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Background and Aims Physiological and morphological traits play essential roles in wheat (Triticum aestivum) growth and development. In particular, photosynthesis is a limitation to yield. Increasing photosynthesis in wheat has been identified as an important strategy to increase yield. However, the genotypic variations and the genomic regions governing morphological, architectural and photosynthesis traits remain unexplored. Methods Here, we conducted a large-scale investigation of the phenological, physiological, plant architectural and yield-related traits, involving 32 traits for 166 wheat lines during 2018–2020 in four environments, and performed a genome-wide association study with wheat 90K and 660K single nucleotide polymorphism (SNP) arrays. Key Results These traits exhibited considerable genotypic variations in the wheat diversity panel. Higher yield was associated with higher net photosynthetic rate (r = 0.41, P < 0.01), thousand-grain weight (r = 0.36, P < 0.01) and truncated and lanceolate shape, but shorter plant height (r = −0.63, P < 0.01), flag leaf angle (r = −0.49, P < 0.01) and spike number per square metre (r = −0.22, P < 0.01). Genome-wide association mapping discovered 1236 significant stable loci detected in the four environments among the 32 traits using SNP markers. Trait values have a cumulative effect as the number of the favourable alleles increases, and significant progress has been made in determining phenotypic values and favourable alleles over the years. Eleven elite cultivars and 14 traits associated with grain yield per plot (GY) were identified as potential parental lines and as target traits to develop high-yielding cultivars. Conclusions This study provides new insights into the phenotypic and genetic elucidation of physiological and morphological traits in wheat and their associations with GY, paving the way for discovering their underlying gene control and for developing enhanced ideotypes in wheat breeding.

Genetic markers can be linked with eco-physiological crop models to accurately predict genotype performance and individual markers' contributions in target environments, exploring interactions between genotype and environment. Here, wheat (Triticum aestivum L.) yield was dissected into seven traits corresponding to cultivar genetic coefficients in an eco-physiological model. Loci for these traits were discovered through the genome-wide association studies (GWAS). The cultivar genetic coefficients were derived from the loci using multiple linear regression or random forest, building a marker-based eco-physiological model. It is then applied to simulate wheat yields and design virtual ideotypes. The results indicated that the loci identified through GWAS explained 46%-75% variations in cultivar genetic coefficients. Using the marker-based model, the normalized root mean square error (nRMSE) between the simulated yield and observed yield was 13.95% by multiple linear regression and 13.62% by random forest. The nRMSE between the simulated and observed maturity dates was 1.24% by multiple linear regression and 1.11% by random forest, respectively. Structural equation modelling indicated that variations in grain yield could be well explained by cultivar genetic coefficients and phenological data. In addition, 24 pleiotropic loci in this study were detected on 15 chromosomes. More significant loci were detected by the model-based dissection method than considering yield per se. Ideotypes were identified by higher yield and more favourable alleles of cultivar genetic traits. This study proposes a genotype-to-phenotype approach and demonstrates novel ideas and tools to support the effective breeding of new cultivars with high yield through pyramiding favourable alleles and designing crop ideotypes.

Wheat (Triticum aestivum L.) radiation use efficiency (RUE) must be raised through crop breeding to further increase the yield potential, as the harvest index is now close to its theoretical limit. Field experiments including 209 wheat cultivars which have been widely cultivated in China since the 1940s were conducted in two growing seasons (2018-2019 and 2019-2020) to evaluate the variations of phenological, physiological, plant architectural, and yield-related traits and their contributions to RUE and to identify limiting factors for wheat yield potential. The average annual genetic gain in grain yield was 0.60% (or 45.32 kg ha

Early prediction of grain yield helps scientists to make better breeding decisions for wheat. Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based multi-sensor data can improve the prediction accuracy of crop yield. For this, five ML algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat. A set of thirty wheat cultivars and breeding lines were grown under three irrigation treatments i.e., light, moderate and high irrigation treatments to evaluate the yield prediction capabilities of a low-cost multi-sensor (RGB, multi-spectral and thermal infrared) UAV platform. Multi-sensor data fusion-based yield prediction showed higher accuracy compared to individual-sensor data in each ML model. The coefficient of determination (The online version contains supplementary material available at 10.1007/s11119-022-09938-8.

