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Bread wheat (T. aestivum) is one of the world’s most widely consumed cereals. Since micronutrient deficiencies are becoming more common among people who primarily depend upon cereal-based diets, a need for better-quality wheat varieties has been felt. An association panel of 154 T. aestivum lines was evaluated for the following quality traits: grain appearance (GA) score, grain hardness (GH), phenol reaction (PR) score, protein percent, sodium dodecyl sulfate (SDS) sedimentation value, and test weight (TWt). In addition, the panel was also phenotyped for grain yield and related traits such as days to heading, days to maturity, plant height, and thousand kernel weight for the year 2017–18 at the Borlaug Institute for South Asia (BISA) Ludhiana and Jabalpur sites. We performed a genome-wide association analysis on this panel using 18,351 genotyping-by-sequencing (GBS) markers to find marker-trait associations for quality and grain yield-related traits. We detected 55 single nucleotide polymorphism (SNP) marker trait associations (MTAs) for quality-related traits on chromosomes 7B (10), 1A (9), 2A (8), 3B (6), 2B (5), 7A (4), and 1B (3), with 3A, 4A, and 6D, having two and the rest, 4B, 5A, 5B, and 1D, having one each. Additionally, 20 SNP MTAs were detected for yield-related traits based on a field experiment conducted in Ludhiana on 7D (4) and 4D (3) chromosomes, while 44 SNP MTAs were reported for Jabalpur on chromosomes 2D (6), 7A (5), 2A (4), and 4A (4). Utilizing these loci in marker-assisted selection will benefit from further validation studies for these loci to improve hexaploid wheat for better yield and grain quality.

Bread wheat germplasm is accessed from the International Maize and Wheat Improvement Centre (CIMMYT) and the International Centre for Agricultural Research in the Dry Areas (ICARDA) by Australian wheat breeders and researchers through the CIMMYT Australia ICARDA Germplasm Evaluation (CAIGE) program. The CAIGE program coordinates the selection, importation, quarantine, dissemination, and evaluation of the imported bread wheat germplasm and the management of associated data and information. This paper describes the CAIGE model and assesses both the genetic and economic impacts of these materials on the Australian wheat industry after commercialisation of wheat breeding in the early 21st century and the establishment of CAIGE. The CAIGE concept was validated using data collected and analysed from multi-environment trials between 2017 and 2020. The impact of cultivars with and without CAIGE contribution to pedigree on yield was estimated using production-by-variety statistics. Net gain in yield, estimated as the yield difference between CAIGE and Non-CAIGE varieties, was multiplied by the percentage contribution to pedigree to estimate the additional yield. The CAIGE bread wheat program identified diverse, high-yielding, and disease-resistant germplasm and significantly improved the capture and dissemination of information. The benefit-cost ratio, calculated as the sum of benefits divided by investments, indicated that, for every dollar invested in CAIGE, a further $20 was generated in benefits. The internal rate of return was estimated at 163% and the modified rate at 18%. The benefits of these international materials to Australian wheat breeding remained significant.

Genetic dissection of yield component traits including kernel characteristics is essential for the continuous improvement in wheat yield. In the present study, one recombinant inbred line (RIL) F6 population derived from a cross between Avocet and Chilero was used to evaluate the phenotypes of kernel traits of thousand-kernel weight (TKW), kernel length (KL), and kernel width (KW) in four environments at three experimental stations during the 2018–2020 wheat growing seasons. The high-density genetic linkage map was constructed with the diversity arrays technology (DArT) markers and the inclusive composite interval mapping (ICIM) method to identify the quantitative trait loci (QTLs) for TKW, KL, and KW. A total of 48 QTLs for three traits were identified in the RIL population on the 21 chromosomes besides 2A, 4D, and 5B, accounting for 3.00%–33.85% of the phenotypic variances. Based on the physical positions of each QTL, nine stable QTL clusters were identified in the RILs, and among these QTL clusters, TaTKW-1A was tightly linked to the DArT marker interval 3950546–1213099, explaining 10.31%–33.85% of the phenotypic variances. A total of 347 high-confidence genes were identified in a 34.74-Mb physical interval. TraesCS1A02G045300 and TraesCS1A02G058400 were among the putative candidate genes associated with kernel traits, and they were expressed during grain development. Moreover, we also developed high-throughput kompetitive allele-specific PCR (KASP) markers of TaTKW-1A, validated in a natural population of 114 wheat varieties. The study provides a basis for cloning the functional genes underlying the QTL for kernel traits and a practical and accurate marker for molecular breeding.

