Improvement of end-use quality in bread wheat (Triticum aestivum L.) depends on a thorough understanding of the genetic basis of important quality traits. The main goal of our study was to investigate the genetic basis of 1,000-kernel weight, protein content, sedimentation volume, test weight, and starch concentration using an association mapping approach. We fingerprinted 207 diverse European elite soft winter wheat lines with 115 SSR markers and evaluated the genotypes in multi-environment trials. The principal coordinate analysis revealed absence of a clear population but presence of a family structure. Therefore, we used linear mixed models and marker-based kinship matrices to correct for family structure. In genome-wide scans, we detected main effect QTL for all five traits. In contrast, epistatic QTL were only observed for sedimentation volume and test weight explaining a small proportion of the genotypic variation. Consequently, our findings suggested that integrating epistasis in marker-assisted breeding will not lead to substantially increased selection gain for quality traits in soft winter wheat.
Wheat is one of the most important cereals, whose growth and development is strongly limited by drought. This study investigated the physiological and metabolic response of six winter wheat cultivars to drought with the emphasis on the induction of dominant metabolites affected by the treatment and genotypes or both. The plants were exposed to a moderate (non-lethal) drought stress, which was induced by withholding watering for six days under controlled greenhouse conditions. A decline in CO2 assimilation (Pn) and transpiration rate, stomata closure, a decrease in relative water content (RWC) and increase of malondialdehyde content were observed in drought-treated plants of all cultivars. These changes were most pronounced in Ellvis, while Soissons was able to retain the higher RWC and Pn. Among the studied metabolites, sugars (sucrose, glucose, fructose, several disaccharides), organic acids (malic acid, oxalic acids), amino acids (proline, threonine, gamma-aminobutyric acid (GABA), glutamine) and sugar alcohols such as myo-inositol accumulated to higher levels in the plants exposed to drought stress in comparison with the control. The accumulation of several metabolites in response to drought differed between the genotypes. Drought induced the production of sucrose, malic acid and oxalic acid, unknown organic acid 1, unknown disaccharide 1, 2 and 3, GABA, L-threonine, glutamic acid in four (Soissons, Zitarka, Antonija or Toborzo) out of six genotypes. In addition, Soissons, which was the most drought tolerant genotype, accumulated the highest amount of unknown disaccharide 5, galactonic and phosphoric acids. The two most drought sensitive cultivars, Srpanjka and Ellvis, demonstrated different metabolic adjustment in response to the stress treatment. Srpanjka responded to drought by increasing the amount of glucose and fructose originated from hydrolyses of sucrose and accumulating unidentified sugar alcohols 1 and 2. In Ellvis, drought caused inhibition of photosynthetic carbon metabolism, as evidence by the decreased Pn, gs, RWC and accumulation levels of sugar metabolites (sucrose, glucose and fructose). The results revealed the differences in metabolic response to drought among the genotypes, which drew attention on metabolites related with general response and on those metabolites which are part of specific response that may play an important role in drought tolerance.
A powdery mildew resistance gene was introgressed from Aegilops speltoides into winter wheat and mapped to chromosome 5BL. Closely linked markers will permit marker-assisted selection for the resistance gene. Powdery mildew of wheat (Triticum aestivum L.) is a major fungal disease in many areas of the world, caused by Blumeria graminis f. sp. tritici (Bgt). Host plant resistance is the preferred form of disease prevention because it is both economical and environmentally sound. Identification of new resistance sources and closely linked markers enable breeders to utilize these new sources in marker-assisted selection as well as in gene pyramiding. Aegilops speltoides (2n = 2x = 14, genome SS), has been a valuable disease resistance donor. The powdery mildew resistant wheat germplasm line NC09BGTS16 (NC-S16) was developed by backcrossing an Ae. speltoides accession, TAU829, to the susceptible soft red winter wheat cultivar ‘Saluda’. NC-S16 was crossed to the susceptible cultivar ‘Coker 68-15’ to develop F2:3 families for gene mapping. Greenhouse and field evaluations of these F2:3 families indicated that a single gene, designated Pm53, conferred resistance to powdery mildew. Bulked segregant analysis showed that multiple simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers specific to chromosome 5BL segregated with the resistance gene. The gene was flanked by markers Xgwm499, Xwmc759, IWA6024 (0.7 cM proximal) and IWA2454 (1.8 cM distal). Pm36, derived from a different wild wheat relative (T. turgidum var. dicoccoides), had previously been mapped to chromosome 5BL in a durum wheat line. Detached leaf tests revealed that NC-S16 and a genotype carrying Pm36 differed in their responses to each of three Bgt isolates. Pm53 therefore appears to be a new source of powdery mildew resistance.
