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In this study, Levinson-type inequalities for the class of n-convex (n ≥ 3 ) functions are generalized using new Green functions and the Hermite interpolating polynomial involving two types of data points. Some estimations for novel functionals are derived using f-divergence. Furthermore, different inequalities involving Shannon entropies are presented. [ABSTRACT FROM AUTHOR]

Digital signal processing (DSP) is one of the most effective tools that have influenced science, engineering, and technology in the twenty-first century. Since 1970, advancements in the expansive field of DSP have made it a crucial tool in many applications. Many in-depth investigations on the filters and the extraction of helpful information have been conducted by researchers in a variety of scientific domains. In this paper, digital filters have been applied on radon (thereafter called to be as 222Rn) and thoron (thereafter to be called as 220Rn) time series signals to extract meaningful information from the original signals. By employing finite impulse response (FIR) equiripple and infinite impulse response (IIR) Butterworth filters to the soil 222Rn and 220Rn signals, the extracted features have been examined to obtain the improved output of the signal. The Persistence spectrums and spectrograms of FIR and IIR filtered 222Rn and 220Rn are constructed to check the anomalous shift in frequency, power and amplitude of the signals, due to the seismic events, if any, that took place during the research period. Finding of study shows that FIR and IIR filtered 220Rn indicates the anomalous behavior, nine and eleven days, before the seismic event of M 4.3 and M 2.5 occurred during the study period on 21st and 23rd of March 2017. Radon and thoron also shows anomalous nature twelve and nine days, before the earthquake of magnitude M 4.8 occurred during the study. Some anomalies appearing in the original 222Rn and 220Rn time series are found to have no connection with seismic events rather meteorological factors are believed to be responsible for them. [ABSTRACT FROM AUTHOR]

In this paper, four new Green functions are used to generalize Levinson-type inequalities for the class of 3-convex functions. The f-divergence, Renyi entropy, Renyi divergence, Shannon entropy, and the Zipf–Mandelbrot law are also used to apply the main results to information theory. [ABSTRACT FROM AUTHOR]

Modern wheat shows phenomenal evolutional success and adaptability to a range of environments owing to polyploidization; however, during its hybridization process a major genetic gain has been overlooked. Recently, Gaurav et al. emphasized harnessing genetic diversity from wheat wild progenitor Aegilops tauschii for the improvement of hexaploid wheat through introgression or transgenesis. [ABSTRACT FROM AUTHOR]

Key message: We discussed the most recent efforts in wheat functional genomics to discover new genes and their deployment in breeding with special emphasis on advances in Asian countries. Wheat research community is making significant progress to bridge genotype-to-phenotype gap and then applying this knowledge in genetic improvement. The advances in genomics and phenomics have intrigued wheat researchers in Asia to make best use of this knowledge in gene and trait discovery. These advancements include, but not limited to, map-based gene cloning, translational genomics, gene mapping, association genetics, gene editing and genomic selection. We reviewed more than 57 homeologous genes discovered underpinning important traits and multiple strategies used for their discovery. Further, the complementary advancements in wheat phenomics and analytical approaches to understand the genetics of wheat adaptability, resilience to climate extremes and resistance to pest and diseases were discussed. The challenge to build a gold standard reference genome sequence of bread wheat is now achieved and several de novo reference sequences from the cultivars representing different gene pools will be available soon. New pan-genome sequencing resources of wheat will strengthen the foundation required for accelerated gene discovery and provide more opportunities to practice the knowledge-based breeding. [ABSTRACT FROM AUTHOR]

Key message: Genetic diversity, population structure, LD decay, and selective sweeps in 687 wheat accessions were analyzed, providing relevant guidelines to facilitate the use of the germplasm in wheat breeding. Common wheat (Triticum aestivum L.) is one of the most widely grown crops in the world. Landraces were subjected to strong human-mediated selection in developing high-yielding, good quality, and widely adapted cultivars. To investigate the genome-wide patterns of allelic variation, population structure and patterns of selective sweeps during modern wheat breeding, we tested 687 wheat accessions, including landraces (148) and cultivars (539) mainly from China and Pakistan in a wheat 90 K single nucleotide polymorphism array. Population structure analysis revealed that cultivars and landraces from China and Pakistan comprised three relatively independent genetic clusters. Cultivars displayed lower nucleotide diversity and a wider average LD decay across whole genome, indicating allelic erosion and a diversity bottleneck due to the modern breeding. Analysis of genetic differentiation between landraces and cultivars from China and Pakistan identified allelic variants subjected to selection during modern breeding. In total, 477 unique genome regions showed signatures of selection, where 109 were identified in both China and Pakistan germplasm. The majority of genomic regions were located in the B genome (225), followed by the A genome (175), and only 77 regions were located in the D genome. EigenGWAS was further used to identify key selection loci in modern wheat cultivars from China and Pakistan by comparing with global winter wheat and spring wheat diversity panels, respectively. A few known functional genes or loci found within these genome regions corresponded to known phenotypes for disease resistance, vernalization, quality, adaptability and yield-related traits. This study uncovered molecular footprints of modern wheat breeding and explained the genetic basis of polygenic adaptation in wheat. The results will be useful for understanding targets of modern wheat breeding, and in devising future breeding strategies to target beneficial alleles currently not pursued. [ABSTRACT FROM AUTHOR]

Key message: Recent technological advances in wheat genomics provide new opportunities to uncover genetic variation in traits of breeding interest and enable genome-based breeding to deliver wheat cultivars for the projected food requirements for 2050. There has been tremendous progress in development of whole-genome sequencing resources in wheat and its progenitor species during the last 5 years. High-throughput genotyping is now possible in wheat not only for routine gene introgression but also for high-density genome-wide genotyping. This is a major transition phase to enable genome-based breeding to achieve progressive genetic gains to parallel to projected wheat production demands. These advances have intrigued wheat researchers to practice less pursued analytical approaches which were not practiced due to the short history of genome sequence availability. Such approaches have been successful in gene discovery and breeding applications in other crops and animals for which genome sequences have been available for much longer. These strategies include, (i) environmental genome-wide association studies in wheat genetic resources stored in genbanks to identify genes for local adaptation by using agroclimatic traits as phenotypes, (ii) haplotype-based analyses to improve the statistical power and resolution of genomic selection and gene mapping experiments, (iii) new breeding strategies for genome-based prediction of heterosis patterns in wheat, and (iv) ultimate use of genomics information to develop more efficient and robust genome-wide genotyping platforms to precisely predict higher yield potential and stability with greater precision. Genome-based breeding has potential to achieve the ultimate objective of ensuring sustainable wheat production through developing high yielding, climate-resilient wheat cultivars with high nutritional quality. [ABSTRACT FROM AUTHOR]

Abstract 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 Bx7 OE (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 Bx7 OE 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 Bx7 OE 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 Bx7 OE 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. Highlights • High-throughput KASP markers were developed for three important quality genes, Bx7 OE , wbm and 1BL.1RS in bread wheat. • Significant allelic effects were identified in a diversity panel. • Screening global landraces and cultivar collections identified candidates with superior alleles. • The use of KASP markers could help to fine tune bread-making quality in wheat breeding. [ABSTRACT FROM AUTHOR]

