Search

Bread wheat (Triticum aestivum) is a globally dominant crop and major source of calories and proteins for the human diet. Compared with its wild ancestors, modern bread wheat shows lower genetic diversity, caused by polyploidisation, domestication and breeding bottlenecks 1,2 . Wild wheat relatives represent genetic reservoirs, and harbour diversity and beneficial alleles that have not been incorporated into bread wheat. Here we establish and analyse extensive genome resources for Tausch's goatgrass (Aegilops tauschii), the donor of the bread wheat D genome. Our analysis of 46 Ae. tauschii genomes enabled us to clone a disease resistance gene and perform haplotype analysis across a complex disease resistance locus, allowing us to discern alleles from paralogous gene copies. We also reveal the complex genetic composition and history of the bread wheat D genome, which involves contributions from genetically and geographically discrete Ae. tauschii subpopulations. Together, our results reveal the complex history of the bread wheat D genome and demonstrate the potential of wild relatives in crop improvement., (© 2024. The Author(s).)

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. The genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored., Methods: In this study, hybrid of elite cultivars of wheat were developed via reciprocal crosses (cytoplasm swapping) and phenotypically evaluated. We assessed differential expression profiles of yield-related negative regulators in these cultivars and their F1 hybrids and identified various cis-regulatory signatures by employing bioinformatics tools. Furthermore, the preferential expression patterns of the syntenic triads encoded from A, B, and D subgenomes were assessed to decipher their functional redundancy at six different growth stages., Results: Hybrid progenies showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents. Based on the expression profiling, our results indicated significant dynamic transcriptional expression patterns, portraying the different homeolog-dominance at the same stage in the different cultivars and their hybrids. Albeit belonging to same syntenic triads, a dynamic trend was observed in the regulatory signatures of these genes that might be influencing their expression profiles., Conclusion: These findings can substantially contribute and provide insights for the selective introduction of better cultivars into traditional and hybrid breeding programs which can be harnessed for the improvement of future wheat., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: SnRK2 plays vital role in responding to adverse abiotic stimuli. The applicability of TaSnRK2.4 and TaSnRK2.9 was investigated to leverage the potential of these genes in indigenous wheat breeding programs., Methods: Genetic diversity was assessed using pre-existing markers for TaSnRK2.4 and TaSnRK2.9. Furthermore, new markers were also developed to enhance their broader applicability. KASP markers were designed for TaSnRK2.4, while CAPS-based markers were tailored for TaSnRK2.9., Results: Analysis revealed lack of polymorphism in TaSnRK2.4 among Pakistani wheat germplasm under study. To validate this finding, available gel-based markers for TaSnRK2.4 were employed, producing consistent results and offering limited potential for application in marker-assisted wheat breeding with Pakistani wheat material. For TaSnRK2.9-5A, CAPS2.9-5A-1 and CAPS2.9-5A-2 markers were designed to target SNP positions at 308 nt and 1700 nt revealing four distinct haplotypes. Association analysis highlighted the significance of Hap-5A-1 of TaSnRK2.9-5A, which exhibited association with an increased number of productive tillers (NPT), grains per spike (GPS), and reduced plant height (PH) under well-watered (WW) conditions. Moreover, it showed positive influence on NPT under WW conditions, GPS under water-limited (WL) conditions, and PH under both WW and WL conditions. High selection intensity observed for Hap-5A-1 underscores the valuable role it has played in Pakistani wheat breeding programs. Gene expression studies of TaSnRK2.9-5A revealed the involvement of this gene in response to PEG, NaCl, low temperature and ABA treatments., Conclusion: These findings propose that TaSnRK2.9 can be effectively employed for improving wheat through marker-assisted selection in wheat breeding efforts., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

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., (© 2024 Institute of Botany, Chinese Academy of Sciences.)

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/m 2 . 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.

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., (© 2024 Scandinavian Plant Physiology Society.)