Modern genomics approaches rely on the availability of high-throughput and high-density genotyping platforms. A major breakthrough in wheat genotyping was the development of an SNP array. In this study, we used a diverse panel of 172 elite European winter wheat lines to evaluate the utility of the SNP array for genomic analyses in wheat germplasm derived from breeding programs. We investigated population structure and genetic relatedness and found that the results obtained with SNP and SSR markers differ. This suggests that additional research is required to determine the optimum approach for the investigation of population structure and kinship. Our analysis of linkage disequilibrium (LD) showed that LD decays within approximately 5–10 cM. Moreover, we found that LD is variable along chromosomes. Our results suggest that the number of SNPs needs to be increased further to obtain a higher coverage of the chromosomes. Taken together, SNPs can be a valuable tool for genomics approaches and for a knowledge-based improvement of wheat.
Association mapping identified quantitative trait loci (QTLs) and the markers linked to pre-harvest sprouting (PHS) resistance in an elite association mapping panel of white winter wheat comprising 198 genotypes. A total of 1,166 marker loci including DArT and SSR markers representing all 21 chromosomes of wheat were used in the analysis. General and mixed linear models were used to analyze PHS data collected over 4 years. Association analysis identified eight QTLs linked with 13 markers mapped on seven chromosomes. A QTL was detected on each arm of chromosome 2B and one each on chromosome arms 1BS, 2DS, 4AL, 6DL, 7BS and 7DS. All except the QTL on 7BS are located in a location similar to previous reports and, if verified, the QTL on 7BS is likely to be novel. Principal components and the kinship matrix were used to account for the presence of population structure but had only a minor effect on the results. Although, none of the QTLs was highly significant across all environments, a QTL on the long arm of chromosome 4A was detected in three different environments and also using the best linear unbiased predictions over years. Although previous reports have identified this as a major QTL, its effects were minor in our biparental mapping populations. The results of this study highlight the benefits of association mapping and the value of using elite material in association mapping for plant breeding programs.
Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head.
This article covers detailed characterization and naming of QSr.sun - 5BL as Sr56 . Molecular markers linked with adult plant stem rust resistance gene Sr56 were identified and validated for marker-assisted selection. The identification of new sources of adult plant resistance (APR) and effective combinations of major and minor genes is well appreciated in breeding for durable rust resistance in wheat. A QTL, QSr.sun-5BL, contributed by winter wheat cultivar Arina providing 12–15 % reduction in stem rust severity, was reported in an Arina/Forno recombinant inbred line (RIL) population. Following the demonstration of monogenic segregation for APR in the Arina/Yitpi RIL population, the resistance locus was formally named Sr56. Saturation mapping of the Sr56 region using STS (from EST and DArT clones), SNP (9 K) and SSR markers from wheat chromosome survey sequences that were ordered based on synteny with Brachypodium distachyon genes in chromosome 1 resulted in the flanking of Sr56 by sun209 (SSR) and sun320 (STS) at 2.6 and 1.2 cM on the proximal and distal ends, respectively. Investigation of conservation of gene order between the Sr56 region in wheat and B. distachyon showed that the syntenic region defined by SSR marker interval sun209-sun215 corresponded to approximately 192 kb in B. distachyon, which contains five predicted genes. Conservation of gene order for the Sr56 region between wheat and Brachypodium, except for two inversions, provides a starting point for future map-based cloning of Sr56. The Arina/Forno RILs carrying both Sr56 and Sr57 exhibited low disease severity compared to those RILs carrying these genes singly. Markers linked with Sr56 would be useful for marker-assisted pyramiding of this gene with other major and APR genes for which closely linked markers are available.