Anomalies in soil radon (222Rn) and thoron (220Rn) time series data (TSD) have been reported in many studies prior to earthquakes, suggesting both radioisotopes may serve as earthquake precursors. In the present study, periodic oscillations are being observed in measured time series of soil radon, thoron, and meteorological parameters viz., pressure, temperature, and humidity. Each time series consists of 15,692 measurements taken over a 40-min sampling period. Singular spectrum analyses (SSA) have been used to identify and split the periodic and aperiodic components. Each of the time series has been tested for the normal distribution using Kolmogorov-Smirnov (K–S) test, the Anderson-Darling (A-D) test, and the Shapiro-Wilk (S–W) test. Results show the non-uniformity of the soil radon and thoron time series. Periodicities in each of the TSD have been studied using periodograms. Control charts have been drawn for radon and thoron time series data (TTSD) to check if any anomalous behavior exists in the time series. Anomalies in TSD have been analyzed for the seismic events that occurred during the study time. The finding of study show that TTSD exhibited unusual behavior prior to the occurrence of the earthquakes. • Anomalies in soil radon (222Rn) and thoron (220Rn) time series data (TSD) have been reported. • Periodic oscillations are being observed in measured time series of soil radon, thoron and meteorological parameters. • Singular spectrum analyses (SSA) have been used to identify and split the periodic and aperiodic components. • Finding of study shows that Thoron Time Series Data exhibited unusual behavior prior to the occurrence of the earthquakes. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum , is an economically devastating disease of wheat worldwide. Fhb1 , a widely used genetic source of FHB resistance, originated in East Asia. The recent cloning of Fhb1 opens a new avenue to improve FHB resistance in wheat and potentially other crops. [ABSTRACT FROM AUTHOR]

Background: Wheat genetic resources have been used for genetic improvement since 1876, when Stephen Wilson (Transactions and Proceedings of the Botanical Society of Edinburgh 12: 286) consciously made the first wide hybrid involving wheat and rye in Scotland. Wide crossing continued with sporadic attempts in the first half of 19th century and became a sophisticated scientific discipline during the last few decades with considerable impact in farmers' fields. However, a large diversity of untapped genetic resources could contribute in meeting future wheat production challenges. Perspectives and Conclusion: Recently the complete reference genome of hexaploid (Chinese Spring) and tetraploid (Triticum turgidum ssp. dicoccoides) wheat became publicly available coupled with on-going international efforts on wheat pan-genome sequencing. We anticipate that an objective appraisal is required in the post-genomics era to prioritize genetic resources for use in the improvement of wheat production if the goal of doubling yield by 2050 is to be met. Advances in genomics have resulted in the development of high-throughput genotyping arrays, improved and efficient methods of gene discovery, genomics-assisted selection and gene editing using endonucleases. Likewise, ongoing advances in rapid generation turnover, improved phenotyping, envirotyping and analytical methods will significantly accelerate exploitation of exotic genes and increase the rate of genetic gain in breeding. We argue that the integration of these advances will significantly improve the precision and targeted identification of potentially useful variation in the wild relatives of wheat, providing new opportunities to contribute to yield and quality improvement, tolerance to abiotic stresses, resistance to emerging biotic stresses and resilience to weather extremes. [ABSTRACT FROM AUTHOR]

Starch is a crucial component in wheat endosperm and plays an important role in processing quality. Endosperm of matured wheat grains contains two distinct starch granules (SG), referred to as larger A- and smaller B-granules. In the present study, 166 Chinese bread wheat cultivars planted in four environments were characterized for variation in SG size. A genome-wide association study (GWAS) using the 90 K SNP assay identified 23 loci for percentage volumes of A- and B-granules, and 25 loci for the ratio of A-/B-granules volumes, distributing on 15 chromosomes. Fifteen MTAs were associated with both the percentage volumes of A-, B-granules and the ratio of A-/B-granules volumes. MTAs IWB34623 and IWA3693 on chromosome 7A and IWB22624 and IWA4574 on chromosome 7B associated with the percentage volumes of A- and B-granules consistently identified in multiple environments were considered to be stable. Linear regression analysis showed a significantly negative correlation of the number of favorable alleles with the percentage volumes of A-granules and a significantly positive correlation between the number of favorable alleles and the percentage volumes of B-granules, respectively. The loci identified in this study and associated markers could provide basis for manipulating SG size to obtain superior noodle quality in wheat. [ABSTRACT FROM AUTHOR]

Key message: We identified 27 stable loci associated with agronomic traits in spring wheat using genome-wide association analysis, some of which confirmed previously reported studies. GWAS peaks identified in regions where no QTL for grain yield per se has been mapped to date, provide new opportunities for gene discovery and creation of new cultivars with desirable alleles for improving yield and yield stability in wheat. Abstract: We undertook large-scale genetic analysis to determine marker-trait associations (MTAs) underlying agronomic and physiological performance in spring wheat using genome-wide association studies (GWAS). Field trials were conducted at seven sites in three countries (Sudan, Egypt, and Syria) over 2-3 years in each country. Twenty-five agronomic and physiological traits were measured on 188 wheat genotypes. After correcting for population structure and relatedness, a total of 245 MTAs distributed over 66 loci were associated with agronomic traits in individual and mean performance across environments respectively; some of which confirmed previously reported loci. Of these, 27 loci were significantly associated with days to heading, thousand kernel weight, grain yield, spike length, and leaf rolling for mean performance across environments. Despite strong QTL by environment interactions, eight of the loci on chromosomes 1A, 1D, 5A, 5D, 6B, 7A, and 7B had pleiotropic effects on days to heading and yield components (TKW, SM, and SNS). The winter-type alleles at the homoeologous VRN1 loci significantly increased days to heading and grain yield in optimal environments, but decreased grain yield in heat prone environments. Top 20 high-yielding genotypes, ranked by additive main effects and multiplicative interaction (AMMI), had low kinship relationship and possessed 4-5 favorable alleles for GY MTAs except two genotypes, Shadi-4 and Qafzah-11/Bashiq-1-2. This indicated different yield stability mechanisms due to potentially favorable rare alleles that are uncharacterized. Our results will enable wheat breeders to effectively introgress several desirable alleles into locally adapted germplasm in developing wheat varieties with high yield stability and enhanced heat tolerance. [ABSTRACT FROM AUTHOR]

There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype–phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

Key message : We developed and validated a robust marker toolkit for high-throughput and cost-effective screening of a large number of functional genes in wheat. Abstract: Functional markers (FMs) are the most valuable markers for crop breeding programs, and high-throughput genotyping for FMs could provide an excellent opportunity to effectively practice marker-assisted selection while breeding cultivars. Here we developed and validated kompetitive allele-specific PCR (KASP) assays for genes that underpin economically important traits in bread wheat including adaptability, grain yield, quality, and biotic and abiotic stress resistances. In total, 70 KASP assays either developed in this study or obtained from public databases were validated for reliability in application. The validation of KASP assays were conducted by (a) comparing the assays with available gel-based PCR markers on 23 diverse wheat accessions, (b) validation of the derived allelic information using phenotypes of a panel comprised of 300 diverse cultivars from China and 13 other countries, and (c) additional testing, where possible, of the assays in four segregating populations. All KASP assays being reported were significantly associated with the relevant phenotypes in the cultivars panel and bi-parental populations, thus revealing potential application in wheat breeding programs. The results revealed 45 times superiority of the KASP assays in speed than gel-based PCR markers. KASP has recently emerged as single-plex high-throughput genotyping technology; this is the first report on high-throughput screening of a large number of functional genes in a major crop. Such assays could greatly accelerate the characterization of crossing parents and advanced lines for marker-assisted selection and can complement the inflexible, high-density SNP arrays. Our results offer a robust and reliable molecular marker toolkit that can contribute towards maximizing genetic gains in wheat breeding programs. [ABSTRACT FROM AUTHOR]

