Genomic Regions Associated with Resistance to Three Rusts in CIMMYT Wheat Line "Mokue#1".

Understanding the genetic basis of rust resistance in elite CIMMYT wheat germplasm enhances breeding and deployment of durable resistance globally. "Mokue#1", released in 2023 in Pakistan as TARNAB Gandum-1, has exhibited high levels of resistance to stripe rust, leaf rust, and stem rust pathotypes present at multiple environments in Mexico and Kenya at different times. To determine the genetic basis of resistance, a F5 recombinant inbred line (RIL) mapping population consisting of 261 lines was developed and phenotyped for multiple years at field sites in Mexico and Kenya under the conditions of artificially created rust epidemics. DArTSeq genotyping was performed, and a linkage map was constructed using 7892 informative polymorphic markers. Composite interval mapping identified three significant and consistent loci contributed by Mokue: QLrYr.cim-1BL and QLrYr.cim-2AS on chromosome 1BL and 2AS, respectively associated with stripe rust and leaf rust resistance, and QLrSr.cim-2DS on chromosome 2DS for leaf rust and stem rust resistance. The QTL on 1BL was confirmed to be the Lr46/Yr29 locus, whereas the QTL on 2AS represented the Yr17/Lr37 region on the 2NS/2AS translocation. The QTL on 2DS was a unique locus conferring leaf rust resistance in Mexico and stem rust resistance in Kenya. In addition to these pleiotropic loci, four minor QTLs were also identified on chromosomes 2DL and 6BS associated with stripe rust, and 3AL and 6AS for stem rust, respectively, using the Kenya disease severity data. Significant decreases in disease severities were also demonstrated due to additive effects of QTLs when present in combinations.

A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments.

The incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a large-scale genomic association analysis with high-density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the high-confidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult-plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker-assisted breeding. These results demonstrate that high-resolution SNP-based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker-assisted selection in wheat disease resistance breeding.

Characterization of QTLs for Seedling Resistance to Tan Spot and Septoria Nodorum Blotch in the PBW343/Kenya Nyangumi Wheat Recombinant Inbred Lines Population.

Tan spot (TS) and Septoria nodorum blotch (SNB) induced by Pyrenophora tritici-repentis and Parastagonospora nodorum, respectively, cause significant yield losses and adversely affect grain quality. The objectives of this study were to decipher the genetics and map the resistance to TS and SNB in the PBW343/Kenya Nyangumi (KN) population comprising 204 F6 recombinant inbred lines (RILs). Disease screening was performed at the seedling stage under greenhouse conditions. TS was induced by P. tritici-repentis isolate MexPtr1 while SNB by P. nodorum isolate MexSN1. Segregation pattern of the RILs indicated that resistance to TS and SNB in this population was quantitative. Diversity Array Technology (DArTs) and simple sequence repeats (SSRs) markers were used to identify the quantitative trait loci (QTL) for the diseases using inclusive composite interval mapping (ICIM). Seven significant additive QTLs for TS resistance explaining 2.98 to 23.32% of the phenotypic variation were identified on chromosomes 1A, 1B, 5B, 7B and 7D. For SNB, five QTLs were found on chromosomes 1A, 5A, and 5B, explaining 5.24 to 20.87% of the phenotypic variation. The TS QTL on 1B chromosome coincided with the pleiotropic adult plant resistance (APR) gene Lr46/Yr29/Pm39. This is the first report of the APR gene Lr46/Yr29/Pm39 contributing to TS resistance.

Resistance to Spot Blotch in Two Mapping Populations of Common Wheat Is Controlled by Multiple QTL of Minor Effects.

Spot blotch (SB) is an important fungal disease of wheat in South Asia and South America. Host resistance is regarded as an economical and environmentally friendly approach of controlling SB, and the inheritance of resistance is mostly quantitative. In order to gain a better understanding on the SB resistance mechanism in CIMMYT germplasm, two bi-parental mapping populations were generated, both comprising 232 F2:7 progenies. Elite CIMMYT breeding lines, BARTAI and WUYA, were used as resistant parents, whereas CIANO T79 was used as susceptible parent in both populations. The two populations were evaluated for field SB resistance at CIMMYT's Agua Fria station for three consecutive years, from the 2012⁻2013 to 2014⁻2015 cropping seasons. Phenological traits like plant height (PH) and days to heading (DH) were also determined. Genotyping was performed using the DArTSeq genotyping-by-sequencing (GBS) platform, and a few D-genome specific SNPs and those for phenological traits were integrated for analysis. The most prominent quantitative trait locus (QTL) in both populations was found on chromosome 5AL at the Vrn-A1 locus, explaining phenotypic variations of 7⁻27%. Minor QTL were found on chromosomes 1B, 3A, 3B, 4B, 4D, 5B and 6D in BARTAI and on chromosomes 1B, 2A, 2D and 4B in WUYA, whereas minor QTL contributed by CIANO T79 were identified on chromosome 1B, 1D, 3A, 4B and 7A. In summary, resistance to SB in the two mapping populations was controlled by multiple minor QTL, with strong influence from Vrn-A1.