Environmental stresses from climate change can alter source–sink relations during plant maturation, leading to premature senescence and decreased yields. Elucidating the genetic control of natural variations for senescence in wheat (Triticum aestivum) can be accelerated using recent developments in unmanned aerial vehicle (UAV)-based imaging techniques. Here, we describe the use of UAVs to quantify senescence in wheat using vegetative indices (VIs) derived from multispectral images. We detected senescence with high heritability, as well as its impact on grain yield (GY), in a doubled-haploid population and parent cultivars at various growth time points (TPs) after anthesis in the field. Selecting for slow senescence using a combination of different UAV-based VIs was more effective than using a single ground-based vegetation index. We identified 28 quantitative trait loci (QTL) for vegetative growth, senescence, and GY using a 660K single-nucleotide polymorphism array. Seventeen of these new QTL for VIs from UAV-based multispectral imaging were mapped on chromosomes 2B, 3A, 3D, 5A, 5D, 5B, and 6D; these QTL have not been reported previously using conventional phenotyping methods. This integrated approach allowed us to identify an important, previously unreported, senescence-related locus on chromosome 5D that showed high phenotypic variation (up to 18.1%) for all UAV-based VIs at all TPs during grain filling. This QTL was validated for slow senescence by developing kompetitive allele-specific PCR markers in a natural population. Our results suggest that UAV-based high-throughput phenotyping is advantageous for temporal assessment of the genetics underlying for senescence in wheat., Temporal multispectral imaging from unmanned aerial vehicles can be used to quantify senescence and identify underlying quantitative trait loci in wheat (Triticum aestivum).

Plant nitrogen assimilation and use efficiency in the seedling's root system are beneficial for adult plants in field condition for yield enhancement. Identification of the genetic basis between root traits and N uptake plays a crucial role in wheat breeding. In the present study, 198 doubled haploid lines from the cross of Yangmai 16/Zhongmai 895 were used to identify quantitative trait loci (QTLs) underpinning four seedling biomass traits and five root system architecture (RSA) related traits. The plants were grown under hydroponic conditions with control, low and high N treatments (Ca(NO3)2·4H2O at 0, 0.05 and 2.0 mmol L−1, respectively). Significant variations among the treatments and genotypes, and positive correlations between seedling biomass and RSA traits (r=0.20 to 0.98) were observed. Inclusive composite interval mapping based on a high-density map from the Wheat 660K single nucleotide polymorphisms (SNP) array identified 51 QTLs from the three N treatments. Twelve new QTLs detected on chromosomes 1AL (1) in the control, 1DS (2) in high N treatment, 4BL (5) in low and high N treatments, and 7DS (3) and 7DL (1) in low N treatments, are first reported in influencing the root and biomass related traits for N uptake. The most stable QTLs (RRS.caas-4DS) on chromosome 4DS, which were related to ratio of root to shoot dry weight trait, was in close proximity of the Rht-D1 gene, and it showed high phenotypic effects, explaining 13.1% of the phenotypic variance. Twenty-eight QTLs were clustered in 12 genetic regions. SNP markers tightly linked to two important QTLs clusters C10 and C11 on chromosomes 6BL and 7BL were converted to kompetitive allele-specific PCR (KASP) assays that underpin important traits in root development, including root dry weight, root surface area and shoot dry weight. These QTLs, clusters and KASP assays can greatly improve the efficiency of selection for root traits in wheat breeding programmes.