We investigated alleles at 31 loci associated with adaptability, yield, and end-use quality in 107 wheat (Triticum aestivum L.) landraces (WLRs) and 121 improved historical wheat cultivars (HWCs) from Pakistan. The WLRs were categorized into two further subgroups: 36 pre-Green-Revolution landraces released as cultivars and 71 geographically spread landraces from all over Pakistan. Alleles VrnA1a, TaGW2-6A-A, TaCKX6-D1b, Pinb-D1b, Psy-A1b, and Wx-D1b were absent in WLRs, whereas ample diversity was observed at all other loci. In HWCs, only Wx-D1b and Glu-A3e were absent among the alleles tested, whereas the alleles Ppd-D1a (90%), Rht-B1b or Rht-D1b (83.4%), TaCwi-A1a (95%), TaGW2-6A-G (76%), TaCKX6-D1a (77.3%), Glu-A1b (66.1%), Glu-D1d (61.3%), Pina-D1b (88.2%), Pinb-D1a (90%), Psy-A1a (66.1%), Psy-B1b (81.8%), Psy-D1a (86.5%), Ppo-A1a (70%), TaZds-D1b (73.9%), TaLox-B1b (80.1%), and Wx-D1a (100%) predominated, indicating significant improvement in adaptability, yield potential, and end-use quality and unconscious selection for favored alleles. Higher frequencies of favored alleles at the TaCwi-A1 and TaCKX6-D1 loci influencing 1000-kernel weight (TKW) in HWCs indicated that selection pressure on these alleles during breeding successfully contributed to cultivar improvement. Wright's pairwise fixation index (Fst) statistics indicated greater genetic divergence between HWC and WLR collections (0.16) than HWC and WLR cultivars (0.14). Population structure based on functional markers (FMs) using principal component analysis partitioned the germplasm into two distinct groups. High genetic divergence and low admixture between HWCs and WLRs indicated limited use of landraces in wheat breeding in Pakistan. Our results suggested these collections as rich reservoirs of alleles and haplotype combinations that may be useful in future breeding programs. [ABSTRACT FROM AUTHOR]

‘Speed breeding’ enables scientists to exploit gene bank accessions and mutant collections for an unparalleled rapid gene discovery and gene deployment. Combining speed breeding and other leading-edge plant breeding technologies with strategic global partnerships, has the potential to achieve the genetic gain targets required to deliver our future crops. [ABSTRACT FROM AUTHOR]

Use of genetic diversity from related wild and domesticated species has made a significant contribution to improving wheat productivity. Synthetic hexaploid wheats (SHWs) exhibit natural genetic variation for resistance and/or tolerance to biotic and abiotic stresses. Stripe rust caused by (Puccinia striiformis f. sp. tritici; Pst), is an important disease of wheat worldwide. To characterise loci conferring resistance to stripe rust in SHWs, we conducted a genome-wide association study (GWAS) with a panel of 181 SHWs using the wheat 9K SNP iSelect array. The SHWs were evaluated for their response to the prevailing races of Pst at the seedling and adult plant stages, the latter in replicated field trials at two sites in Ethiopia in 2011. About 28% of the SHWs exhibited immunity at the seedling stage while 56% and 83% were resistant to Pst at the adult plant stage at Meraro and Arsi Robe, respectively. A total of 27 SNPs in nine genomic regions (1BS, 2AS, 2BL, 3BL, 3DL, 5A, 5BL, 6DS and 7A) were linked with resistance to Pst at the seedling stage, while 38 SNPs on 18 genomic regions were associated with resistance at the adult plant stage. Six genomic regions were commonly detected at both locations using a mixed linear model corrected for population structure, kinship relatedness and adjusted for false discovery rate (FDR). The loci on chromosome regions 1AS, 3DL, 6DS and 7AL appeared to be novel QTL; our results confirm that resynthesized wheat involving its progenitor species is a rich source of new stripe (yellow) rust resistance that may be useful in choosing SHWs and incorporating diverse yellow rust (YR) resistance loci into locally adapted wheat cultivars. [ABSTRACT FROM AUTHOR]

Abstract: Wheat seed storage proteins, especially glutenins and gliadins, have unique functional properties giving rise to a wide array of food products for human consumption. The wheat seed storage proteins, however, are also the most common cause of food-related allergies and intolerances, and it has become crucially important to understand their composition, variation and functional properties and interface this knowledge with the grain handling industry as well as the breeders. This review focuses on advances in understanding the genetics and function of storage proteins and their application in wheat breeding programs. These include: (1) The development and validation of high-throughput molecular marker systems for defining the composition and variation of low molecular weight glutenin subunits (LMW-GS) genes and a summary of the more than 30 gene-specific markers for rapid screening in wheat breeding programs; (2) The identification of more than 100 alleles of storage proteins in wild species provide candidate genes for future quality improvement; (3) The documentation of quality effects of individual LMW-GS and HMW-GS for improving end-use quality; and (4) The analysis of α-gliadin genes on chromosomes 6A and 6D with non-toxic epitopes as potential targets to develop less toxic cultivars for people with celiac disease. Genomic and proteomic technologies that will continue to provide new tools for understanding variation and function of seed storage proteins in wheat are discussed. [Copyright &y& Elsevier]

Background Grain size and shape greatly influence grain weight which ultimately enhances grain yield in wheat. Digital imaging (DI) based phenomic characterization can capture the three dimensional variation in grain size and shape than has hitherto been possible. In this study, we report the results from using digital imaging of grain size and shape to understand the relationship among different components of this trait, their contribution to enhance grain weight, and to identify genomic regions (QTLs) controlling grain morphology using genome wide association mapping with high density diversity array technology (DArT) and allele-specific markers. Results Significant positive correlations were observed between grain weight and grain size measurements such as grain length (r = 0.43), width, thickness (r = 0.64) and factor from density (FFD) (r = 0.69). A total of 231 synthetic hexaploid wheats (SHWs) were grouped into five different sub-clusters by Bayesian structure analysis using unlinked DArT markers. Linkage disequilibrium (LD) decay was observed among DArT loci > 10 cM distance and approximately 28% marker pairs were in significant LD. In total, 197 loci over 60 chromosomal regions and 79 loci over 31 chromosomal regions were associated with grain morphology by genome wide analysis using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. They were mainly distributed on homoeologous group 2, 3, 6 and 7 chromosomes. Twenty eight marker-trait associations (MTAs) on the D genome chromosomes 2D, 3D and 6D may carry novel alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Statistical simulations showed that favorable alleles for thousand kernel weight (TKW), grain length, width and thickness have additive genetic effects. Allelic variations for known genes controlling grain size and weight, viz. TaCwi-2A, TaSus-2B, TaCKX6-3D and TaGw2-6A, were also associated with TKW, grain width and thickness. In silico functional analysis predicted a range of biological functions for 32 DArT loci and receptor like kinase, known to affect plant development, appeared to be common protein family encoded by several loci responsible for grain size and shape. Conclusion Conclusively, we demonstrated the application and integration of multiple approaches including high throughput phenotyping using DI, genome wide association studies (GWAS) and in silico functional analysis of candidate loci to analyze target traits, and identify candidate genomic regions underlying these traits. These approaches provided great opportunity to understand the breeding value of SHWs for improving grain weight and enhanced our deep understanding on molecular genetics of grain weight in wheat. [ABSTRACT FROM AUTHOR]

Stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici has been an important disease of wheat in the Indian subcontinent since 1786. Currently, it prevails across all the wheat growing areas from north to south in the country. Due to the favourable weather conditions, the northern uplands have been historically hit by the severe disease epidemics. These epidemics caused significant losses to national wheat production. Acquisition of broader virulence pattern by the pathogen poses a serious threat to national agriculture. Although the deployed national wheat varieties have adequate resistance, these are developed around few major genes and are vulnerable to the new evolving strains of the pathogen. Utilisation of race non-specific durable resistance and seedling resistance via gene pyramiding, based on the current virulence scenario of the pathogen should provide sustainable control. This review focuses on the national milestones that recognise the economic significance of the disease and current status of stripe rust and its management in Pakistan. [ABSTRACT FROM AUTHOR]