Developing wheat varieties with durable resistance is a core objective of the International Maize and Wheat Improvement Center (CIMMYT) and many other breeding programs worldwide. The CIMMYT advanced wheat line “Mucuy” displayed high levels of resistance to stripe rust (YR) and leaf rust (LR) in field evaluations in Mexico and several other countries. To determine the genetic basis of YR and LR resistance, 138 F5 recombinant inbred lines (RILs) derived from the cross of Apav#1× Mucuy were phenotyped for YR responses from 2015 to 2020 at field sites in India, Kenya, and Mexico, and LR in Mexico. Seedling phenotyping for YR and LR responses was conducted in the greenhouse in Mexico using the same predominant races as in field trials. Using 12,681 polymorphic molecular markers from the DArT, SNP, and SSR genotyping platforms, we constructed genetic linkage maps and QTL analyses that detected seven YR and four LR resistance loci. Among these, a co-located YR/LR resistance loci was identified as Yr29/Lr46, and a seedling stripe rust resistance gene YrMu was mapped on the 2AS/2NS translocation. This fragment also conferred moderate adult plant resistance (APR) under all Mexican field environments and in one season in Kenya. Field trial phenotyping with Lr37-virulent Puccinia triticina races indicated the presence of an APR QTL accounting for 18.3–25.5% of the LR severity variation, in addition to a novel YR resistance QTL, QYr.cim-3DS, derived from Mucuy. We developed breeder-friendly KASP and indel molecular markers respectively for Yr29/Lr46 and YrMu. The current study validated the presence of known genes and identified new resistance loci, a QTL combination effect, and flanking markers to facilitate accelerated breeding for genetically complex, durable rust resistance.

Leaf rust and stripe rust are important wheat diseases worldwide causing significant losses where susceptible varieties are grown. Resistant cultivars offer long-term control and reduce the use of hazardous chemicals, which can be detrimental to both human health and the environment. Land races have been a valuable resource for mining new genes for various abiotic and biotic stresses including wheat rusts. Afghan wheat landrace “KU3067” displayed high seedling infection type (IT) for leaf rust and low IT for stripe rust; however, it displayed high levels of field resistance for both rusts when tested for multiple seasons against the Mexican rust isolates. This study focused on identifying loci-conferring seedling resistance to stripe rust, and also loci-conferring adult plant resistance (APR) against the Mexican races of leaf rust and stripe rust. A backcrossed inbred line (BIL) population advanced to the BC1F5 generation derived from the cross of KU3067 and Apav (triple rust susceptible line) was used for both, inheritance and QTL mapping studies. The population and parents were genotyped with Diversity Arrays Technology-genotyping-by-sequencing (DArT-Seq) and phenotyped for leaf rust and stripe rust response at both seedling and adult plant stages during multiple seasons in Mexico with relevant pathotypes. Mapping results identified an all-stage resistance gene for stripe rust, temporarily designated as YrKU, on chromosome 7BL. In total, six QTL-conferring APR to leaf rust on 1AS, 2AL, 4DL, 6BL, 7AL, and 7BL, and four QTL for stripe rust resistance on 1BS, 2AL, 4DL, and 7BL were detected in the analyses. Among these, pleiotropic gene Lr67/Yr46 on 4DL with a significantly large effect is the first report in an Afghan landrace-conferring resistance to both leaf and stripe rusts. QLr.cim-7BL/YrKU showed pleiotropic resistance to both rusts and explained 7.5–17.2 and 12.6–19.3% of the phenotypic variance for leaf and stripe rusts, respectively. QYr.cim-1BS and QYr.cim-2AL detected in all stripe environments with phenotypic variance explained (PVE) 12.9–20.5 and 5.4–12.5%, and QLr.cim-6BL are likely to be new. These QTL and their closely linked markers will be useful for fine mapping and marker-assisted selection (MAS) in breeding for durable resistance to multiple rust diseases.

Accelerating breeding efforts for developing biofortified bread wheat varieties necessitates understanding the genetic control of grain zinc concentration (GZnC) and grain iron concentration (GFeC). Hence, the major objective of this study was to perform genome-wide association mapping to identify consistently significant genotyping-by-sequencing markers associated with GZnC and GFeC using a large panel of 5,585 breeding lines from the International Maize and Wheat Improvement Center. These lines were grown between 2018 and 2021 in an optimally irrigated environment at Obregon, Mexico, while some of them were also grown in a water-limiting drought-stressed environment and a space-limiting small plot environment and evaluated for GZnC and GFeC. The lines showed a large and continuous variation for GZnC ranging from 27 to 74.5 ppm and GFeC ranging from 27 to 53.4 ppm. We performed 742,113 marker-traits association tests in 73 datasets and identified 141 markers consistently associated with GZnC and GFeC in three or more datasets, which were located on all wheat chromosomes except 3A and 7D. Among them, 29 markers were associated with both GZnC and GFeC, indicating a shared genetic basis for these micronutrients and the possibility of simultaneously improving both. In addition, several significant GZnC and GFeC associated markers were common across the irrigated, water-limiting drought-stressed, and space-limiting small plots environments, thereby indicating the feasibility of indirect selection for these micronutrients in either of these environments. Moreover, the many significant markers identified had minor effects on GZnC and GFeC, suggesting a quantitative genetic control of these traits. Our findings provide important insights into the complex genetic basis of GZnC and GFeC in bread wheat while implying limited prospects for marker-assisted selection and the need for using genomic selection.