Here, we performed RNA-seq based expression analysis of root and leaf tissues of a set of 24 historical spring wheat cultivars representing 110 years of temporal genetic variations. This huge 130 tissues RNAseq dataset was initially used to study expression pattern of 97 genes regulating root growth and development in wheat. Root system architecture (RSA) is an important target for breeding stress-resilient and high-yielding wheat cultivars under climatic fluctuations. However, root transcriptome analysis is usually obscured due to challenges in root research due to their below ground presence. We also validated the dataset by performing correlation analysis between expression of RSA related genes in roots and leaves with 25 root traits analyzed under varying moisture conditions and 10 yield-related traits. The Pearson's correlation coefficients between root phenotypes and expression of root-specific genes varied from -0.72 to 0.78, and strong correlations with genes such as DRO1, TaMOR, ARF4, PIN1 was observed. The presented datasets have multiple uses such as a) studying the change in expression pattern of genes during time, b) differential expression of genes in two very important tissues of wheat i.e., leaf and roots, and c) studying customized expression of genes associated with important phenotypes in diverse wheat cultivars. The initial findings presented here provided key insights into understanding the transcriptomic basis of phenotypic variability of RSA in wheat cultivars., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

Wheat (Triticum aestivum) is one of the most essential human energy and protein sources. However, wheat production is threatened by devastating fungal diseases such as stripe rust, caused by Puccinia striiformis Westend. f. sp. tritici (Pst). Here, we reveal that the alternations in chloroplast lipid profiles and the accumulation of jasmonate (JA) in the necrosis region activate JA signaling and trigger the host defense. The collapse of chloroplasts in the necrosis region results in accumulations of polyunsaturated membrane lipids and the lipid-derived phytohormone JA in transgenic lines of Yr36 that encodes Wheat Kinase START 1 (WKS1), a high-temperature-dependent adult plant resistance protein. WKS1.1, a protein encoded by a full-length splicing variant of WKS1, phosphorylates and enhances the activity of keto-acyl thiolase (KAT-2B), a critical enzyme catalyzing the β-oxidation reaction in JA biosynthesis. The premature stop mutant, kat-2b, accumulates less JA and shows defects in the host defense against Pst. Conversely, overexpression of KAT-2B results in a higher level of JA and limits the growth of Pst. Moreover, JA inhibits the growth and reduces pustule densities of Pst. This study illustrates the WKS1.1‒KAT-2B‒JA pathway for enhancing wheat defense against fungal pathogens to attenuate yield loss., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2023 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Wheat fixes CO 2 by photosynthesis into kernels to nourish humankind. Improving the photosynthesis rate is a major driving force in assimilating atmospheric CO 2 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 CO 2 ASSIMILATION 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 CO 2 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., (© 2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Main Conclusion: The study provided an insight toward better understanding of stay-green mechanisms for drought tolerance improvement and identified that synthetic-derived wheats proved as a promising germplasm for improved tolerance against water stress. Stay-green (SG) trait is considered to be related with the ability of wheat plants to maintain photosynthesis and CO 2 assimilation. The present study explored the interaction of water stress with SG expression through physio-biochemical, agronomic and phenotypic responses among diverse wheat germplasm comprising of 200 synthetic hexaploids, 12 synthetic derivatives, 97 landraces and 16 conventional bread wheat varieties, for 2 years. The study established that variation of SG trait existed in the studied wheat germplasm and there was positive association between SG trait and tolerance to water stress. The relationship of SG trait with chlorophyll content (r = 0.97), ETR (r = 0.28), GNS (r = 0.44), BMP (r = 0.34) and GYP (r = 0.44) was particularly promising under water stress environment. Regarding chlorophyll fluorescence, the positive correlation of фPSII (r = 0.21), qP (r = 0.27) and ETR (r = 0.44) with grain yield per plant was noted. The improved ΦPSII and Fv/Fm of PSII photochemistry resulted in the high photosynthesis activity in SG wheat genotypes. Regarding relative water content and photochemical quenching coefficient, synthetic-derived wheats were better by maintaining 20.9, 9.8 and 16.1% more RWC and exhibiting 30.2, 13.5 and 17.9% more qP when compared with landraces, varieties and synthetic hexaploids, respectively, under water stress environment. Synthetic derived wheats also exhibited relatively more SG character with good yield and were more tolerant to water stress in terms of grain yield, grain weight per plant, better photosynthetic performance through chlorophyll fluorescence measurement, high leaf chlorophyll and proline content, and hence, may be used as novel sources for breeding drought tolerant materials. The study will further facilitate research on wheat leaf senescence and will add to better understanding of SG mechanisms for drought tolerance improvement., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (Copyright © 2022 The Author. Published by Elsevier Inc. All rights reserved.)