Triticum urartu possesses the Au genome common to bread wheat. Similarly, Triticum monococcum contains the Am genome, which is closely related to the A-genome donor of bread wheat. Aegilops speltoides of the Sitopsis section has the S genome, which is most similar to the B genome of bread and durum wheat when compared with all other wild grasses. Amphiploids developed through bridge crossing between Am/Au and S-genome diploid resources and elite durum cultivars demonstrate enormous diversity to improve both bread and durum wheat cultivars. We evaluated such A-genome amphiploids (Triticum turgidum × T. urartu and T. turgidum × T. monococcum, 2n = 6x = 42; BBAAAmAm/AuAu) and S-genome amphiploids (T. turgidum × Ae. speltoides, 2n = 6x = 42; AABBSS) along with their durum parents (AABB) for their resistance to powdery mildew (PM) at the seedling stage. The results indicated that 104 accessions (53.6%) of A-genome amphiploids (AABBAmAm/AuAu) were resistant to PM at the seedling stage. Of their 24 durum parents, five (20.83%) were resistant to PM and 16 (66.6%) were moderately tolerant. Similarly, ten (50%) accessions of S-genome amphiploids (BBAASS) possessed seedling PM resistance, suggesting a valuable source of major resistance genes. PM screening of the amphiploids and parental durum lines showed that resistance was contributed either by the diploid progenitors or durum parents, or both. We also observed the suppression of resistance in several cases; for example, resistance in durum wheat was suppressed in respective amphiploids. The results from this germplasm screening will facilitate their utilization to genetically control PM and widen the genetic base of wheat. [ABSTRACT FROM PUBLISHER]

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms. The application of deep learning to plant breeding is an active area of research. This work reports a new method, DNNGP, for prediction of quantitative traits from multi-omics data in the context of genomic selection in plants. DNNGP performs as well as or better than several commonly used methods in a broad range of tasks. The insights gained through the development, testing, and application of DNNGP can benefit genomics applications of deep learning in the future. [ABSTRACT FROM AUTHOR]

Characterization of high-molecular-weight (HMW) glutenins is an important criterion for identifying genotypes with good bread-making quality. In synthetic hexaploids (SHs), the D-genome encodes several allelic variants of HMW glutenins that require proper identification prior to their utilization for bread wheat (BW) improvement. In this study, SHs with promising agronomic features were characterized for HMW glutenin composition. Seven different allelic variants were observed at the Glu-Dt1 locus, three of which (1Dx1.5+1Dy10, 1Dx1.5+1Dy12.2 and 1Dx2.1+1Dy10) have not been previously reported in existing BW germplasm. The results also showed a variety of D-genome-encoded subunits along with superior glutenin alleles in the B-genome (1Bx7+1By8, 1Bx6+1By8 and 1Bx13+1By16). About 63% of these SHs encoded favourable allelic variants of HMW glutenins, which make them a good choice for improvement in wheat bread making. Glu-Dt1 encoded favourable allelic variants (1Dx5+1Dy10 and 1Dx1.5+1Dy10) that are frequently observed in SHs can be easily incorporated into BW through recombination breeding. [ABSTRACT FROM AUTHOR]

Key message: Three stable QTL for grain zinc concentration were identified in wheat landrace Chinese Spring. Favorable alleles were more frequent in landraces than in modern wheat cultivars. Wheat is a major source of dietary energy for the growing world population. Developing cultivars with enriched zinc and iron can potentially alleviate human micronutrient deficiency. In this study, a recombinant inbred line (RIL) population with 245 lines derived from cross Zhou 8425B/Chinese Spring was used to detect quantitative trait loci (QTL) for grain zinc concentration (GZnC) and grain iron concentration (GFeC) across four environments. Three stable QTL for GZnC with all favorable alleles from Chinese Spring were identified on chromosomes 3BL, 5AL, and 5BL. These QTL explaining maxima of 8.7%, 5.8%, and 7.1% of phenotypic variances were validated in 125 resequenced wheat accessions encompassing both landraces and modern cultivars using six kompetitive allele specific PCR (KASP) assays. The frequencies of favorable alleles for QGZnCzc.caas-3BL, QGZnCzc.caas-5AL and QGZnCzc.caas-5BL were higher in landraces (90.4%, 68.0%, and 100.0%, respectively) compared to modern cultivars (45.9%, 35.4%, and 40.9%), suggesting they were not selected in breeding programs. Candidate gene association studies on GZnC in the cultivar panel further delimited the QTL into 8.5 Mb, 4.1 Mb, and 47.8 Mb regions containing 46, 4, and 199 candidate genes, respectively. The 5BL QTL located in a region where recombination was suppressed. Two stable and three less stable QTL for GFeC with favorable alleles also from Chinese Spring were identified on chromosomes 4BS (Rht-B1a), 4DS (Rht-D1a), 1DS, 3AS, and 6DS. This study sheds light on the genetic basis of GZnC and GFeC in Chinese Spring and provides useful molecular markers for wheat biofortification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Achieving high yield and good quality in crops is essential for human food security and health. However, there is usually disharmony between yield and quality. Seed storage protein (SSP) and starch, the predominant components in cereal grains, determine yield and quality, and their coupled synthesis causes a yield–quality trade‐off. Therefore, dissection of the underlying regulatory mechanism facilitates simultaneous improvement of yield and quality. Here, we summarize current findings about the synergistic molecular machinery underpinning SSP and starch synthesis in the leading staple cereal crops, including maize, rice and wheat. We further evaluate the functional conservation and differentiation of key regulators and specify feasible research approaches to identify additional regulators and expand insights. We also present major strategies to leverage resultant information for simultaneous improvement of yield and quality by molecular breeding. Finally, future perspectives on major challenges are proposed. [ABSTRACT FROM AUTHOR]

Roots are the main sensing organ, initiating multiple signaling pathways in response to abiotic factors, including nutrients, drought, and salt stress. A focus on improving the root system architecture is a key strategy to mitigate these stresses in wheat crop. In the present study, a diversity panel comprising indigenous landraces and historical cultivars from Pakistan was characterized for the root system architecture (RSA) and important loci were identified using a genome‐wide association study (GWAS). RSA of the diversity panel was characterized 30 days after sowing in brunch tubes, and root images were taken. A high‐throughput root imaging analysis using Rhizovision software was performed by setting the scale to extract the eight RSA traits and four plant biomass‐related traits. GWAS identified 323 association signals for 12 root and biomass traits present on all wheat chromosomes, while the most important and reliable genetic loci (based on pleotropic loci and candidate genes) were identified on chromosomes 2A, 2B, 5A, 5D, 6A, 7B, and 7D for RSA. SNP annotation and transcriptome profiling identified nine candidate genes regulating the RSA and plant biomass traits, including ROOTLESS WITH UNDETECTABLE MERISTEM1, MYB TRANSCRIPTION FACTOR4, BRASSINOSTEROID INSENSITIVE1, SLENDER RICE1, AUXIN‐RESPONSIVE FACTOR25, SCARECROW, NARROW LEAF2, PIN‐FORMED1 AND PHOSPHATE TRANSCRIPTION FACTOR1. This study provided pre‐breeding information for deep‐rooting genotypes and associated markers that will accelerate the incorporation of such traits in breeding. [ABSTRACT FROM AUTHOR]

The Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) was established in 2000, forming a collaboration between breeding and research programs through biannual yield trials. A core set of 142 genotypes from 15 breeding programs was selected, genotyped for 81 DNA functional markers and phenotyped for 10 agronomic traits at three sites in Kazakhstan (Karabalyk, Shortandy and Shagalaly) and one site in Russia (Omsk) in 2020–2022. The study aim was to identify markers demonstrating significant effects on agronomic traits. The average grain yield of individual trials varied from 118 to 569 g/m2. Grain yield was positively associated with the number of days to heading, plant height, number of grains per spike and 1000-kernel weight. Eight DNA markers demonstrated significant effects. The spring-type allele of the Vrn-A1 gene accelerated heading by two days (5.6%) and was present in 80% of the germplasm. The winter allele of the Vrn-A1 gene significantly increased grain yield by 2.7%. The late allele of the earliness marker per se, TaMOT1-D1, delayed development by 1.9% and increased yield by 4.5%. Translocation of 1B.1R was present in 21.8% of the material, which resulted in a 6.2% yield advantage compared to 1B.1B germplasm and a reduction in stem rust severity from 27.6 to 6.6%. The favorable allele of TaGS-D1 increased both kernel weight and yield by 2–3%. Four markers identified in ICARDA germplasm, ISBW2-GY (Kukri_c3243_1065, 3B), ISBW3-BM (TA004946-0577, 1B), ISBW10-SM2 (BS00076246_51, 5A), ISBW11-GY (wsnp_Ex_c12812_20324622, 4A), showed an improved yield in this study of 3–4%. The study recommends simultaneous validation and use of selected markers in KASIB's network. [ABSTRACT FROM AUTHOR]

Background: Durum wheat is one of the most important crops, especially in the Mediterranean region. Insight into the genetic diversity of germplasm can improve the breeding program management in various traits. This study was done using single nucleotide polymorphisms (SNP) markers to characterize the genetic distinctiveness and differentiation of tetraploid wheat landraces collected from nine European and Asian countries. A sum of 23,334 polymorphic SNPs was detected in 126 tetraploid wheat landraces in relation to the reference genome. Results: The number of identified SNPs was 11,613 and 11,721 in A and B genomes, respectively. The highest and lowest diversity was on 6B and 6 A chromosomes, respectively. Structure analysis classified the landraces into two distinct subpopulations (K = 2). Evaluating the principal coordinate analysis (PCoA) and weighted pair-group method using arithmetic averages (WPGMA) clustering results demonstrated that landraces (99.2%) are categorized into one of the two chief subpopulations. Therefore, the grouping pattern did not clearly show the presence of a clear pattern of relationships between genetic diversity and their geographical derivation. Part of this result could be due to the historical exchange between different germplasms. Although the result did not separate landraces based on their region of origin, the landraces collected from Iran were classified into the same group and cluster. Analysis of molecular variance (AMOVA) also confirmed the results of population structure. Finally, Durum wheat landraces in some countries, including Turkey, Russia, Ukraine, and Afghanistan, were highly diverse, while others, including Iran and China, were low-diversity. Conclusion: The recent study concluded that the 126 tetraploid wheat genotypes and their GBS-SNP markers are very appropriate for quantitative trait loci (QTLs) mapping and genome-wide association studies (GWAS). The core collection comprises two distinct subpopulations. Subpopulation II genotypes are the most diverse genotypes, and if they possess desired traits, they may be used in future breeding programs. The degree of diversity in the landraces of countries can provide the ground for the improvement of new cultivars with international cooperation. linkage disequilibrium (LD) hotspot distribution across the genome was investigated, which provides useful information about the genomic regions that contain intriguing genes. [ABSTRACT FROM AUTHOR]

Sustainable wheat production is challenged by climate change events such as drought, salinity, and heat stress. Senescence is a gradual programmed cell death trait occurring post anthesis in wheat (Triticum aestivum), with significant affecting stability of yield and quality‐related traits under climate severities. Phenotyping of complex traits is increasingly perceived as a bottleneck due to elevated labor costs and time in large field conditions. Unmanned aerial vehicle (UAV)‐based platforms using RGB imagery and ground phenotyping–based novel traits can facilitate the repeated nondestructive measurements of crop canopy traits cost‐effectively. Here, we described combined application of UAV‐based RGB imaging and ground measurements based novel trait to quantify canopy senescence in wheat. We reported senescence related traits with high heritability in a recombinant inbred line population derived from the cross Zhongmai 175/Lunxuan 987. Our results showed that the selection of slow senescence genotypes using UAV‐based vegetation indices (VIs) was equally effective as ground‐based traits and illustrated significant variations among the genotypes. We also identified five quantitative trait loci (QTL) for canopy senescence in both UAV and ground‐based datasets using a 50K single‐nucleotide polymorphism array. QTL for UAV‐based VIs from RGB imaging and ground measurements based traits were mapped on chromosomes 1B, 2B, 3A, and 4B. The integration of both datasets with genetic analysis identified an important slow senescence/stay‐green related locus on chromosome 4B that explained phenotypic variation up to 23.07% for both UAV and ground traits at late grain‐ filling stage. Three QTL (QTL‐caas.1B, QTL‐caas.3A, and QTL‐caas.4B2) on chromosomes 1B, 3A, and 4B were also validated for slow senescence with high chlorophyll level and cool canopy temperature in a natural population of 160 lines by developing kompetitive allele‐specific PCR markers. QTL‐caas.4B1 was validated not only for senescence but also for plant height. Our findings suggest that UAV‐based RGB imagery is advantageous for precise and rapid assessment of senescence related traits and genetic studies of senescence in wheat. Core Ideas: Unmanned aerial vehicle (UAV)‐based phenotyping platforms using RGB imagery can facilitate the repeated nondestructive measurements of canopy greenness and senescence cost‐effectively.We have integrated vegetation indices derived from UAV‐based RGB pixels and ground measurements with quantitative genetic analysis to identify loci controlling canopy senescence in wheat.We have developed kompetitive allele‐specific PCR (KASP) markers based on identified loci that contribute to variations in wheat senescence. [ABSTRACT FROM AUTHOR]

Key message: Major QTL for grain zinc and iron concentrations were identified on the long arm of chromosomes 2D and 6D. Gene-based KASP markers were developed for putative candidate genes TaIPK1-2D and TaNAS10-6D. Micronutrient malnutrition is one of the most common public health problems in the world. Biofortification, the most attractive and sustainable solution to surmount malnutrition requires the development of micronutrient enriched new crop cultivars. In this study, two recombinant inbred line (RIL) populations, ZM175/XY60 and ZM175/LX987, were used to identify QTL for grain zinc concentration (GZnC), grain iron concentration (GFeC) and thousand grain weight (TGW). Eight QTL for GZnC, six QTL for GFeC and five QTL for TGW were detected. Three QTL on chromosomes 2DL and 4BS and chromosome 6A showed pleiotropic effects on all three traits. The 4BS and 6A QTL also increased plant height and might be Rht-B1a and Rht25a, respectively. The 2DL locus within a suppressed recombination region was identified in both RIL populations and the favorable allele simultaneously increasing GZnC, GFeC and TGW was contributed by XY60 and LX987. A QTL on chromosome 6DL associated only with GZnC was detected in ZM175/XY60 and was validated in JD8/AK58 RILs using kompetitive allele-specific PCR (KASP) marker K_AX-110119937. Both the 2DL and 6DL QTL were new loci for GZnC. Based on gene annotations, sequence variations and expression profiles, the phytic acid biosynthesis gene TaIPK1-2D and nicotianamine synthase gene TaNAS10-6D were predicted as candidate genes. Their gene-based KASP markers were developed and validated in a cultivar panel of 343 wheat accessions. This study investigated the genetic basis of GZnC and GFeC and provided valuable candidate genes and markers for breeding Zn- and Fe-enriched wheat. [ABSTRACT FROM AUTHOR]

Understanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high‐quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom‐up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large‐scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding. [ABSTRACT FROM AUTHOR]

The wheat gene pool has a tremendous amount of genetic diversity for salinity tolerance. During the last few decades, several wheat genetic stocks have been developed showing all three types of tolerance mechanisms, i.e., tissue tolerance, osmotic tolerance and ion (Na+) exclusion. However, delivery of improved crop varieties adapted to saline conditions has been lagging behind due to several reasons including the huge knowledge gap in understanding genetic basis of salinity tolerance in wheat, and then applying the available knowledge to deliver salt-resilient crop varieties. We review the research around salinity tolerance in wheat in context of historical and rapidly evolving breeding technologies and discuss the future prospects. The extensive research on identifying promising resources of salinity tolerance in durum wheat, synthetic hexaploid wheats and tertiary gene pool species such as those of Thinopyrum have been explored to transfer salinity tolerance traits to bread wheat. As the last few years witnessed leading-edge transformations where we have now (i) new and improved genotyping assays in form of SNP arrays and next-generation sequencing to facilitate gene discovery, (ii) new generation turn-over methods to get five to six generations per year by "speed breeding" facilitating gene deployment, (iii) gene-editing tools to precisely manipulate the effects of causal genes, and (iv) new phenomic platforms for capturing salinity effects in field and glass-house conditions. Integration of all these technologies will help in understanding the complex genetic architecture of wheat adaptability in saline soils and will accelerate the delivery of our future potential wheat cultivars. [ABSTRACT FROM AUTHOR]

Highlights • An unmanned aerial vehicle (UAV) was optimized and used for non-destructive high-throughput phenotyping of NDVI. • UAV-NDVI measurements were highly consistent with ground data captured by a handheld Greenseeker. • UAV-NDVI explained significant variations in biomass and grain yield. • UAV-NDVI was accurate and can be used for selection of high yielding genotypes during grain-filling stages in large breeding programs. Abstract Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection. [ABSTRACT FROM AUTHOR]

Seedling emergence is the preliminary factor defining wheat adaptability and stability under salt stress. This study was led to assess the salinity tolerance amongst 226 synthetic hexaploid wheats (SHWs) evaluated against two check cultivars, the tolerant ‘S-24’ and the susceptible ‘PBW-343’ at three sodium chloride treatments (0, 100 and 200 mM). Highly significant and positive correlation was observed between germination % and germination index (r = 0.85), and between seedling height and weight (r = 0.85). All four traits across three treatments were transformed into the salt tolerance trait index and salt tolerance index (STI). STI had significant positive correlation with all four parameters indicating reliability of this index for ranking the tolerance levels. STI-based 20 best performing genotypes were known as being promising candidates for wheat breeding. Local tolerant check was amongst the top three tolerant accessions. Two SHWs, AUS30288 {Croc_1/Aegilops squarrosa (466)} and AUS34444 {Ceta/Ae. squarrosa (872)} outperformed S-24 with STI of 61.8 and 55.7, respectively. SHW with same durum parents were included in tolerant and susceptible categories indicating that tolerance is contributed by the Ae. squarrosa syn. tauschii parent of SHWs. In conclusion, this baseline study revealed that continuous variation in the seedling emergence traits under salt stress is a conduit towards implementing genome-wide association studies. Likewise, new diversity has implications in development of salt tolerance germplasm after genetic dissection permitting unique Ae. squarrosa accessional diversity validation to target SHW donors for breeding. [ABSTRACT FROM AUTHOR]

Summary: Wheat fixes CO2 by photosynthesis into kernels to nourish humankind. Improving the photosynthesis rate is a major driving force in assimilating atmospheric CO2 and guaranteeing food supply for human beings. Strategies for achieving the above goal need to be improved. Here, we report the cloning and mechanism of CO2ASSIMILATION RATE AND KERNEL‐ENHANCED 1 (CAKE1) from durum wheat (Triticum turgidum L. var. durum). The cake1 mutant displayed a lower photosynthesis rate with smaller grains. Genetic studies identified CAKE1 as HSP90.2‐B, encoding cytosolic molecular chaperone folding nascent preproteins. The disturbance of HSP90.2 decreased leaf photosynthesis rate, kernel weight (KW) and yield. Nevertheless, HSP90.2 over‐expression increased KW. HSP90.2 recruited and was essential for the chloroplast localization of nuclear‐encoded photosynthesis units, for example PsbO. Actin microfilaments docked on the chloroplast surface interacted with HSP90.2 as a subcellular track towards chloroplasts. A natural variation in the hexaploid wheat HSP90.2‐B promoter increased its transcription activity, enhanced photosynthesis rate and improved KW and yield. Our study illustrated an HSP90.2–Actin complex sorting client preproteins towards chloroplasts to promote CO2 assimilation and crop production. The beneficial haplotype of Hsp90.2 is rare in modern varieties and could be an excellent molecular switch promoting photosynthesis rate to increase yield in future elite wheat varieties. [ABSTRACT FROM AUTHOR]

Common wheat is a major source of nutrition around the globe, but unlike maize and rice hybrids, no breakthrough has been made to enhance wheat yield since Green Revolution. With the availability of reference genome sequence of wheat and advancement of allied genomics technologies, understanding of genes involved in grain yield components and disease resistance/susceptibility has opened new avenues for crop improvement. Wheat has a huge hexaploidy genome of approximately 17 GB with 85% repetition, and it is a daunting task to induce any mutation across three homeologues that can be helpful for the enhancement of agronomic traits. The CRISPR-Cas9 system provides a promising platform for genome editing in a site-specific manner. In wheat, CRISPR-Cas9 is being used in the improvement of yield, grain quality, biofortification, resistance against diseases, and tolerance against abiotic factors. The promising outcomes of the CRISPR-based multiplexing approach circumvent the constraint of targeting merely one gene at a time. Deployment of clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) 9 endonuclease (CRISPR-Cas9) and Cas9 variant systems such as cytidine base editing, adenosine base editing, and prime editing in wheat has been used to induce point mutations more precisely. Scientists have acquired major events such as induction of male sterility, fertility restoration, and alteration of seed dormancy through Cas9 in wheat that can facilitate breeding programs for elite variety development. Furthermore, a recent discovery in tissue culturing enables scientists to significantly enhance regeneration efficiency in wheat by transforming the GRF4-GIF1 cassette. Rapid generation advancement by speed breeding technology provides the opportunity for the generation advancement of the desired plants to segregate out unwanted transgenes and allows rapid integration of gene-edited wheat into the breeding pipeline. The combination of these novel technologies addresses some of the most important limiting factors for sustainable and climate-smart wheat that should lead to the second "Green Revolution" for global food security. [ABSTRACT FROM AUTHOR]