Spot blotch caused by the fungus Bipolaris sorokiniana poses a serious threat to bread wheat production in warm and humid wheat-growing regions of the world. Hence, the major objective of this study was to identify consistent genotyping-by-sequencing (GBS) markers associated with spot blotch resistance using genome-wide association mapping on a large set of 6,736 advanced bread wheat breeding lines from the International Maize and Wheat Improvement Center. These lines were phenotyped as seven panels at Agua Fria, Mexico between the 2013–2014 and 2019–2020 crop cycles. We identified 214 significant spot blotch associated GBS markers in all the panels, among which only 96 were significant in more than one panel, indicating a strong environmental effect on the trait and highlights the need for multiple phenotypic evaluations to identify lines with stable spot blotch resistance. The 96 consistent GBS markers were on chromosomes 1A, 1B, 1D, 2A, 3B, 4A, 5B, 5D, 6B, 7A, 7B, and 7D, including markers possibly linked to the Lr46, Sb1, Sb2 and Sb3 genes. We also report the association of the 2NS translocation from Aegilops ventricosa with spot blotch resistance in some environments. Moreover, the spot blotch favorable alleles at the 2NS translocation and two markers on chromosome 3BS (3B_2280114 and 3B_5601689) were associated with increased grain yield evaluated at several environments in Mexico and India, implying that selection for favorable alleles at these loci could enable simultaneous improvement for high grain yield and spot blotch resistance. Furthermore, a significant relationship between the percentage of favorable alleles in the lines and their spot blotch response was observed, which taken together with the multiple minor effect loci identified to be associated with spot blotch in this study, indicate quantitative genetic control of resistance. Overall, the results presented here have extended our knowledge on the genetic basis of spot blotch resistance in bread wheat and further efforts to improve genetic resistance to the disease are needed for reducing current and future losses under climate change.

Biofortification of cereal grains offers a lasting solution to combat micronutrient deficiency in developing countries where it poses developmental risks to children. Breeding efforts thus far have been directed toward increasing the grain concentrations of iron (Fe) and zinc (Zn) ions. Phytic acid (PA) chelates these metal ions, reducing their bioavailability in the digestive tract. We present a high-throughput assay for quantification of PA and its application in screening a breeding population. After extraction in 96-well megatiter plates, PA content was determined from the phosphate released after treatment with a commercially available phytase enzyme. In a set of 330 breeding lines of wheat grown in the field over 3 years as part of a HarvestPlus breeding program for high grain Fe and Zn, our assay unraveled variation for PA that ranged from 0.90 to 1.72% with a mean of 1.24%. PA content was not associated with grain yield. High yielding lines were further screened for low molar PA/Fe and PA/Zn ratios for increased metal ion bioavailability, demonstrating the utility of our assay. Genome-wide association study revealed 21 genetic associations, six of which were consistent across years. Five of these associations mapped to chromosomes 1A, 2A, 2D, 5A, and 7D. Additivity over four of these haplotypes accounted for an ∼10% reduction in PA. Our study demonstrates it is possible to scale up assays to directly select for low grain PA in forward breeding programs.

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.

A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from −0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

Wheat (Triticum aestivum L.) is the most widely grown cereal crop in the world and is staple food to half the world’s population. The current world population is expected to reach 9.8 billion people by 2050, but food production is not expected to keep pace with demand in developing countries. Significant opportunities exist for traditional grain exporters to produce and export greater amounts of wheat to fill the gap. Karnal bunt, however, is a major threat, due to its use as a non-tariff trade barrier by several wheat-importing countries. The cultivation of resistant varieties remains the most cost-effective approach to manage the disease, but in countries that are free of the disease, genetic improvement is difficult due to quarantine restrictions. Here we report a study on pre-emptive breeding designed to identify linked molecular markers, evaluate the prospects of genomic selection as a tool, and prioritise wheat genotypes suitable for use as parents. In a genome-wide association (GWAS) study, we identified six DArTseq markers significantly linked to Karnal bunt resistance, which explained between 7.6 and 29.5% of the observed phenotypic variation. The accuracy of genomic prediction was estimated to vary between 0.53 and 0.56, depending on whether it is based solely on the identified Quantitative trait loci (QTL) markers or the use of genome-wide markers. As genotypes used as parents would be required to possess good yield and phenology, further research was conducted to assess the agronomic value of Karnal bunt resistant germplasm from the International Maize and Wheat Improvement Center (CIMMYT). We identified an ideal genotype, ZVS13_385, which possessed similar agronomic attributes to the highly successful Australian wheat variety, Mace. It is phenotypically resistant to Karnal bunt infection (

The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.