PCR-based individual Single nucleotide polymorphism (SNP) genotyping methods are preferred due to their flexibility, high-throughput, and improved accuracy. Semi-thermal asymmetric reverse PCR (STARP) is one of the SNP genotyping methods developed to reduce operational cost with improved platform compatibility. STARP is a unique method which can be used either as a gel-free SNP genotyping by detection of fluorescent signals or polyacrylamide gel-based size separation. SNP assay designing using sequence information and detection methods of STARP are described in detail., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2022. The Author(s).)

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., Competing Interests: Declaration of Competing Interest Authors declare no conflict of interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Crop wild resources are excellent sources of new genetic variation for resilience against climate extremes. However, detailed characterization of the desirable phenotypes is essential before using these crop wild resources in breeding programs. This current study was, therefore, conducted to investigate the water stress responses of eight wild Elymus species and two wheat cultivars. The experiment was carried out under varying levels of osmotic stress induced by polyethylene glycol and progressive water stress through different field capacities. Water stress significantly reduced both physiological and biochemical traits compared to control, ranging from 7.1% (protein content) to 34.5% (chlorophyll) under moderate stress and 9.1-45.8% under severe stress. The anatomical features were also affected under progressive water stress, including a reduction in xylem vessel diameter (7.92 and 16.50%), phloem length (4.36 and 7.18%), vascular bundle length (3.09 and 6.04%), and ground tissue thickness (2.36 and 5.52%), respectively. Conclusively, Elymus borianus (endemic to Swat, Pakistan) , E . russelli, E . caninus, E . longioristatus , and E . dauhuricus outperformed the check wheat cultivar, Pirsabak 2005, which is a rainfed variety. The results revealed that Elymus species belonging to the tertiary gene pool of bread wheat could be an excellent drought tolerance source for use in a breeding program., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer JI declared a shared affiliation with the author AR to the handling editor at the time of the review., (Copyright © 2022 Khan, Ali, Ullah, Ali, Kaushik, Alyemeni, Rasheed and Sher.)

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 (r 2 ) ranged between 0.57 and 0.74. The r 2 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., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Root hairs play an important role in absorbing water and nutrients in crop plants. Here we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope. A collection of 24 century wide spring wheat cultivars released between 1911 and 2016 were phenotyped for RHL and RHD. The results revealed significant variations for both traits with five and six-fold variation for RHL and RHD, respectively. RHL ranged from 1.01 mm to 1.77 mm with an average of 1.39 mm, and RHD ranged from 17.08 mm -2 to 20.8 mm -2 with an average of 19.6 mm -2 . Agronomic and physiological traits collected from five different environments and their best linear unbiased predictions (BLUPs) were correlated with RHL and RHD, and results revealed that relative-water contents (RWC), biomass and grain per spike (GpS) were positively correlated with RHL in both water-limited and well-watered conditions. While RHD was negatively correlated with grain yield (GY) in four environments and their BLUPs. Both RHL and RHD had positive correlation indicating the possibility of simultaneous selection of both phenotypes during wheat breeding. The expression pattern of TaRSL4 gene involved in regulation of root hair length was determined in all 24 wheat cultivars based on RNA-seq data, which indicated the differentially higher expression of the A- and D- homeologues of the gene in roots, while B-homeologue was consistently expressed in both leaf and roots. The results were validated by qRT-PCR and the expression of TaRSL4 was consistently high in rainfed cultivars such as Chakwal-50, Rawal-87, and Margallah-99. Overall, the new phenotyping method for RHL and RHD along with correlations with morphological and physiological traits in spring wheat cultivars improved our understanding for selection of these phenotypes in wheat breeding.