A high density GBS map of bread wheat and its application for dissecting complex disease resistance traits.

BACKGROUND: Genotyping-by-sequencing (GBS) is a high-throughput genotyping approach that is starting to be used in several crop species, including bread wheat. Anchoring GBS tags on chromosomes is an important step towards utilizing them for wheat genetic improvement. Here we use genetic linkage mapping to construct a consensus map containing 28644 GBS markers. RESULTS: Three RIL populations, PBW343 × Kingbird, PBW343 × Kenya Swara and PBW343 × Muu, which share a common parent, were used to minimize the impact of potential structural genomic variation on consensus-map quality. The consensus map comprised 3757 unique positions, and the average marker distance was 0.88 cM, obtained by calculating the average distance between two adjacent unique positions. Significant variation of segregation distortion was observed across the three populations. The consensus map was validated by comparing positions of known rust resistance genes, and comparing them to wheat reference genome sequences recently published by the International Wheat Genome Sequencing Consortium, Rye and Ae. tauschii genomes. Three well-characterized rust resistance genes (Sr58/Lr46/Yr29, Sr2/Yr30/Lr27, and Sr57/Lr34/Yr18) and 15 published QTLs for wheat rusts were validated with high resolution. Fifty-two per cent of GBS tags on the consensus map were successfully aligned through BLAST to the right chromosomes on the wheat reference genome sequence. CONCLUSION: The consensus map should provide a useful basis for analyzing genome-wide variation of complex traits. The identified genes can then be explored as genetic markers to be used in genomic applications in wheat breeding.

Dissecting the Genetic Architecture of Phenology Affecting Adaptation of Spring Bread Wheat Genotypes to the Major Wheat-Producing Zones in India.

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.

Variation in Grain Zinc and Iron Concentrations, Grain Yield and Associated Traits of Biofortified Bread Wheat Genotypes in Nepal.

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.

Field induced hysteretic structural phase switching and possible CDW in Re-doped MoTe2.

Novel electronic systems displaying exotic physical properties can be derived from complex topological materials through chemical doping. MoTe2, the candidate type-II Weyl semimetal shows dramatically enhanced superconductivity up to 4.1 K upon Re doping in Mo sites. Based on bulk transport and local scanning tunnelling microscopy here we show that Re doping also leads to the emergence of a possible charge density wave phase in Re0.2Mo0.8Te2. In addition, the tunnellingI-Vcharacteristics display non-linearity and hysteresis which is commensurate with a hysteresis observed in the change in tip-height (z) as a function of applied voltageV. The observations indicate an electric field induced hysteretic switching consistent with piezoelectricity and possible ferroelectricity.

Identification of bread wheat genotypes with superior grain yield and agronomic traits through evaluation under rust epiphytotic conditions in Kenya.

Bread wheat (Triticum aestivum L.) cultivars adapted to specific environments and resistant to prevalent pathogens are preferred for obtaining high yield. This study aimed to identify wheat genotypes with superior grain yield (GY) and yield associated traits from 168 genotypes of International Maize and Wheat Improvement Center's 13th Stem Rust Resistance Screening Nursery evaluated over two seasons during 2019 and 2020 under high disease pressure of both stem rust (SR) and yellow rust (YR) in a 21 × 8 α-lattice design with 3 replications in Kenya. Effects due to seasons were significant for YRAud, SRAud, 1000-kernel weight (TKW), days to heading (DH), plant height (PH) and number of spikelets spike-1 (SS), while genotypes and genotypes × season interaction effects were significant for all traits except number of kernels spike-1. Respectively, heritability values of 0.95, 0.93, 0.87, 0.86, 0.77 and 0.75 were observed for area under disease progress curve for SR (SRAud), YR (YRAud), TKW, DH, biomass (BM) and GY. Path analysis showed positive direct effects on GY via PH, SS, BM, and TKW. Biplot analysis identified 16 genotypes with superior desirable traits GY, BM and harvest index. The SR contributed the highest reduction in GY and TKW while YR contributed the most reduction in BM. These identified genotypes with superior GY combined with adequate resistance to both SR and YR are potentially valuable resources for improvement of locally adapted wheat cultivars.

Elucidating SNP-based genetic diversity and population structure of advanced breeding lines of bread wheat (Triticum aestivum L.).