The greenbug, Schizaphis graminum Rondani, significantly reduces wheat, Triticum aestivum L., grain yields if not controlled. Host plant resistance (HPR) can protect yield, is environmentally friendly and easy to use. Our objectives were to: (1) identify genomic regions associated with S. graminum resistance in a recombinant inbred line (RIL) population derived from a cross of “Sokoll” (resistant) and “Weebill1” (moderately susceptible), (2) evaluate Sokoll derived breeding germplasm for resistance, and (3) conduct allelism tests between Sokoll and sources carrying resistance genes Gba, Gbb, and Gbd. Resistance was measured quantitatively and qualitatively using a SPAD meter and visual assessments, respectively. We identified a large effect resistance gene on chromosome arm 7DL of Sokoll, herein referred as GbSkl, which contributed up to 24% of the phenotypic variation. Other minor QTL on chromosomes 2B, 3A, and 7B were also identified. The QTL on 2B and 3A originated from Weebill1. Of the Sokoll derived germplasm, 13% displayed resistance. Allelism tests indicated that GbSkl could be allelic or tightly linked to the temporarily designated genes Gba, Gbb, and Gbd. Utility of SPAD to determine quantitative variation in resistance phenotyping is demonstrated and breeding efforts are underway to transfer the resistance from Sokoll to new CIMMYT elite germplasm.

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

The recent availability of an assembled and annotated genome reference sequence for the diploid crop barley (Hordeum vulgare L.) provides new opportunities to study the genetic basis of agronomically important traits such as resistance to stripe [Puccinia striiformis f. sp. hordei (Psh)], leaf [P. hordei (Ph)], and stem [P. graminis f. sp. tritici (Pgt)] rust diseases. The European barley cultivar Pompadour is known to possess high levels of resistance to leaf rust, predominantly due to adult plant resistance (APR) gene Rph20. We developed a barley recombinant inbred line (RIL) population from a cross between Pompadour and the leaf rust and stripe rust susceptible selection Biosaline-19 (B-19), and genotyped this population using DArT-Seq genotyping by sequencing (GBS) markers. In the current study, we produced a high-density linkage map comprising 8,610 (SNP and in silico) markers spanning 5957.6 cM, with the aim of mapping loci for resistance to leaf rust, stem rust, and stripe rust. The RIL population was phenotyped in the field with Psh (Mexico and Ecuador) and Ph (Australia) and in the greenhouse at the seedling stage with Australian Ph and Pgt races, and at Wageningen University with a European variant of Psh race 24 (PshWUR). For Psh, we identified a consistent field QTL on chromosome 2H across all South American field sites and years. Two complementary resistance genes were mapped to chromosomes 1H and 4H at the seedling stage in response to PshWUR, likely to be the loci rpsEm1 and rpsEm2 previously reported from the cultivar Emir from which Pompadour was bred. For leaf rust, we determined that Rph20 in addition to two minor-effect QTL on 1H and 3H were effective at the seedling stage, whilst seedling resistance to stem rust was due to QTL on chromosomes 3H and 7H conferred by Pompadour and B-19, respectively.

Breeding wheat with enhanced levels of grain zinc (Zn) and iron (Fe) is a cost-effective, sustainable solution to malnutrition problems. Modern wheat varieties have limited variation in grain Zn and Fe, but large-scale screening has identified high levels of Zn and Fe in wild relatives and progenitors of cultivated wheat. The most promising sources of high Zn and Fe are einkorn (Triticum monococcum), wild emmer (T. dicoccoides), diploid progenitors of hexaploid wheat (such as Aegilops tauschii), T. spelta, T. polonicum, and landraces of T. aestivum. This study evaluate the effects of translocations from rye and different Aegilops species in a “Pavon-76” wheat genetic background and utilized in the wheat biofortification breeding program at CIMMYT that uses diverse genetic resources, including landraces, recreated synthetic hexaploids, T. spelta and pre-breeding lines. Four translocations were identified that resulted significantly higher Zn content in “Pavon 76” genetic background than the check varieties, and they had increased levels of grain Fe as well-compared to “Pavon 76.” These lines were also included in the breeding program aimed to develop advanced high Zn breeding lines. Advanced lines derived from diverse crosses were screened under Zn-enriched soil conditions in Mexico during the 2017 and 2018 seasons. The Zn content of the grain was ranging from 35 to 69 mg/kg during 2017 and 38 to 72 mg/kg during 2018. Meanwhile grain Fe ranged from 30 to 43 mg/kg during 2017 and 32 to 52 mg/kg during 2018. A highly significant positive correlation was found between Zn and Fe (r = 0.54; P < 0.001) content of the breeding lines, therefore it was possible to breed for both properties in parallel. Yield testing of the advanced lines showed that 15% (2017) and 24% (2018) of the lines achieved 95–110% yield potential of the commercial checks and also had 12 mg/kg advantage in the Zn content suggesting that greater genetic gains and farmer-preferred wheat varieties were developed and deployed. A decade of research and breeding efforts led to the selection of “best-bet” breeding lines and the release of eight biofortified wheat varieties in target regions of South Asia and in Mexico.