Increasing global temperature has adverse effects on crop health and productivity at both seedling and reproductivity stages. It is paramount to develop heat tolerant wheat cultivars able to sustain under high and fluctuating temperature conditions. An experiment was conducted to characterize 194 historical wheat cultivars of Pakistan under high temperature at seedling stage to identify loci associated with heat tolerance using genome-wide association studies (GWAS). A quantitative trait locus, TaHST1 , on chr4A was also characterized to identify the haplotypes at this locus associated with heat tolerance in wheat from Pakistan. Initially, the diversity panel was planted under control conditions (25°C/20°C day and night temperature) in a glass house. At three leaf stage, plants were subjected to heat stress (HS) by increasing temperature (40°C/35°C day and night), while one treatment was kept at control condition. After 7 days of HS, data were collected for seedling morphology. Heat stress reduced these traits by 25% (root weight) to 40% (shoot weight), and shoot biomass was largely affected by heat stress. A GWAS model, fixed and random model circulating probability unification (FarmCPU), identified 43 quantitative trait nucleotides (QTNs) on all chromosomes, except chr7B, were associated under both HS and control conditions. Thirteen QTNs were identified in control, while 30 QTNs were identified in HS condition. In total, 24 haplotypes were identified at TaHST1 locus, and most of the heat tolerant genotypes were assigned to Hap-20 and Hap-21. Eleven QTNs were identified within 0.3-3.1 Mb proximity of heat shock protein (HSP). Conclusively, this study provided a detailed genetic framework of heat tolerance in wheat at the seedling stage and identify potential genetic regions associated with heat tolerance which can be used for marker assisted selection (MAS) in breeding for heat stress tolerance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Khan, Kainat, Maqbool, Ambreen, Ahmad, Suleman, Mahmood, Ali, Aziz, Rasheed and Li.)

Phenolics are a class of chemical compounds possessing antioxidant activity, which are mainly located in the wheat (Triticum aestivum) bran. Different approaches have been used in food industry to increase the availability of phenolics. Compared to these methods, however, genetic improvement of the wheat antioxidant potential, is a cost-effective, easier and safer approach. Here, we showed a single premature stop mutation in the keto-acythiolase-2 (kat-2b) gene, which significantly improved the antioxidant potential of pasta by a 60 ± 16% increase in its antioxidant potential by increasing the accumulation of ferulic acid. These changes are likely determined by the increased transcription (46% higher) and activity (120% higher) of the phenylalanine lyase genes observed in the mutated line compared to the control. Even if more studies will need to be done, overall, this study suggested that the kat-2b mutant could represent an excellent genetic resource to improve wheat's antioxidant and health-promoting potential., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Synthetic hexaploid wheats and their derived advanced lines were subject to empirical selection in developing genetically superior cultivars. To investigate genetic diversity, patterns of nucleotide diversity, population structure, and selection signatures during wheat breeding, we tested 422 wheat accessions, including 145 synthetic-derived wheats, 128 spring wheat cultivars, and 149 advanced breeding lines from Pakistan. A total of 18,589 high-quality GBS-SNPs were identified that were distributed across the A (40%), B (49%), and D (11%) genomes. Values of population diversity parameters were estimated across chromosomes and genomes. Genome-wide average values of genetic diversity and polymorphic information content were estimated to be 0.30 and 0.25, respectively. Neighbor-joining (NJ) tree, principal component analysis (PCA), and kinship analyses revealed that synthetic-derived wheats and advanced breeding lines were genetically diverse. The 422 accessions were not separated into distinct groups by NJ analysis and confirmed using the PCA. This conclusion was validated with both relative kinship and Rogers' genetic distance analyses. EigenGWAS analysis revealed that 32 unique genome regions had undergone selection. We found that 50% of the selected regions were located in the B-genome, 29% in the D-genome, and 21% in the A-genome. Previously known functional genes or QTL were found within the selection regions associated with phenology-related traits such as vernalization, adaptability, disease resistance, and yield-related traits. The selection signatures identified in the present investigation will be useful for understanding the targets of modern wheat breeding in Pakistan., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ali, Danting, Wang, Sadiq, Rasheed, He and Li.)

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., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (© 2022 Society for Experimental Biology and John Wiley & Sons Ltd.)