Background: Wheat is an important food crop globally, and timely prediction of wheat yield in breeding efforts can improve the efficiency of selection. Traditional plant breeding based on grain yield selection is time-consuming, costly, and destructive. There is a great need for innovative methods to enhance efficiency and accelerate genetic gains in the breeding cycle. Results: In this study, a new ensemble learning method was developed to predict wheat yield by combining hyperspectral data and deep learning-based regression technology. For this, 207 wheat cultivars and breeding lines were grown under full and limited irrigation treatments, and their canopy hyperspectral reflectance was measured at the flowering, early grain fill (EGF), mid grain fill (MGF), and late grain fill (LGF). Firstly 115 vegetation indices (VIs) were extracted from the hyperspectral reflectance and combined with four feature selection methods i.e., mean decrease impurity (MDI), Boruta, FeaLect, and Relief to train deep neural network (DNN) models for yield prediction. Then, a novel ensemble learning framework was developed by combining the predicted values of selected and the full features using multiple linear regression (MLR). The results show that feature selection methods achieved higher yield prediction accuracy than the full features, where MDI method performed best across growth stages, with the mean R2 ranging from 0.634-0.666 (mean RMSE = 0.926-0.967 t ha-1). The proposed ensemble method outperformed all the FS methods across growth stages, with the mean R2 ranging from 0.648-0.679 (mean RMSE = 0.911-0.950 t ha-1). Conclusions: By integrating multiple FS methods and DNN, more prediction potential of hyperspectral data can be exploited, and this ensemble method is also applicable to trait estimation of other crops.

Stem lignin content (SLC) in common wheat (Triticum aestivum L.) contributes to lodging resistance. Caffeic acid 3-O-methyltransferase (COMT) is a key enzyme involved in lignin biosynthesis. Characterization of TaCOMT genes and development of gene-specific markers could enable marker-assisted selection in wheat breeding. In the present study, the full-length genomic DNA (gDNA) sequences of TaCOMT genes located on chromosomes 3A, 3B, and 3D were cloned by homologous cloning. Two allelic variants, TaCOMT-3Ba and TaCOMT-3Bb, were identified and differed by a 222-bp insertion/deletion (InDel) in the 3′-untranslated region (3′-UTR). A co-dominant gene-specific marker based on this InDel was developed and designated as TaCOMT-3BM. A total of 157 wheat cultivars and advanced lines grown in four environments were used to validate the associations between allelic patterns and SLC. The SLC of cultivars with TaCOMT-3Ba was significantly (P

Background Phosphorus (P) is an important in ensuring plant morphogenesis and grain quality, therefore an efficient root system is crucial for P-uptake. Identification of useful loci for root morphological and P uptake related traits at seedling stage is important for wheat breeding. The aims of this study were to evaluate phenotypic diversity of Yangmai 16/Zhongmai 895 derived doubled haploid (DH) population for root system architecture (RSA) and biomass related traits (BRT) in different P treatments at seedling stage using hydroponic culture, and to identify QTL using 660 K SNP array based high-density genetic map. Results All traits showed significant variations among the DH lines with high heritabilities (0.76 to 0.91) and high correlations (r = 0.59 to 0.98) among all traits. Inclusive composite interval mapping (ICIM) identified 34 QTL with 4.64–20.41% of the phenotypic variances individually, and the log of odds (LOD) values ranging from 2.59 to 10.43. Seven QTL clusters (C1 to C7) were mapped on chromosomes 3DL, 4BS, 4DS, 6BL, 7AS, 7AL and 7BL, cluster C5 on chromosome 7AS (AX-109955164 - AX-109445593) with pleiotropic effect played key role in modulating root length (RL), root tips number (RTN) and root surface area (ROSA) under low P condition, with the favorable allele from Zhongmai 895. Conclusions This study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture. It is an efficient approach for screening of large populations under different nutrient conditions. Four QTL on chromosomes 6BL (2) and 7AL (2) identified in low P treatment showed positive additive effects contributed by Zhongmai 895, indicating that Zhongmai 895 could be used as parent for P-deficient breeding. The most stable QTL QRRS.caas-4DS for ratio of root to shoot dry weight (RRS) harbored the stable genetic region with high phenotypic effect, and QTL clusters on 7A might be used for speedy selection of genotypes for P-uptake. SNPs closely linked to QTLs and clusters could be used to improve nutrient-use efficiency.

Additional file 1: Figure S1. Distribution of line and parent means for each trait in three levels of P treatments.

Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha-1, T2 = 180 kg ha-1, and T3 = 240 kg ha-1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R 2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R 2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R 2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha-1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.

We developed and validated high-throughput Kompetitive Allele-Specific PCR (KASP) assays for key genes underpinning bread-making quality, including the wbm gene on chromosome 7AL and over-expressed glutenin Bx7OE (Glu-B1al) gene. Additionally, we used pre-existing KASP assay for Sec1 (1B.1R translocation) gene on chromosome 1B. The newly developed KASP assays were compared with gel-based markers for reliability and phenotypically validated in a diversity panel for Mixograph, Rapid Visco Analyzer (RVA) and Mixolab traits. Genotypes carrying the 1B.1R translocation had significantly lower Mixolab parameters than those without the translocation. Similarly, superior effects of the wbm+ and Bx7OE alleles on Mixograph and RVA properties and their extremely low frequencies in global wheat collections supported the idea of using these genes for bread-making quality improvement. The allele frequencies of wbm+ and Bx7OE were extremely low in historical Chinese and CIMMYT wheat germplasm, but were relatively higher in synthetic hexaploid wheats and their breeding derivatives. In both the Vavilov and Watkins global landrace collections, the frequency of wbm+ was 6.4 and 3.5%, and frequency of Bx7OE was 3.2% and 7.0%, respectively. The high-throughput marker resources and large-scale global germplasm screening provided further opportunities to exploit these genes in wheat breeding to enhance bread-making quality.

GWAS identified 36 potentially new loci for wheat stem water-soluble carbohydrate (WSC) contents and 13 pleiotropic loci affecting WSC and thousand-kernel weight. Five KASP markers were developed and validated. Water-soluble carbohydrates (WSC) reserved in stems contribute significantly to grain yield (GY) in wheat. However, knowledge of the genetic architecture underlying stem WSC content (SWSCC) is limited. In the present study, 166 diverse wheat accessions from the Yellow and Huai Valleys Winter Wheat Zone of China and five other countries were grown in four well-watered environments. SWSCC at 10 days post-anthesis (10DPA), 20DPA and 30DPA, referred as WSC10, WSC20 and WSC30, respectively, and thousand-kernel weight (TKW) were assessed. Correlation analysis showed that TKW was significantly and positively correlated with WSC10 and WSC20. Genome-wide association study was performed on SWSCC and TKW with 373,106 markers from the wheat 660 K and 90 K SNP arrays. Totally, 62 stable loci were detected for SWSCC, with 36, 24 and 19 loci for WSC10, WSC20 and WSC30, respectively; among these, 36 are potentially new, 16 affected SWSCC at two or three time-points, and 13 showed pleiotropic effects on both SWSCC and TKW. Linear regression showed clear cumulative effects of favorable alleles for increasing SWSCC and TKW. Genetic gain analyses indicated that pyramiding favorable alleles of SWSCC had simultaneously improved TKW. Kompetitive allele-specific PCR markers for five pleiotropic loci associated with both SWSCC and TKW were developed and validated. This study provided a genome-wide landscape of the genetic architecture of SWSCC, gave a perspective for understanding the relationship between WSC and GY and explored the theoretical basis for co-improvement of WSC and GY. It also provided valuable loci and markers for future breeding.

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields.