Genetic diversity and population structure information are crucial for enhancing traits of interest and the development of superlative varieties for commercialization. The present study elucidated the population structure and genetic diversity of 141 advanced wheat breeding lines using single nucleotide polymorphism markers. A total of 14,563 high-quality identified genotyping-by-sequencing (GBS) markers were distributed covering 13.9 GB wheat genome, with a minimum of 1,026 SNPs on the homoeologous group four and a maximum of 2,838 SNPs on group seven. The average minor allele frequency was found 0.233, although the average polymorphism information content (PIC) and heterozygosity were 0.201 and 0.015, respectively. Principal component analyses (PCA) and population structure identified two major groups (sub-populations) based on SNPs information. The results indicated a substantial gene flow/exchange with many migrants (Nm = 86.428) and a considerable genetic diversity (number of different alleles, Na = 1.977; the number of effective alleles, Ne = 1.519; and Shannon's information index, I = 0.477) within the population, illustrating a good source for wheat improvement. The average PIC of 0.201 demonstrates moderate genetic diversity of the present evaluated advanced breeding panel. Analysis of molecular variance (AMOVA) detected 1% and 99% variance between and within subgroups. It is indicative of excessive gene traffic (less genetic differentiation) among the populations. These conclusions deliver important information with the potential to contribute new beneficial alleles using genome-wide association studies (GWAS) and marker-assisted selection to enhance genetic gain in South Asian wheat breeding programs.

Unconventional Hall effect and its variation with Co-doping in van der Waals Fe3GeTe2.

Two-dimensional (2D) van der Waals (vdW) magnetic materials have attracted a lot of attention owing to the stabilization of long range magnetic order down to atomic dimensions, and the prospect of novel spintronic devices with unique functionalities. The clarification of the magnetoresistive properties and its correlation to the underlying magnetic configurations is essential for 2D vdW-based spintronic devices. Here, the effect of Co-doping on the magnetic and magnetotransport properties of Fe3GeTe2 have been investigated. Magnetotransport measurements reveal an unusual Hall effect behavior whose strength was considerably modified by Co-doping and attributed to arise from the underlying complicated spin textures. The present results provide a clue to tailoring of the underlying interactions necessary for the realization of a variety of unconventional spin textures for 2D vdW FM-based spintronics.

Spectroscopic evidence of mixed angular momentum symmetry in non-centrosymmetric Ru[Formula: see text]B[Formula: see text].

Superconducting crystals with a lack of inversion symmetry can potentially host unconventional pairing. However, till today, no direct conclusive experimental evidence of such unconventional order parameters in non-centrosymmetric superconductors has been reported. In this paper, through direct measurement of the superconducting energy gap by scanning tunnelling spectroscopy, we report the existence of both s-wave (singlet) and p-wave (triplet) pairing symmetries in non-centrosymmetric Ru[Formula: see text]B[Formula: see text]. Our temperature and magnetic field-dependent studies also indicate that the relative amplitudes of the singlet and triplet components change differently with temperature.

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.).

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.

Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains.

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.

Evaluations of Genomic Prediction and Identification of New Loci for Resistance to Stripe Rust Disease in Wheat (Triticum aestivum L.).

Stripe rust is one of the most destructive diseases of wheat (Triticum aestivum L.), caused by Puccinia striiformis f. sp. tritici (Pst), and responsible for significant yield losses worldwide. Single-nucleotide polymorphism (SNP) diagnostic markers were used to identify new sources of resistance at adult plant stage to wheat stripe rust (YR) in 141 CIMMYT advanced bread wheat lines over 3 years in replicated trials at Borlaug Institute for South Asia (BISA), Ludhiana. We performed a genome-wide association study and genomic prediction to aid the genetic gain by accumulating disease resistance alleles. The responses to YR in 141 advanced wheat breeding lines at adult plant stage were used to generate G × E (genotype × environment)-dependent rust scores for prediction and genome-wide association study (GWAS), eliminating variation due to climate and disease pressure changes. The lowest mean prediction accuracies were 0.59 for genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP (RRBLUP), while the highest mean was 0.63 for extended GBLUP (EGBLUP) and random forest (RF), using 14,563 SNPs and the G × E rust score results. RF and EGBLUP predicted higher accuracies (∼3%) than did GBLUP and RRBLUP. Promising genomic prediction demonstrates the viability and efficacy of improving quantitative rust tolerance. The resistance to YR in these lines was attributed to eight quantitative trait loci (QTLs) using the FarmCPU algorithm. Four (Q.Yr.bisa-2A.1, Q.Yr.bisa-2D, Q.Yr.bisa-5B.2, and Q.Yr.bisa-7A) of eight QTLs linked to the diagnostic markers were mapped at unique loci (previously unidentified for Pst resistance) and possibly new loci. The statistical evidence of effectiveness and distribution of the new diagnostic markers for the resistance loci would help to develop new stripe rust resistance sources. These diagnostic markers along with previously established markers would be used to create novel DNA biosensor-based microarrays for rapid detection of the resistance loci on large panels upon functional validation of the candidate genes identified in the present study to aid in rapid genetic gain in the future breeding programs.