Biofortification is a sustainable strategy to alleviate micronutrient deficiency in humans. It is necessary to improve grain zinc (GZnC) and iron concentrations (GFeC) in wheat based on genetic knowledge. However, the precise dissection of the genetic architecture underlying GZnC and GFeC remains challenging. In this study, high-resolution genome-wide association studies were conducted for GZnC and GFeC by three different models using 166 wheat cultivars and 373,106 polymorphic markers from the wheat 660K and 90K single nucleotide polymorphism (SNP) arrays. Totally, 25 and 16 stable loci were detected for GZnC and GFeC, respectively. Among them, 17 loci for GZnC and 8 for GFeC are likely to be new quantitative trait locus/loci (QTL). Based on gene annotations and expression profiles, 28 promising candidate genes were identified for Zn/Fe uptake (8), transport (11), storage (3), and regulations (6). Of them, 11 genes were putative wheat orthologs of known Arabidopsis and rice genes related to Zn/Fe homeostasis. A brief model, such as genes related to Zn/Fe homeostasis from root uptake, xylem transport to the final seed storage was proposed in wheat. Kompetitive allele-specific PCR (KASP) markers were successfully developed for two major QTL of GZnC on chromosome arms 3AL and 7AL, respectively, which were independent of thousand kernel weight and plant height. The 3AL QTL was further validated in a bi-parental population under multi-environments. A wheat multidrug and toxic compound extrusion (MATE) transporter TraesCS3A01G499300 , the ortholog of rice gene OsPEZ2 , was identified as a potential candidate gene. This study has advanced our knowledge of the genetic basis underlying GZnC and GFeC in wheat and provides valuable markers and candidate genes for wheat biofortification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tong, Zhao, Sun, Fu, Song, Liu, Zhang, Zheng, Pu, Liu, Rasheed, Li, Xia, He and Hao.)

Stripe rust caused by Puccnina striiformis ( Pst ) is an economically important disease attacking wheat all over the world. Identifying and deploying new genes for Pst resistance is an economical and long-term strategy for controlling Pst . A genome-wide association study (GWAS) using single nucleotide polymorphisms (SNPs) and functional haplotypes were used to identify loci associated with stripe rust resistance in synthetic-derived (SYN-DER) wheats in four environments. In total, 92 quantitative trait nucleotides (QTNs) distributed over 65 different loci were associated with resistance to Pst at seedling and adult plant stages. Nine additional loci were discovered by the linkage disequilibrium-based haplotype-GWAS approach. The durable rust-resistant gene Lr34/Yr18 provided resistance in all four environments, and against all the five Pst races used in this study. The analysis identified several SYN-DER accessions that carried major genes: either Yr24/Yr26 or Yr32 . New loci were also identified on chr2B, chr5B, and chr7D, and 14 QTNs and three haplotypes identified on the D-genome possibly carry new alleles of the known genes contributed by the Ae. tauschii founders. We also evaluated eleven different models for genomic prediction of Pst resistance, and a prediction accuracy up to 0.85 was achieved for an adult plant resistance, however, genomic prediction for seedling resistance remained very low. A meta-analysis based on a large number of existing GWAS would enhance the identification of new genes and loci for stripe rust resistance in wheat. The genetic framework elucidated here for stripe rust resistance in SYN-DER identified the novel loci for resistance to Pst assembled in adapted genetic backgrounds., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Mahmood, Ali, Mirza, Fayyaz, Majeed, Naeem, Aziz, Trethowan, Ogbonnaya, Poland, Quraishi, Hickey, Rasheed and He.)

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.