Identification of genes for tolerance against phosphorus (P) deficiency is important for wheat production in P-deficient soils. Nine root system architecture (RSA) and root biomass-related traits (RBT) in 198 double haploid lines derived from cross between Yangmai 16/Zhongmai 895 were characterized under zero (control), low, and high P treatments (KH2PO4 contents: 0, 0.005, 0.25 mmol/L, respectively) in hydroponic culture, and the DH lines and parents were genotyped using Wheat 660K SNP array. All traits showed significant variations among the DH lines with high heritabilities (0.76 to 0.91) and high correlations (r =0.59 to 0.98) with each other. Inclusive composite interval mapping using 10,242 markers identified 34 QTLs for all traits in the three P treatments. Of these, 11 QTLs under low P condition on chromosomes 2BL, 4BS (2 QTLs), 4DS (2), 6BL (2), 7AL (2), and 7BL (2) for root length (RL), root tip number (RTN) and ratio of root to shoot dry weight (RRS) explained 4.6 - 20.4% of the phenotypic variances. QRRS.caas-4DS (16.64 - 30.66 Mb) was detected in all three P treatments and accounted for 8.4 to 20.4% of the phenotypic variances. Seven QTLs clusters (C1 to C7) were mapped on chromosomes 3DL, 4BS, 4DS, 6BL, 7AS, 7AL and 7BL, of which five (C3 to C7) were detected under low P condition. Cluster C5 on chromosome 7AS (AX-109955164 - AX-109445593) played the key role in modulating RL, RTN and ROSA, with the favorable allele coming from Zhongmai 895. SNPs closely linked to QTLs and clusters could be used for improving root nutrient-use efficiency traits in future breeding.

Background Plant height is an important selection target since it is associated with yield potential, stability and particularly with lodging resistance in various environments. Rapid and cost-effective estimation of plant height from airborne devices using a digital surface model can be integrated with academic research and practical wheat breeding programs. A bi-parental wheat population consisting of 198 doubled haploid lines was used for time-series assessments of progress in reaching final plant height and its accuracy was assessed by quantitative genomic analysis. UAV-based data were collected at the booting and mid-grain fill stages from two experimental sites and compared with conventional measurements to identify quantitative trait loci (QTL) underlying plant height. Results A significantly high correlation of R2 = 0.96 with a 5.75 cm root mean square error was obtained between UAV-based plant height estimates and ground truth observations at mid-grain fill across both sites. Correlations for UAV and ground-based plant height data were also very high (R2 = 0.84–0.85, and 0.80–0.83) between plant height at the booting and mid-grain fill stages, respectively. Broad sense heritabilities were 0.92 at booting and 0.90–0.91 at mid-grain fill across sites for both data sets. Two major QTL corresponding to Rht-B1 on chromosome 4B and Rht-D1 on chromosome 4D explained 61.3% and 64.5% of the total phenotypic variations for UAV and ground truth data, respectively. Two new and stable QTL on chromosome 6D seemingly associated with accelerated plant growth was identified at the booting stage using UAV-based data. Genomic prediction accuracy for UAV and ground-based data sets was significantly high, ranging from r = 0.47–0.55 using genome-wide and QTL markers for plant height. However, prediction accuracy declined to r = 0.20–0.31 after excluding markers linked to plant height QTL. Conclusion This study provides a fast way to obtain time-series estimates of plant height in understanding growth dynamics in bread wheat. UAV-enabled phenotyping is an effective, high-throughput and cost-effective approach to understand the genetic basis of plant height in genetic studies and practical breeding. Electronic supplementary material The online version of this article (10.1186/s13007-019-0419-7) contains supplementary material, which is available to authorized users.

Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

The simultaneous improvement of protein content (PC) and grain yield (GY) in bread wheat (Triticum aestivum L.) under low-input management enables the development of resource-use efficient varieties that combine high grain yield potential with desirable end-use quality. However, the complex mechanisms of genotype, management, and growing season, and the negative correlation between PC and GY complicate the simultaneous improvement of PC and GY under low-input management. To identify favorable genotypes for PC and GY under low-input management, this study used 209 wheat varieties, including strong gluten, medium-strong gluten, medium gluten, weak gluten, winter, semi-winter, weak-spring, and spring types, which has been promoted from the 1980s to the 2010s. Allelic genotyping, performed using kompetitive allele-specific polymerase chain reaction (KASP) technology, found 69 types of GY-PC allelic combinations in the tested materials. Field trials were conducted with two growing season treatments (2018–2019 and 2019–2020) and two management treatments (conventional management and low-input management). Multi-environment analysis of variance showed that genotype, management, and growing season had extremely substantial effects on wheat GY and PC, respectively, and the interaction of management × growing season also had extremely significant effects on wheat GY. According to the three-sigma rule of the normal distribution, the GY of wheat varieties Liangxing 66 and Xinmai 18 were stable among the top 15.87% of all tested materials with high GY, and their PC reached mean levels under low-input management, but also stably expressed high GY and high PC under conventional management, which represents a great development potential. These varieties can be used as cultivars of interest for breeding because TaSus1-7A, TaSus1-7B, TaGW2-6A, and TaGW2-6B, which are related to GY, and Glu-B3, which is related to PC, carry favorable alleles, among which Hap-1/2, the allele of TaSus1-7A, and Glu-B3b/d/g/i, the allele of Glu-B3, can be stably expressed. Our results may be used to facilitate the development of high-yielding and high-quality wheat varieties under low-input management, which is critical for sustainable food and nutrition security.

