmental conditions in Australia and Mexico. In general, yield and grain quality were reduced in environments exposed to drought and/or heat stress. Despite large effects of known photoperiod-sensitivity loci (Ppd-B1 and Ppd-D1) on crop development, grain yield and grain quality traits, it was possible to detect QTL elsewhere in the genome. Some of these QTL were detected consistently across environments. A locus on chromosome 6A (TaGW2) that is known to be associated with grain development was associated with grain width, thickness and roundness. The grain hardness (Ha) locus on chromosome 5D was associated with particle size index and flour extraction and a region on chromosome 3B was associated with grain width, thickness, thousand grain weight and yield. The genetic control of grain length appeared to be largely independent of the genetic control of the other grain dimensions. As expected, effects on grain yield were detected at loci that also affected yield components. Some QTL displayed QTL-by-environment interactions, with some having effects only in environments subject to water limitation and/or heat stress.

Discovery, distribution and diversity of Puroindoline-D1 genes in bread wheat from five countries (Triticum aestivum L.)

BACKGROUND

Grain texture is one of the most important characteristics in bread wheat (Triticum aestivum L.). Puroindoline-D1 genes play the main role in controlling grain texture and are intimately associated with the milling and processing qualities in bread wheat.

RESULTS

A series of diagnostic molecular markers and dCAPS markers were used to characterize Pina-D1 and Pinb-D1 in 493 wheat cultivars from diverse geographic locations. A primer walking strategy was used to characterize PINA-null alleles at the DNA level. Results indicated that Chinese landraces encompassing 12 different Puroindoline-D1 allelic combinations showed the highest diversity, while CIMMYT wheat cultivars containing 3 different Puroindoline-D1 allelic combinations showed the lowest diversity amongst wheat cultivars from the five countries surveyed. Two novel Pina-D1 alleles, designated Pina-D1s with a 4,422-bp deletion and Pina-D1u with a 6,460-bp deletion in the Ha (Hardness) locus, were characterized at the DNA level by a primer walking strategy, and corresponding molecular markers Pina-N3 and Pina-N4 were developed for straightforward identification of the Pina-D1s and Pina-D1u alleles. Analysis of the association of Puroindoline-D1 alleles with grain texture indicated that wheat cultivars with Pina-null/Pinb-null allele, possessing an approximate 33-kb deletion in the Ha locus, have the highest SKCS hardness index amongst the different genotypes used in this study. Moreover, wheat cultivars with the PINA-null allele have significantly higher SKCS hardness index than those of Pinb-D1b and Pinb-D1p alleles.

CONCLUSIONS

Molecular characterization of the Puroindoline-D1 allele was investigated in bread wheat cultivars from five geographic regions, resulting in the discovery of two new alleles - Pina-D1s and Pina-D1u. Molecular markers were developed for both alleles. Analysis of the association of the Puroindoline-D1 alleles with grain texture showed that cultivars with PINA-null allele possessed relatively high SKCS hardness index. This study can provide useful information for the improvement of wheat quality, as well as give a deeper understanding of the molecular and genetic processes controlling grain texture in bread wheat.