Races belonging to Ug99 lineage of stem rust fungus Pucciniagraminis f. sp. tritici (Pgt) continue to pose a threat to wheat (Triticumaestivum L.) production in various African countries. Growing resistant varieties is the most economical and environmentally friendly control measure. Recombinant inbred line (RIL) populations from the crosses of susceptible parent ‘Cacuke’ with the resistant parents ‘Huhwa’ and ‘Yaye’ were phenotyped for resistance at the seedling stage to Pgt race TTKSK (Ug99) and in adult plants in field trials at Njoro, Kenya for two seasons in 2016. Using the Affymetrix Axiom breeders SNP array, two stem rust resistance genes, temporarily designated as SrH and SrY, were identified and mapped on chromosome arm 2BL through selective genotyping and bulked segregant analysis (BSA), respectively. Kompetitive allele specific polymorphism (KASP) markers and simple sequence repeat (SSR) markers were used to saturate chromosome arm 2BL in both RIL populations. SrH mapped between markers cim109 and cim114 at a distance of 0.9 cM proximal, and cim117 at 2.9 cM distal. SrY was flanked by markers cim109 and cim116 at 0.8 cM proximal, and IWB45932 at 1.9 cM distal. Two Ug99-effective stem rust resistance genes derived from bread wheat, Sr9h and Sr28, have been reported on chromosome arm 2BL. Infection types and map position in Huhwa and Yaye indicated that Sr28 was absent in both the parents. However, susceptible reactions produced by resistant lines from both populations against Sr9h-virulent race TTKSF+ confirmed the presence of a common resistance locus Sr9h in both lines. Test of allelism is required to establish genetic relationships between genes identified in present study and Sr9h. Marker cim117 linked to SrH was genotyped on set of wheat lines with Huhwa in the pedigree and is advised to be used for marker assisted selection for this gene, however, a combination of phenotypic and genotypic assays is desirable for both genes especially for selection of Sr9h in breeding programs.

Karnal bunt (KB) of wheat, caused by Tilletia indica, is one of the greatest challenges to grain industry, not because of yield loss, but quarantine regulations that restrict international movement and trade of affected stocks. Genetic resistance is the best way to manage this disease. Although several different sources of resistance have been identified to date, very few of those have been subjected to genetic analyses. Understanding the genetics of resistance, characterization and mapping of new resistance loci can help in development of improved germplasm. The objective of this study was to identify and characterize resistance loci (QTL) in two independent recombinant inbred lines (RILs) populations utilizing different wheat lines as resistance donors. Elite CIMMYT wheat lines Blouk#1 and Huirivis#1 were used as susceptible female parents and WHEAR/KUKUNA/3/C80.1/3∗BATAVIA//2∗WBLL1 (WKCBW) and Mutus as moderately resistant male parents in Pop1 and Pop2 populations, respectively. Populations were evaluated for KB resistance in 2015–16 and 2016–17 cropping seasons at two seeding dates (total four environments) in Cd. Obregon, Mexico. Two stable QTL from each population were identified in each environment: QKb.cim-2B and QKb.cim-3D (Pop1), QKb.cim-3B1 and QKb.cim-5B2 (Pop2). Other than those four QTL, other QTL were detected in each population which were specific to environments: QKb.cim-5B1, QKb.cim-6A, and QKb.cim-7A (Pop1), QKb.cim-3B2, QKb.cim-4A1, QKb.cim-4A2, QKb.cim-4B, QKb.cim-5A1, QKb.cim-5A2, and QKb.cim-7A2 (Pop2). Among the four stable QTL, all but QKb.cim-3B1 were derived from the resistant parent. QKb.cim-2B and QKb.cim-3D in Pop1 and QKb.cim-3B1 and QKb.cim-5B2 in Pop2 explained 5.0–11.4% and 3.3–7.1% phenotypic variance, respectively. A combination of two stable QTL in each population reduced KB infection by 24–33%, respectively. Transgressive resistant segregants lines derived with resistance alleles from both parents in each population were identified. Single nucleotide polymorphism (SNP) markers flanking these QTL regions may be amenable to marker-assisted selection. The best lines from both populations (in agronomy, end-use quality and KB resistance) carrying resistance alleles at all identified loci, may be used for inter-crossing and selection of improved germplasm in future. Markers flanking these QTL may assist in selection of such lines.

More than 50% of undernourished children live in Asia and more than 25% live in Africa. Coupled with an inadequate food supply, mineral deficiencies are widespread in these populations; particularly zinc (Zn) and iron (Fe) deficiencies that lead to retarded growth, adverse effects on both the immune system and an individual's cognitive abilities. Biofortification is one solution aimed at reducing the incidence of these deficiencies. To efficiently breed a biofortified wheat variety, it is important to generate knowledge of the genomic regions associated with grain Zn (GZn) and Fe (GFe) concentration. This allows for the introgression of favorable alleles into elite germplasm. In this study we evaluated two bi-parental populations of 188 recombinant inbred lines (RILs) displaying a significant range of transgressive segregation for GZn and GFe during three crop cycles in CIMMYT, Mexico. Parents of the RILs were derived from Triticum spelta L. and synthetic hexaploid wheat crosses. QTL analysis identified a number of significant QTL with a region denominated as QGZn.cimmyt-7B_1P2 on chromosome 7B explaining the largest (32.7%) proportion of phenotypic variance (PVE) for GZn and leading to an average additive effect of −1.3. The QTL with the largest average additive effect for GFe (−0.161) was found on chromosome 4A (QGFe.cimmyt-4A_P2), with 21.14% of the PVE. The region QGZn.cimmyt-7B_1P2 co-localized closest to the region QGZn.cimmyt-7B_1P1 in a consensus map built from the linkage maps of both populations. Pleiotropic or tightly linked QTL were also found on chromosome 3B, however of minor effects and PVE between 4.3 and 10.9%. Further efforts are required to utilize the QTL information in marker assisted backcrossing schemes for wheat biofortification. A strategy to follow is to intercross the transgressive individuals from both populations and then utilize them as sources in biofortification breeding pipelines.