Many physiological traits are associated with grain yield and yield-related traits in wheat. The aim of the present study was to identify quantitative trait loci (QTL) for physiological traits, including ground cover, normalized difference in vegetation index and canopy temperature depression, and closely linked molecular markers for marker-assisted breeding. Two hundred and forty-six F8 recombinant inbred lines (RILs) come from the Zhou 8425B/Chinese Spring cross were genotyped using the high-density Illumina iSelect 90K single nucleotide polymorphism (SNP) assay. Field trials were conducted under irrigated conditions at Zhengzhou and Zhoukou in Henan province, during the 2012–2013 and 2013–2014 cropping seasons. Analysis of variance of physiological traits showed significant differences (P

Bread-making quality is a core trait for wheat breeding programs. Recently, the expression of a novel gene named wheat bread making (wbm) gene has been associated with good bread-making quality. In this study, 54 historical and modern bread wheat genotypes from CIMMYT were screened by PCR marker for the presence of the allele associated with high wbm expression. Eight of the 54 wheat genotypes tested positive for the wbm allele and the genotype Waxwing was identified as the most likely donor of the allele in part of CIMMYT germplasm. The wbm allele had a significant effect on overall gluten quality, gluten strength, gluten extensibility and bread-making quality, although its effect was smaller than the effects of other quality related genes as Glu-D1, Glu-B1 or the negative effect of the 1BL.1RS translocation. The wbm allele was associated with higher values of the traits mentioned but not with higher protein content. The identification of this new wbm gene/protein is a step forward in understanding wheat quality genetic control. Implementation of marker assisted selection in breeding programs to detect the wbm allele is highly recommended.

We cloned TaSST genes, developed a gene-specific marker for TaSST - D1 , and identified three QTL in the Doumai/Shi 4185 RIL population. TaSST - D1 is within one of the three QTL. Sucrose:sucrose-1-fructosyltransferase (1-SST), a critical enzyme in the fructan biosynthetic pathway, is significantly and positively associated with water soluble carbohydrate (WSC) content in bread wheat stems. In the present study, wheat 1-SST genes (TaSST) were isolated and located on chromosomes 4A, 7A and 7D. Sequence analysis of TaSST-D1 revealed 15 single nucleotide polymorphisms (SNP) in the third exon between cultivars with higher and lower WSC content. A cleaved amplified polymorphism sequence (CAPS) marker, WSC7D, based on the polymorphism at position 1216 (C-G) was developed to discriminate the two alleles. WSC7D was located on chromosome 7DS using a recombinant inbred line (RIL) population from a Doumai/Shi 4185 cross, and a set of Chinese Spring nullisomic-tetrasomic lines. TaSST-D1 co-segregated with the CAPS marker WSC7D and was linked to SNP marker BS00108793_51 on chromosome 7DS at a genetic distance of 6.1 cM. It explained 8.8, 10.9, and 11.3 % of the phenotypic variances in trials at Beijing and Shijiazhuang as well as the averaged data from those environments, respectively. Two additional QTL (QWSC.caas-4BS and QWSC.caas-7AS) besides TaSST-D1 were mapped in the RIL population. One hundred and forty-nine Chinese wheat cultivars and advanced lines tested in four environments were used to validate a highly significant (P