We evaluated root system architecture (RSA) of a set of 58 historical spring wheat cultivars from Pakistan representing 105 years of selection breeding. The evaluations were carried out under control and water-limited conditions using a high-throughput phenotyping system coupled with RhizoVision Explorer software. The cultivars were classified into three groups based on release year as cultivars released pre-1965, released between 1965 and 2000, and cultivars released post-2000. Under water-limited conditions a decline in 20 out of 25 RSA component traits was observed in pre-1965 cultivars group. Whereas cultivars released after the 1965, so-called green revolution period, showed a decline in 17 traits with significant increments in root length, depth, and steep angle frequency which are important root traits for resource-uptake under water-limited conditions. Similarly, cultivars released after 2000 indicated an increase in the number of roots, depth, diameter, surface area, and steep angle frequency. The coefficient of correlation analysis showed a positive correlation between root depth and yield-related traits under water-limited conditions. We also investigated the effects of green-revolution genes (Rht1) and some phenology-related genes such as DRO1, TaMOR, TaLTPs, TaSus-2B on RSA and identified significant associations of these genes with important root traits. There was strong selection pressure on DRO1 gene in cultivated wheat indicating the allele fixed in modern wheat cultivars is different from landraces. The expression of DRO1, and TaMOR were retrieved from an RNAseq experiment, and results were validated using qRT-PCR. The highest expression of DRO1 and TaMOR was found in Chakwal-50, a rainfed cultivar released in 2008, and MaxiPak-65 released in 1965. We conclude that there is a positive historic change in RSA after 1965 that might be attributed to genetic factors associated with favored RSA traits. Furthermore, we suggest root depth and steep angle as promising traits to withstand water-limited environments and may have implications in selection for breeding. [ABSTRACT FROM AUTHOR]

Spike number (SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks (Faster R-CNN) on Red-Green-Blue (RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660K SNP array. Analysis of manual spike number (MSN) in the field, image-based spike number (ISN), and verification of spike number (VSN) by Faster R-CNN revealed significant variation (P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination (R²) between ISN and VSN was 0.83, which was higher than that between ISN and MSN (R² = 0.51), and between VSN and MSN (R² = 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL Qsnyz.caas-4DS, Qsnyz.caas-7DS, and QSnyz.caas-7DL were identified based on MSN, ISN and VSN data, while QSnyz.caas-7DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat. [ABSTRACT FROM AUTHOR]

A high grain zinc wheat cultivar, Zincol-2016, was released in Pakistan to mitigate mineral malnutrition. Here, we used RNAseq based approach to reveal transcriptional landscape of its developing grains, and to identify genetic variants in zinc (Zn) and iron (Fe) homeostasis genes. Differential gene expression analysis identified 8370 differentially expressed genes (DEGs) over the course of grain filling. The profiling of 265 Fe/Zn homeostasis genes revealed the homeologs of TaNAS9 , TaFER1, TaNAAT2 , TaDMAS, TaZIFL5-A , TaZIFL5-D , TaZIP14-B , TaNRAMP3 , TaVIT1, TaPRI2-D , TabZIPF1-7B , TabZIPF1-7D , TabZIPF2-5A , and TabZIPF2-5B had increased expression in Zincol-2016 relative to other cultivars from expVIP database. The variant calling identified 358 SNPs and 34 InDels in the Fe/Zn homeostasis genes. Two of those SNPs in TaZIP14-B (TraesCS3B02G140400), and TaNAAT2-B (TraesCS1B02G300600), were associated with grain zinc and iron concentrations (GZnC and GFeC) in a diverse panel of 145 wheat cultivars. The Zincol-type alleles of both genes significantly increased GZnC and GFeC by 7.3–8.6% and 5.9–6.3%, respectively. In addition, 8 SNPs causing missense mutation and 2 InDels causing frameshift mutation were found unique to Zincol. The present study forms a basis for understanding the genetic basis of high GZnC and GFeC in Zincol-2016 and can help efforts to biofortify other wheat varieties. [Display omitted] • RNAseq of Zincol-2016 developing grains during grain filling stage was conducted. • Increased expression of several Fe/Zn homeostasis genes was observed in Zincol. • In total 358 SNPs and 34 InDels in known Fe/Zn homeostasis genes were identified. • Comparison with variants data of other wheat cultivars identified 10 unique variants in the Fe/Zn homeostasis genes of Zincol. • Zincol-type alleles of TaZIP14-B and TaNAAT2-B increased grain iron and zinc contents. [ABSTRACT FROM AUTHOR]

Grain yield (GY) prediction for wheat based on canopy spectral reflectance can improve selection efficiency in breeding programs. Time-series spectral information from different growth stages such as flowering to maturity is considered to have high accuracy in predicting GY and combining this information from multiple growth stages could effectively improve prediction accuracy. For this, 207 wheat cultivars and breeding lines were grown in full and limited irrigation treatments, and their canopy spectral reflectance was measured at the flowering, early, middle, and late grain fill stages. The potential of temporal spectral information at multiple growth stages for GY prediction was evaluated by a new method based on stacking the multiple growth stages data. Twenty VIs derived from spectral reflectance were used as the input feature of a support vector regression (SVR) to predict GY at each growth stage. The predicted GY values at multiple growth stages were trained by multiple linear regression (MLR) to establish a second-level prediction model. Results suggested that the prediction accuracy (R2) of VIs data from single growth stages ranged from 0.60 to 0.66 and 0.35 to 0.42 in the full and limited irrigation treatments, respectively. The prediction accuracy was increased by an average of 0.06, 0.07, and 0.07 after stacking the VIs of two, three, and four growth stages, respectively, under full irrigation. Similarly, under limited irrigation, the prediction accuracy was increased by 0.03, 0.04, and 0.04 by stacking the VIs of two, three, and four growth stages, respectively. Stacking of VIs of multiple important growth stages can increase the accuracy of GY prediction and application of a stable stacking model could increase the usefulness of data obtained from different phenotyping platforms. [ABSTRACT FROM AUTHOR]

Key Message: A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r2) ranged between 0.57 and 0.74. The r2 values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat. [ABSTRACT FROM AUTHOR]

Kernel hardness determined by two tightly linked Puroindoline genes, Pina-D1 and Pinb-D1, located on chromosome 5DS define commercially important characteristics, uses, major grades and export markets of wheat. This study was conducted to characterize Pina-D1 and Pinb-D1 alleles, in fifteen synthetic hexaploid wheats (SHWs) and its relation with grain hardness. Additionally, in silico functional analyses of puroindoline-a protein was conducted for better understanding of their putative importance in grain quality. Six different Pina-D1 alleles were identified in the SHWs, of which three i.e. Pina - D1a , Pina - D1c and Pina - D1d were already known whereas the other three had new sequence polymorphisms and were designated as Pina-D1w , Pina-D1x and Pina-D1y . Three different Pinb-D1 alleles were identified which have been reported earlier and no novel sequence polymorphism was detected. It was concluded that despite some primary, secondary and 3D structure variations, ligand binding sites and disulfide bonds discrepancies, the main features of PINA, i.e. the tryptophan-rich domain, the cysteine backbone, the signal peptide and basic identity of the proteins were all conserved. In silico analysis showed that puroindolines having binding capacity with small parts of prolamins causing celiac disease of human, however their potential role is not obvious. Conclusively, the new Pina-D1 alleles with modest effect on grain hardness, and insight into their functional and structural characteristics are important findings and their putative role in celiac disease require further studies to validate. [ABSTRACT FROM AUTHOR]

SUMMARY: Roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this 'hidden half' provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image‐based approaches for high‐throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non‐invasive methods like X‐ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root‐based cereal breeding. The integrated use of pan‐genome resources, now available in most cereals, coupled with new in‐field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA. Significance Statement: We reviewed the current status of genetics underpinning root system architecture in cereals and presented a comprehensive genetic framework. We further provided a comparative analysis of the phenotyping methods, different root phenes and their interaction with the environment, and breeding applications. [ABSTRACT FROM AUTHOR]