Growing resistant wheat varieties is a key method of minimizing the extent of yield losses caused by the globally important wheat leaf rust (LR) and stripe rust (YR) diseases. In this study, a population of 186 F8 recombinant inbred lines (RILs) derived from a cross between a synthetic wheat derivative (PI610750) and an adapted common wheat line (cv. “UC1110”) were phenotyped for LR and YR response at both seedling and adult plant stages over multiple seasons. Using a genetic linkage map consisting of single sequence repeats and diversity arrays technology markers, in combination with inclusive composite interval mapping analysis, we detected a new LR adult plant resistance (APR) locus, QLr.cim-2DS, contributed by UC1110. One co-located resistance locus to both rusts, QLr.cim-3DC/QYr.cim-3DC, and the known seedling resistance gene Lr26 were also mapped. QLr.cim-2DS and QLr.cim-3DC showed a marginally significant interaction for LR resistance in the adult plant stage. In addition, two previously reported YR APR loci, QYr.ucw-3BS and Yr48, were found to exhibit stable performances in rust environments in both Mexico and the United States and showed a highly significant interaction in the field. Yr48 was also observed to confer intermediate seedling resistance against Mexican YR races, thus suggesting it should be re-classified as an all-stage resistance gene. We also identified 5 and 2 RILs that possessed all detected YR and LR resistance loci, respectively. With the closely linked molecular markers reported here, these RILs could be used as donors for multiple resistance loci to both rusts in wheat breeding programs.

CIMMYT wheat (Triticum aestivum L.) lines Francolin#1 and Quaiu#3 displayed effective and stable adult plant resistance (APR) to Chinese Blumeria graminis f. sp. tritici isolates in the field. To elucidate their genetic basis of resistance, two recombinant inbred line (RIL) populations of their crosses with Avocet, the susceptible parent, were phenotyped in Zhengzhou and Shangqiu in the 2014–2015 and 2015–2016 cropping seasons. These populations were also genotyped with SSR (simple sequence repeat markers) and DArT (diversity arrays technology) markers. Two common significant quantitative trait loci (QTL) on wheat chromosomes 1BL and 4BL were detected in both populations by joint and individual inclusive composite interval mapping, explaining 20.3–28.7% and 9.6–15.9% of the phenotypic variance in Avocet × Francolin#1 and 4.8–11.5% and 10.8–18.9% in Avocet × Quaiu#3, respectively. Additional QTL were mapped on chromosomes 1DL and 5BL in Avocet × Francolin#1 and on 2DL and 6BS in Avocet × Quaiu#3. Among these, QPm.heau-1DL is probably a novel APR gene contributing 6.1–8.5% of total phenotypic variance. The QTL on 1BL corresponds to the pleiotropic multi-pathogen resistance gene Yr29/Lr46/Pm39, whereas the QTL on 2DL maps to a similar region where stripe rust resistance gene Yr54 is located. The QTL identified can potentially be used for the improvement of powdery mildew and rust resistance in wheat breeding.

Spring bread wheat adaptation to diverse environments is supported by various traits such as phenology and plant architecture. A large-scale genome-wide association study (GWAS) was designed to investigate and dissect the genetic architecture of phenology affecting adaptation. It used 48 datasets from 4,680 spring wheat lines. For 8 years (2014–2021), these lines were evaluated for days to heading (DH) and maturity (DM) at three sites: Jabalpur, Ludhiana, and Samastipur (Pusa), which represent the three major Indian wheat-producing zones: the Central Zone (CZ), North-Western Plain Zone (NWPZ), and North-Eastern Plain Zone (NEPZ), respectively. Ludhiana had the highest mean DH of 103.8 days and DM of 148.6 days, whereas Jabalpur had the lowest mean DH of 77.7 days and DM of 121.6 days. We identified 119 markers significantly associated with DH and DM on chromosomes 5B (76), 2B (18), 7D (10), 4D (8), 5A (1), 6B (4), 7B (1), and 3D (1). Our results clearly indicated the importance of the photoperiod-associated gene (Ppd-B1) for adaptation to the NWPZ and the Vrn-B1 gene for adaptation to the NEPZ and CZ. A maximum variation of 21.1 and 14% was explained by markers 2B_56134146 and 5B_574145576 linked to the Ppd-B1 and Vrn-B1 genes, respectively, indicating their significant role in regulating DH and DM. The results provide important insights into the genomic regions associated with the two phenological traits that influence adaptation to the major wheat-producing zones in India.