A suitable planting pattern and irrigation strategy are essential for optimizing winter wheat yield and water use efficiency (WUE). The study aimed to evaluate the impact of planting pattern and irrigation frequency on grain yield and WUE of winter wheat. During the 20132014 and 2014-2015 winter wheat growing seasons in the North China Plain, the effects of planting patterns and irrigation frequencies were determined on tiller number, grain yield, and WUE. The two planting patterns tested were wide-precision and conventional-cultivation. Each planting pattern had three irrigation regimes: irrigation (120 mm) at the jointing stage; irrigation (60 mm) at both the jointing and heading stages; and irrigation (40 mm) at the jointing, heading, and milking stages. In our study, tiller number was significantly higher in the wide-precision planting pattern than in the conventional-cultivation planting pattern. Additionally, the highest grain yields and WUE were observed when irrigation was applied at the jointing stage (120 mm) or at the jointing and heading stages (60 mm each) in the wide-precision planting pattern. These results could be attributed to higher tiller numbers as well as reduced water consumption due to reduced irrigation frequency. In both growing seasons, applying 60 mm of water at jointing and heading stages resulted in the highest grain yield among the treatments. Based on our results, for winter wheat production in semi-humid regions, we recommend a wide-precision planting pattern with irrigation (60 mm) at both the jointing and heading stages.
Air temperature variability and trends in Romania were analysed using monthly, seasonal, and annual datasets. Temperature data of winter wheat season were also analysed. The Mann-Kendall test, Sen’s slope estimate, the sequential version of the Mann-Kendall test, the Pettitt test and spatial and temporal hierarchical cluster analyses were used. First, the datasets were checked for changing points. The 106-year period was divided into two long periods of 100 years each to verify the importance of a very short interval in changing of general trends; after that it was divided into three shorter periods of 35–36 years each. The main conclusions are as follows: the 6 years making up the difference between the two long periods are very important in the context of the recent global warming; the three shorter periods analysis indicate some fluctuations rather than continuous warming. The latest short period is the most relevant for global warming. Spatial hierarchical cluster analysis indicated the existence of two distinctive groups. One of them, which includes stations in the south-east part of the country, seems to be influenced by the Black Sea surface temperature. Temporal hierarchical cluster analysis reveals that annual data series have the closest relation with the summer data series. Further, the impact of temperature changes on winter wheat phenology was determined using a phenology simulation performed with the model from the Decision Support System for Agrotechnology Transfer v. 18.104.22.168 platform. Earlier occurrences of anthesis and maturity were noticed for several regions in the country.
In this study, a big research progress has made in the research concerning leaf nitrogen content (LNC) nutritional spectral diagnosis on winter wheat at several growth stages, in which typical wave bands were put forward and quantitative models were constructed and validated. First, the unmanned aerial vehicle (UAV)-based hyperspectral data and the corresponding LNC data on winter wheat at several growth stages were obtained through experimenting in 2015, and the measured hyperspectral data and the LNC data were also obtained from the field-measured experimentation in 2014. Second, the spectral indices were calculated using UAV-based hyperspectral data and measured hyperspectral data, and the statistical regression models for diagnosing the LNC of different growth stages were constructed and analysed. Then, the correlation between the LNC and the spectral band is analysed. A method for selecting the typical bands of hyperspectral data responding to the LNC is proposed using spectral correlation as the basis. The UAV-based hyperspectral bands sensitive to the LNC of winter wheat are determined using this method. Finally, the hyperspectral quantitative models for diagnosing the LNC at the four stages are established by multifactor statistical regression and Back Propagation (BP) neural network methods. By comparing the modelling and verifying the coefficient, the UAV-based quantitative hyperspectral models' effectiveness and practicability are then validated. The modelling results show that the predicted values are very ideal in jointing stage, flagging leaf stage, and flowering stage, while it is slightly less in the filling stage. The BP neural network modelling results were generally better than the multiple linear regression modelling results. This demonstrates that the effectiveness and spectrum sampling precision of UAV-based hyperspectral data are believable.