Background: The COBL genes encode a plant-specific glycosylphosphatidylinositol (GPI)-anchored protein. Recently identified COBRA genes are supposed as a key regulator of the orientation of cell expansion in the root indicating that COBRA gene family members are likely to be important players at the plasma membrane-cell wall interface., Methods and Results: Five COBL gene namely, TaCOBL 1, TaCOBL 2, TaCOBL 3, TaCOBL 4 and TaCOBL 5 were identified using database search and domain predictions. Chromosomal location of each gene was mapped on karyotype. Structure of genes, promoter analysis and phylogenetic analysis were performed using different bioinformatics tools. Set of novel SNPs were also predicted. Gene ontologies were analyzed, and the processes and pathways were identified in which COBRA genes were involved. The molecular weight all the cobra proteins was in range of 50-75 KDa with 429-461 amino acid residues. The COBL genes were mapped on homeologous groups 2, 4, 5, 6 and 7. Gene ontology analysis revealed that TaCOBL genes were involved in cellulose microfibril organization, mucilage biosynthetic process involved in seed coat development, plant-type cell wall biogenesis plant-type cell wall cellulose biosynthetic process, seed coat development and seed development. Three drought responsive cis-elements (WRKY, ABRE and DRE) were found nearby COBL genes The qRT-PCR revealed TaCOBL genes are drought responsive and can be further explored to understand their role in drought tolerance in wheat., Conclusion: The comprehensive annotation and expression profiling of COBL genes revealed that all five COBL genes are drought response. The promoter cis-regulatory element analysis revealed that COBL genes had stress related WRKY, ABRE and DRE cis-regulatory elements. This evidence suggest that TaCOBL genes are involved in drought stress tolerance., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Characterization of genomic regions underlying adaptation of landraces can reveal a quantitative genetics framework for local wheat (Triticum aestivum L.) adaptability. A collection of 512 wheat landraces from the eastern edge of the Fertile Crescent in Iran and Pakistan were genotyped using genome-wide single nucleotide polymorphism markers generated by genotyping-by-sequencing. The minor allele frequency (MAF) and the heterozygosity (H) of Pakistani wheat landraces (MAF = 0.19, H = 0.008) were slightly higher than the Iranian wheat landraces (MAF = 0.17, H = 0.005), indicating that Pakistani landraces were slightly more genetically diverse. Population structure analysis clearly separated the Pakistani landraces from Iranian landraces, which indicates two separate adaptability trajectories. The large-scale agro-climatic data of seven variables were quite dissimilar between Iran and Pakistan as revealed by the correlation coefficients. Genome-wide association study identified 91 and 58 loci using agroclimatic data, which likely underpin local adaptability of the wheat landraces from Iran and Pakistan, respectively. Selective sweep analysis identified significant hits on chromosomes 4A, 4B, 6B, 7B, 2D, and 6D, which were colocalized with the loci associated with local adaptability and with some known genes related to flowering time and grain size. This study provides insight into the genetic diversity with emphasis on the genetic architecture of loci involved in adaptation to local environments, which has breeding implications., (© 2021 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

The micronutrients iron (Fe) and zinc (Zn) are not only essential for plant survival and proliferation but are crucial for human health. Increasing Fe and Zn levels in edible parts of plants, known as biofortification, is seen a sustainable approach to alleviate micronutrient deficiency in humans. Wheat, as one of the leading staple foods worldwide, is recognized as a prioritized choice for Fe and Zn biofortification. However, to date, limited molecular and physiological mechanisms have been elucidated for Fe and Zn homeostasis in wheat. The expanding molecular understanding of Fe and Zn homeostasis in model plants is providing invaluable resources to biofortify wheat. Recent advancements in NGS (next generation sequencing) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms have initiated a revolution in resources and approaches for wheat genetic investigations and breeding. Here, we summarize molecular processes and genes involved in Fe and Zn homeostasis in the model plants Arabidopsis and rice, identify their orthologs in the wheat genome, and relate them to known wheat Fe/Zn QTL (quantitative trait locus/loci) based on physical positions. The current study provides the first inventory of the genes regulating grain Fe and Zn homeostasis in wheat, which will benefit gene discovery and breeding, and thereby accelerate the release of Fe- and Zn-enriched wheats.

Key Message: 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.

Stripe rust, caused by Puccinia striiformis f. sp. tritici , is a globally devastating disease of common wheat ( Triticum aestivum L.), resulting in substantial economic losses. To identify effective resistance genes, a genome-wide association study was conducted on 120 common wheat lines from different wheat-growing regions of China using the wheat 90K iSelect SNP array. Seventeen loci were identified, explaining 9.5 to 21.8% of the phenotypic variation. Most of these genes were detected in the A (seven) and B (seven) genomes, with only three in the D genome. Among them, 11 loci were colocated with known resistance genes or quantitative trait loci reported previously, whereas the other six are likely new resistance loci. Annotation of flanking sequences of significantly associated SNPs indicated the presence of three important candidate genes, including E3 ubiquitin-protein ligase, F-box repeat protein, and disease resistance RPP13-like protein. This study increased our knowledge in understanding the genetic architecture for stripe rust resistance and identified wheat varieties with multiple resistance alleles, which are useful for improvement of stripe rust resistance in breeding.