Juvenile Heat Tolerance in Wheat for Attaining Higher Grain Yield by Shifting to Early Sowing in October in South Asia.

Farmers in northwestern and central India have been exploring to sow their wheat much earlier (October) than normal (November) to sustain productivity by escaping terminal heat stress and to utilize the available soil moisture after the harvesting of rice crop. However, current popular varieties are poorly adapted to early sowing due to the exposure of juvenile plants to the warmer temperatures in the month of October and early November. Therefore, a study was undertaken to identify wheat genotypes suited to October sowing under warmer temperatures in India. A diverse collection of 3322 bread wheat varieties and elite lines was prepared in CIMMYT, Mexico, and planted in the 3rd week of October during the crop season 2012-2013 in six locations (Ludhiana, Karnal, New Delhi, Indore, Pune and Dharwad) spread over northwestern plains zone (NWPZ) and central and Peninsular zone (CZ and PZ; designated as CPZ) of India. Agronomic traits data from the seedling stage to maturity were recorded. Results indicated substantial diversity for yield and yield-associated traits, with some lines showing indications of higher yields under October sowing. Based on agronomic performance and disease resistance, the top 48 lines (and two local checks) were identified and planted in the next crop season (2013-2014) in a replicated trial in all six locations under October sowing (third week). High yielding lines that could tolerate higher temperature in October sowing were identified for both zones; however, performance for grain yield was more promising in the NWPZ. Hence, a new trial of 30 lines was planted only in NWPZ under October sowing. Lines showing significantly superior yield over the best check and the most popular cultivars in the zone were identified. The study suggested that agronomically superior wheat varieties with early heat tolerance can be obtained that can provide yield up to 8 t/ha by planting in the third to fourth week of October.

Elucidating the genetics of grain yield and stress-resilience in bread wheat using a large-scale genome-wide association mapping study with 55,568 lines.

Wheat grain yield (GY) improvement using genomic tools is important for achieving yield breakthroughs. To dissect the genetic architecture of wheat GY potential and stress-resilience, we have designed this large-scale genome-wide association study using 100 datasets, comprising 105,000 GY observations from 55,568 wheat lines evaluated between 2003 and 2019 by the International Maize and Wheat Improvement Center and national partners. We report 801 GY-associated genotyping-by-sequencing markers significant in more than one dataset and the highest number of them were on chromosomes 2A, 6B, 6A, 5B, 1B and 7B. We then used the linkage disequilibrium (LD) between the consistently significant markers to designate 214 GY-associated LD-blocks and observed that 84.5% of the 58 GY-associated LD-blocks in severe-drought, 100% of the 48 GY-associated LD-blocks in early-heat and 85.9% of the 71 GY-associated LD-blocks in late-heat, overlapped with the GY-associated LD-blocks in the irrigated-bed planting environment, substantiating that simultaneous improvement for GY potential and stress-resilience is feasible. Furthermore, we generated the GY-associated marker profiles and analyzed the GY favorable allele frequencies for a large panel of 73,142 wheat lines, resulting in 44.5 million datapoints. Overall, the extensive resources presented in this study provide great opportunities to accelerate breeding for high-yielding and stress-resilient wheat varieties.

Karnal Bunt: A Re-Emerging Old Foe of Wheat.

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 Selection for Grain Yield in the CIMMYT Wheat Breeding Program-Status and Perspectives.

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.

New QTLs for Spot Blotch Disease Resistance in Wheat (Triticum aestivum L.) Using Genome-Wide Association Mapping.

Spot blotch disease caused by Bipolaris sorokiniana is a major constraint for wheat production in tropics and subtropics. The introgression of spot blotch resistance alleles to the disease susceptible lines is critical to securing the wheat production in these regions. Although genome-wide association studies (GWASs) for spot blotch were attempted earlier, the present study focused on identifying new quantitative trait loci (QTLs) for spot blotch under natural disease pressure in diverse field conditions. A total of 139 advanced spring wheat lines were evaluated in three environments (three years and two locations) in India and Bangladesh. The GWAS using 14,063 polymorphic genotyping-by-sequencing (GBS) markers identified eight QTLs associated with spot blotch disease resistance belonging to eight chromosomes across the wheat genome. Here, we report the identified marker-trait associations (MTAs), along with the allele effects associated with the disease. The functional annotation of the significant markers identified NBS-LRR, MADS-box transcription factor, and 34 other plant-related protein families across multiple chromosomal regions. The results indicate four promising new QTLs on chromosomes 1A (497.2 Mb), 1D (89.84 Mb), 2B (421.92 Mb), and 6D (6.84 Mb) associated with several disease resistance protein families. These results provide insights into new genomic regions associated with spot blotch disease, and with additional validation, could be utilized in disease resistance breeding efforts in wheat development.