Wheat (Triticum aestivum L.) is one of the major staples in Nepal providing the bulk of food calories and at least 30% of Fe and Zn intake and 20% of dietary energy and protein consumption; thus, it is essential to improve its nutritional quality. To select high-yielding genotypes with elevated grain zinc and iron concentration, the sixth, seventh, eighth, and ninth HarvestPlus Yield Trials (HPYTs) were conducted across diverse locations in Nepal for four consecutive years: 2015–16, 2016–17, 2017–18, and 2018–19, using 47 biofortified and 3 non-biofortified CIMMYT-bred, bread wheat genotypes: Baj#1, Kachu#1, and WK1204 (local check). Genotypic and spatial variations were found in agro-morphological traits; grain yield and its components; and the grain zinc and iron concentration of tested genotypes. Grain zinc concentration was highest in Khumaltar and lowest in Kabre. Likewise, grain iron concentration was highest in Doti and lowest in Surkhet. Most of the biofortified genotypes were superior for grain yield and for grain zinc and iron concentration to the non-biofortified checks. Combined analyses across environments showed moderate to high heritability for both Zn (0.48–0.81) and Fe (0.46–0.79) except a low heritability for Fe observed for 7th HPYT (0.15). Grain yield was positively correlated with the number of tillers per m2, while negatively correlated with days to heading and maturity, grain iron, grain weight per spike, and thousand grain weight. The grain zinc and iron concentration were positively correlated, suggesting that the simultaneous improvement of both micronutrients is possible through wheat breeding. Extensive testing of CIMMYT derived high Zn wheat lines in Nepal led to the release of five biofortified wheat varieties in 2020 with superior yield, better disease resistance, and 30–40% increased grain Zn and adaptable to a range of wheat growing regions in the country – from the hotter lowland, or Terai, regions to the dry mid- and high-elevation areas.

Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers’ risk from year-to-year environmental variation in India’s wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.

Wheat (Triticum aestivum L.) crop health assumes unprecedented significance in being the second most important staple crop of the world. It is host to an array of fungal pathogens attacking the plant at different developmental stages and accrues various degrees of yield losses owing to these. Tilletia indica that causes Karnal bunt (KB) disease in wheat is one such fungal pathogen of high quarantine importance restricting the free global trade of wheat besides the loss of grain yield as well as quality. With global climate change, the disease appears to be shifting from its traditional areas of occurrence with reports of increased vulnerabilities of new areas across the continents. This KB vulnerability of new geographies is of serious concern because once established, the disease is extremely difficult to eradicate and no known instance of its complete eradication using any management strategy has been reported yet. The host resistance to KB is the most successful as well as preferred strategy for its mitigation and control. However, breeding of KB resistant wheat cultivars has proven to be not so easy, and the low success rate owes to the scarcity of resistance sources, extremely laborious and regulated field screening protocols delaying identification/validation of putative resistance sources, and complex quantitative nature of resistance with multiple genes conferring only partial resistance. Moreover, given a lack of comprehensive understanding of the KB disease epidemiology, host-pathogen interaction, and pathogen evolution. Here, in this review, we attempt to summarize the progress made and efforts underway toward a holistic understanding of the disease itself with a specific focus on the host-pathogen interaction between T. indica and wheat as key elements in the development of resistant germplasm. In this context, we emphasize the tools and techniques being utilized in development of KB resistant germplasm by illuminating upon the genetics concerning the host responses to the KB pathogen including a future course. As such, this article could act as a one stop information primer on this economically important and re-emerging old foe threatening to cause devastating impacts on food security and well-being of communities that rely on wheat.

Genomic breeding technologies offer new opportunities for grain yield (GY) improvement in common wheat. In this study, we have evaluated the potential of genomic selection (GS) in breeding for GY in wheat by modeling a large dataset of 48,562 GY observations from the International Maize and Wheat Improvement Center (CIMMYT), including 36 yield trials evaluated between 2012 and 2019 in Obregón, Sonora, Mexico. Our key objective was to determine the value that GS can add to the current three-stage yield testing strategy at CIMMYT, and we draw inferences from predictive modeling of GY using 420 different populations, environments, cycles, and model combinations. First, we evaluated the potential of genomic predictions for minimizing the number of replications and lines tested within a site and year and obtained mean prediction accuracies (PAs) of 0.56, 0.5, and 0.42 in Stages 1, 2, and 3 of yield testing, respectively. However, these PAs were similar to the mean pedigree-based PAs indicating that genomic relationships added no value to pedigree relationships in the yield testing stages, characterized by small family-sizes. Second, we evaluated genomic predictions for minimizing GY testing across stages/years in Obregón and observed mean PAs of 0.41, 0.31, and 0.37, respectively when GY in the full irrigation bed planting (FI BP), drought stress (DS), and late-sown heat stress environments were predicted across years using genotype × environment (G × E) interaction models. Third, we evaluated genomic predictions for minimizing the number of yield testing environments and observed that in Stage 2, the FI BP, full irrigation flat planting and early-sown heat stress environments (mean PA of 0.37 ± 0.12) and the reduced irrigation and DS environments (mean PA of 0.45 ± 0.07) had moderate predictabilities among them. However, in both predictions across years and environments, the PAs were inconsistent across years and the G × E models had no advantage over the baseline model with environment and line effects. Overall, our results provide excellent insights into the predictability of a quantitative trait like GY and will have important implications on the future design of GS for GY in wheat breeding programs globally.