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., (Copyright © 2020 Yang, Hassan, Xu, Zheng, Rasheed, Zhang, Jin, Xia, Xiao and He.)

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.

Fusarium head blight (FHB) is a devastating wheat disease worldwide. To decipher the genetic architecture of FHB resistance in Chinese germplasm, a Wheat Association Panel for Scab Research (WAPS) consisting of 240 leading Chinese wheat cultivars and elite lines was genotyped using the 90K single nucleotide polymorphism (SNP) arrays. The FHB response was evaluated in the field nurseries in Wuhan in Hubei Province over four consecutive years from 2014 to 2017. Five quantitative trait loci (QTL) were consistently identified on chromosome arms 1AS, 2DL, 5AS, 5AL, and 7DS using a mixed linear model (MLM), explaining 5.6, 10.3, 5.7, 5.4, and 5.6% of phenotypic variation, respectively. The QTL on 5AS, 5AL, and 7DS QTL are probably novel. The allelic frequency analysis indicated that cultivars from the Middle and Lower Yangtze River Valleys harbored more favorable alleles and were therefore more resistant than those from other regions. To facilitate in-house germplasm screening and marker-assisted selection (MAS), SNP-derived PCR markers were developed for the QTL regions on 1AS, 5AS, and 5AL QTL. In addition to the above five QTL, the WAPS population had a very low frequency of Fhb1 , confirming that the gene is not widely used in Chinese wheat breeding programs. The resistant lines and molecular markers developed in this study are resources and information for enhancing FHB resistance in breeding populations by marker-assisted recurrent selection and gene stacking., (Copyright © 2020 Zhu, Chen, Zhang, Yang, Zhu, Li, Liu, Tong, Fu, Liu, Rasheed, Xia, He, Hao and Gao.)

Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS., Competing Interests: The authors declare no conflict of interest.

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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.

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., (© 2019 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.)

Crop-wild introgressions have long been exploited without knowing the favorable recombination points. Synthetic hexaploid wheats are one of the most exploited genetic resources for bread wheat improvement. However, despite some QTL with major effects, much less is known about genome-wide patterns of introgressions and their effects on phenotypes. We used two genome-wide association approaches: SNP-GWAS and haplotype-GWAS to identify SNPs and haplotypes associated with productivity under water-limited conditions in a synthetic-derived wheat (SYN-DER) population. Haplotype-GWAS further enriched and identified 20 more genomic regions associated with drought adaptability that did not overlap with SNP-GWAS. Since GWAS is biased to the phenotypes in the study and may fail to detect important genetic diversity during breeding, we used five complementary analytical approaches ( t -test, Tajima's D, nucleotide diversity (π), Fst , and EigenGWAS) to identify divergent selections in SYN-DER compared to modern bread wheat. These approaches consistently pinpointed 89 'selective sweeps', out of which 30 selection loci were identified on D-genome. These key selections co-localized with important functional genes of adaptive traits such as TaElf3-D1 (1D) for earliness per se (Eps), TaCKX-D1 (3D), TaGS1a (6D) and TaGS-D1 (7D) for grain size, weight and morphology, TaCwi-D1 (5D) influencing drought tolerance, and Vrn-D3 (7D) for vernalization. Furthermore, 55 SNPs and 23 haplotypes of agronomic and physiological importance such as grain yield, relative water content and thousand grain weight in SYN-DER, were among the top 5% of divergent selections contributed by synthetic hexaploid wheats. These divergent selections associated with improved agronomic performance carry new alleles that have been introduced to wheat. Our results demonstrated that GWAS and selection sweep analyses are powerful approaches for investigating favorable introgressions under strong selection pressure and the use of crop-wild hybridization to assist the improvement of wheat yield and productivity under moisture limiting environments., (Copyright © 2019 Afzal et al.)