Karnal bunt caused by Tilletia indica is a quarantine disease of wheat causing huge economic losses due to the ban on the import of bunted grains. This study was designed to characterize pathogenicity, aggressiveness and genetic diversity of 68 Tilletia indica isolates collected from different geographic regions of Pakistan. Forty-six isolates were tested for their pathogenicity on eight wheat varieties, out of which three were non-aggressive. The coefficient of infection (CI) ranged from 15.73% (PB-25) to 10% (PB-68, PB-60, and PB-43). The isolates collected from central Punjab showed higher infestation compared to other isolates. Among the wheat varieties used for the aggressiveness study, WL-711 showed susceptible reaction with 10.88% CI, while NIFA-Barsat, HD-29, Janbaz, Bakhtawar-92, Tatara, and AARI 2011 showed resistance to the highly resistant response. These isolates were amplified using 31 random amplified polymorphic DNA (RAPD) markers and 32 inter-simple sequence repeat (ISSR) markers for diversity analysis. The principal component analysis (PCA) and analysis of molecular variance (AMOVA) showed greater divergence among isolates collected from Punjab and Khyber Pakhtunkhwa (KPK), with a moderate level of admixture. The isolates from Faisalabad (Punjab) were more aggressive compared to isolates from KPK and were clearly separated based on PCA, indicating the significant genetic distance in the populations. Our findings will assist breeders and pathologists in better understanding the pathogenic variability in Tilletia indica and in subsequent disease management. [ABSTRACT FROM AUTHOR]

It is important to understand the genetic gain achieved through selection of key yield traits for planning future breeding strategies in developing high yielding wheat (Triticum aestivum L.) cultivars. The aim of this study was to characterize the genetic changes and genotype × environment (G×E) interaction by additive main effect and multiplicative interactions (AMMI) for morphological, physiological, and yield component traits under five environments using 24 wheat cultivars released from 1911 to 2016 in Pakistan. There was a significant increase in grain yield (9.03 kg ha−1 yr−1, 0.37%), and plant height was reduced linearly (−0.26 cm yr−1, −0.33%). The traits waxiness, leaf rolling, harvest index, spike length, and grains per spike significantly increased but the gain was only 0.16–0.2% per year. Analysis of variance revealed that genotype, environment, and G×E interaction were highly significant (P <.01) for all traits except relative chlorophyll content, biomass, days to maturity, and number of spikes. Gene‐specific markers identified the durable resistance gene Lr67/Yr46/Sr55/Pm46 in obsolete cultivars as early as 1911, whereas the photoperiod‐insensitive allele Ppd‐D1a and reduced height alleles Rht‐B1b and Rht‐D1b were present only in the post‐1965 cultivars. Diversity analysis based on a 50K single nucleotide polymorphism genotyping array clearly differentiated temporal patterns in 24 cultivars, which was correlated with the agronomic performance of the cultivars. This dataset provided detailed insight into the performance of historical wheat cultivars and could help in devising wheat breeding strategies to focus on the traits contributing to grain yield and have slower rate of genetic progress. Core Ideas: The genetic gain in morphology of Pakistani bread wheat cultivars over 105 yr areas was assessed.Leaf rolling, waxiness, and grains/spikes associated with genetic gains in grain yield were achieved by breeding.The performance and stability of yield and yield‐related traits was analyzed for the different cultivars. [ABSTRACT FROM AUTHOR]

Tajikistan is rich in genetic resources and one of a few countries where farmers still grow wheat (Triticum spp) landraces, which are important for production and breeding. This study undertook morphological description and agronomic and genomic characterization of wheat landraces collected in Tajikistan in 2013 and 2014 to develop opportunities for their use in breeding and on‐farm maintenance. In total, 60 wheat landraces were collected from 40 villages in 14 administrative districts and four regions. They were evaluated for agronomic traits in field trials in Turkey. The study identified six distinct regions where landraces are still grown. The villages growing the landraces were remote, with the distance to the nearest market being 30–100 km. The area allocated to wheat landraces varied from 0.01 to 2 ha. The main reason for maintaining the wheat landraces was their suitability for homemade bread and specific adaptation to high altitude. Overall, 68 distinct lines originating from landraces were identified using morphological and genomic descriptions. A core set of 30 lines was selected and field phenotyped under rain‐fed conditions. Several landraces (Joydori, Safedak, Surkhaki besuk, Shukhak, Surkh‐suk, and Kilaki bartang) that exceeded local check for grain yield and size were identified. Cluster analysis using single nucleotide polymorphism (SNP) array kinship matrix demonstrated relatedness between the landraces from different regions of Tajikistan and from neighboring regions of Uzbekistan. The value of this study for the international community is in conservation of rare and unique wheat landraces that could have been lost in the future. Core Ideas: Wheat landraces are cultivated in Tajikistan in mountains because of their adaptation and quality.Overall, 68 unique landraces were identified using morphological and genomic tools.Field evaluation of the core set of 30 landraces identified superior genotypes. [ABSTRACT FROM AUTHOR]

Popcorn (Zea mays L. var. Everta) is the most ancient type of cultivated maize. However, there is little known about the genetics of popping-related traits based on genotyping-by-sequencing (GBS) technology. Here, we characterized the phenotypic variation for seven popping-related traits in maize kernels among 526 CIMMYT inbred lines (CMLs). In total, 155 083 high-quality single nucleotide polymorphism (SNP) markers were identified by a GBS approach. Several trait-associated loci were detected by genome-wide association study for color, popping expansion volume, shape, pericarp, flotation index, floury/vitreous, and protein content, explaining a majority of the observed phenotypic variance, and these were validated by a diverse panel comprising 764 tropical landrace accessions. Sixty two of the identified loci were recognized to have undergone selection. On average, there was a 55.27% frequency for alleles that promote popping in CMLs. Our work not only pinpoints previously unknown loci for popping-related traits, but also reveals that many of these loci have undergone selection. Beyond establishing a new benchmark for the genetics of popcorn, our study provides a foundation for gene discovery and breeding. It also presents evidence to investigate the role of a gradual loss of popping ability as a by-product of diversification of culinary uses throughout the evolution of teosinte–to–modern maize. [ABSTRACT FROM AUTHOR]

Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) was established in 2000 to conduct multi-location cooperative yield trials for the exchange of breeding material and to evaluate agronomic traits such as grain yield and disease resistance. In 2016, aggregated KASIB data were analyzed and 120 cultivars and breeding lines demonstrating different degrees of resistance to leaf rust selected. They were tested for leaf rust and agronomic traits in Omsk, Russia; Almaty, Kazakhstan and Sakarya, Turkey in 2017–2019. This germplasm was grouped into four categories based on leaf rust reaction: 31 resistant (R), 27 moderately resistant (MR), 37 intermediate (M) and 25 moderately susceptible (MS) entries. Lr genes were postulated based on tests using pathotypes with known virulence profiles and molecular markers. The most frequent Lr genes alone or in combination were Lr10 (31 entries), Lr26 (24), Lr9 (23), Lr1 (18), Lr17 (11), Lr34 (11) and Lr19 (7). Genes Lr1, Lr9, Lr10 and Lr17 reduced the leaf rust severity to M or MS across environments. The presence of Lr26 alone or in combination with other major genes reduced the leaf rust severity to MR-M in Omsk and R in Sakarya. The only gene with the large effect on leaf rust was Lr19, which provided a high level of resistance at all sites both alone and in combination with Lr26. Leaf rust severity in 52 entries with adult plant resistance (APR) was compared to 68 entries possessing effective seedling resistance genes. The leaf rust severity in entries with APR compared to seedling resistance was similar at low infection pressure at Sakarya, 2018 and Omsk, 2019. However, at higher leaf rust infection pressure at Omsk in 2017 and 2018, Almaty in 2019, there was a clear advantage for the germplasm possessing major genes. High level of stem rust infection in Omsk in 2019 confirmed effectiveness of the Sr31/Lr26 gene. A set of 33 genotypes was selected combining resistance to leaf and stem rust with superior agronomic performance. [ABSTRACT FROM AUTHOR]