Tan spot caused by Pyrenophora tritici-repentis (Ptr) is an important disease of wheat in many wheat producing areas of the world. A genome wide association study (GWAS) was conducted using 11,401 SNP markers of the Illumina Infinium 15K Bead Chip with whole genome coverage to identify genomic regions associated with resistance to tan spot in a diverse panel of 184 wheat genotypes originating from South Asia and CIMMYT. The GWAS panel was phenotyped for seedling resistance to tan spot with Ptr race 1 in two greenhouse experiments. Besides CIMMYT germplasm, several lines from South Asia (India, Bangladesh and Nepal) showed good degree of resistance to tan spot. Association mapping was conducted separately for individual experiments and for pooled data using mixed linear model (MLM) and Fixed and random model Circulating Probability Unification (FarmCPU) model; no significant MTAs were recorded through the MLM model, whereas FarmCPU model reported nine significant MTAs located on chromosomes 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, and 7D. The long arms of chromosomes 5A and 5B were consistent across both environments, in which the Vrn-A1 locus was found in identified region of chromosome 5A, and MTA at IACX9261 on 5BL appears to represent the resistance gene tsn 1. MTAs observed on chromosomes 1B, 2A, 2B, 3B, 4A, 6A, and 7D have not been reported previously and are likely novel.

Breeding for grain yield (GY) in bread wheat at the International Maize and Wheat Improvement Center (CIMMYT) involves three-stage testing at Obregon, Mexico in different selection environments (SEs). To understand the efficiency of selection in the SEs, we performed a large retrospective quantitative genetics study using CIMMYT’s yield trials evaluated in the SEs (2013–2014 to 2017–2018), the South Asia Bread Wheat Genomic Prediction Yield Trials (SABWGPYTs) evaluated in India, Pakistan, and Bangladesh (2014–2015 to 2017–2018), and the Elite Spring Wheat Yield Trials (ESWYTs) evaluated in several sites globally (2003–2004 to 2016–2017). First, we compared the narrow-sense heritabilities in the Obregon SEs and target sites and observed that the mean heritability in the SEs was 44.2 and 92.3% higher than the mean heritabilities in the SABWGPYT and ESWYT sites, respectively. Second, we observed significant genetic correlations between a SE in Obregon and all the five SABWGPYT sites and 65.1% of the ESWYT sites. Third, we observed high ratios of response to indirect selection in the SEs of Obregon with a mean of 0.80 ± 0.21 and 2.6 ± 5.4 in the SABWGPYT and ESWYT sites, respectively. Furthermore, our results also indicated that for all the SABWGPYT sites and 82% of the ESWYT sites, a response greater than 0.5 can be achieved by indirect selection for GY in Obregon. We also performed genomic prediction for GY in the target sites using the performance of the same lines in the SEs of Obregon and observed moderate mean prediction accuracies of 0.24 ± 0.08 and 0.28 ± 0.08 in the SABWGPYT and ESWYT sites, respectively using the genotype x environment (GxE) model. However, we observed similar accuracies using the baseline model with environment and line effects and no advantage of modeling GxE interactions. Overall, this study provides important insights into the suitability of the Obregon SEs in breeding for GY, while the variable genomic predictabilities of GY and the high year-to-year GY fluctuations reported, highlight the importance of multi-environment testing across time and space to stave off GxE induced uncertainties in varietal yields.

Rusts, a fungal disease as old as its host plant wheat, an enemy as old as wheat, has caused havoc for over 8,000 years. As the rust pathogens can evolve into new virulent races which quickly defeat to qualitative or vertical the resistance that primarily rely on race specificity over time, adult plant resistance (APR) has often been found to be race non-specific and hence is considered have been proven to be a more to be a more reliable and durable strategy to combat this malady. Over decades sets of donor lines have been identified at International Maize and Wheat Improvement Center (CIMMYT) representing a wide range of APR sources in wheat. In this study, using nine donors and a common parent ‘PBW343’, a popular Green Revolution variety at CIMMYT, the nested association mapping (NAM) population of 1122 lines was constructed to understand the APR genetics underlying these founder lines. Thirty-four QTL were associated with APR to rusts, and 20 of 34 QTL had pleiotropic effects on SR, YR and LR resistance. Three chromosomal regions, associated with known APR genes (Sr58/Yr29/Lr46, Sr2/Yr30/Lr27, and Sr57/Yr18/Lr34), were also identified, 13 previously reported QTL regions were validated. Of the 18 QTL first detected in this study, 7 were pleiotropic QTL, distributing on chromosomes 3A, 3B, 6B, 3D, and 6D. The present investigation revealed the genetic relationship of historical APR donor lines, the novel knowledge on APR, as well as the new analytical methodologies to facilitate the applications of NAM design in crop genetics. Results shown in this study will aid the parental selection for hybridization in wheat breeding, and envision the future rust management breeding for addressing potential threat to wheat production and food security.