Modern breeding imposed selection for improved productivity that largely influenced the frequency of superior alleles underpinning traits of breeding interest. Therefore, molecular diagnosis for the allelic variations of such genes is important to manipulate beneficial alleles in wheat molecular breeding. We analyzed a diversity panel largely consisted of advanced lines derived from synthetic hexaploid wheats for allelic variation at 87 functional genes or loci of breeding importance using 124 high-throughput KASP markers. We also developed two KASP markers for water-soluble carbohydrate genes ( TaSST-D1 and TaSST-A1 ) associated with plant height and thousand grain weight (TGW) in the diversity panel. KASP genotyping results indicated that beneficial alleles for genes underpinning flowering time ( Ppd-D1 and Vrn-D3 ), thousand grain weight ( TaCKX-D1, TaTGW6-A1, TaSus1-7B , and TaCwi-D1 ), water-soluble carbohydrates ( TaSST-A1 ), yellow-pigment content ( Psy-B1 and Zds-D1 ), and root lesion nematodes ( Rlnn1 ) were fixed in diversity panel with frequency ranged from 96.4 to 100%. The association analysis of functional genes with agronomic and biochemical traits under well-watered (WW) and water-limited (WL) conditions revealed that 21 marker-trait associations (MTAs) were consistently detected in both moisture conditions. The major developmental genes such as Vrn-A1, Rht-D1 , and Ppd-B1 had the confounding effect on several agronomic traits including plant height, grain size and weight, and grain yield in both WW and WL conditions. The accumulation of favorable alleles for grain size and weight genes additively enhanced grain weight in the diversity panel. Graphical genotyping approach was used to identify accessions with maximum number of favorable alleles, thus likely to have high breeding value. These results improved our knowledge on the selection of favorable and unfavorable alleles through unconscious selection breeding and identified the opportunities to deploy alleles with effects in wheat breeding.

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 R 2  = 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 (h 2  = 0.91), flowering (F)(h 2  = 0.95), EGF (h 2  = 0.79) and mid grain filling (MGF) (h 2  = 0.71) under the full irrigation treatment, and at booting (B) (h 2  = 0.89), EGF (h 2  = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R 2  = 0.86), MGF (R 2  = 0.83) and LGF (R 2  = 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 (R 2  = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R 2  = 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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Identification of loci for grain yield (GY) and related traits, and dissection of the genetic architecture are important for yield improvement through marker-assisted selection (MAS). Two genome-wide association study (GWAS) methods were used on a diverse panel of 166 elite wheat varieties from the Yellow and Huai River Valleys Wheat Zone (YHRVWD) of China to detect stable loci and analyze relationships among GY and related traits., Results: A total of 326,570 single nucleotide polymorphism (SNP) markers from the wheat 90 K and 660 K SNP arrays were chosen for GWAS of GY and related traits, generating a physical distance of 14,064.8 Mb. One hundred and twenty common loci were detected using SNP-GWAS and Haplotype-GWAS, among which two were potentially functional genes underpinning kernel weight and plant height (PH), eight were at similar locations to the quantitative trait loci (QTL) identified in recombinant inbred line (RIL) populations in a previous study, and 78 were potentially new. Twelve pleiotropic loci were detected on eight chromosomes; among these the interval 714.4-725.8 Mb on chromosome 3A was significantly associated with GY, kernel number per spike (KNS), kernel width (KW), spike dry weight (SDW), PH, uppermost internode length (UIL), and flag leaf length (FLL). GY shared five loci with thousand kernel weight (TKW) and PH, indicating significantly affected by two traits. Compared with the total number of loci for each trait in the diverse panel, the average number of alleles for increasing phenotypic values of GY, TKW, kernel length (KL), KW, and flag leaf width (FLW) were higher, whereas the numbers for PH, UIL and FLL were lower. There were significant additive effects for each trait when favorable alleles were combined. UIL and FLL can be directly used for selecting high-yielding varieties, whereas FLW can be used to select spike number per unit area (SN) and KNS., Conclusions: The loci and significant SNP markers identified in the present study can be used for pyramiding favorable alleles in developing high-yielding varieties. Our study proved that both GWAS methods and high-density genetic markers are reliable means of identifying loci for GY and related traits, and provided new insight to the genetic architecture of GY.

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 R 2  = 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 ( R 2  = 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., Competing Interests: The authors declare that they have no competing interests.