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Abstract A panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.

Abstract Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8–10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re‐synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so‐called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of “hybrid prediction”—originally developed for the prediction of wheat hybrids generated from different heterotic groups — to a situation of allopolyploidization. Our use‐case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in ‘traditional’ elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross‐validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks.

Abstract Selecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package—an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback–Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions—EvalMPS, FastMPS, and ApproxMPS—catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits.

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous–categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

To fulfill the growing demand for wheat consumption, it is important to focus on enhancement breeding strategies targeting key parameters such as yield, thousand kernel weight (TKW), quality characteristics including morphological traits, and protein content. These elements are key to the ongoing and future objectives of wheat breeding programs. Prioritizing these factors will effectively help meet the rising demand for wheat, especially given the challenges posed by unpredictable weather patterns. This study evaluated the morphological traits and protein content of 249 winter wheat varieties and advanced lines grown in eleven different environments in Morocco and Spain incorporating three varied sowing dates. The results showed considerable variability in morphological traits and protein content. Significant correlations were observed among various grain traits, with most grain morphological parameters exhibiting negative correlations with protein content. Differences across environments (p ≤ 0.01) in all traits, genotypes, and genotype by environment interaction were significant. A factorial regression analysis revealed significant impacts of environmental conditions on all grain morphological parameters, protein content, and TKW during the three growth stages. The study identified several high-performing and stable genotypes across diverse environments, providing valuable insights for wheat breeding programs such as genotypes 129, 234, 241, and 243. Genome-Wide Association Studies pinpointed 603 significant markers across 11 environments, spread across chromosomes. Among these, 400 markers were linked with at least two traits or observed in at least two different environments. Moreover, twelve marker-trait associations were detected that surpassed the Bonferroni correction threshold. These findings highlight the importance of targeted breeding efforts to enhance wheat quality and adaptability to different environmental conditions.

Common wheat (Triticum aestivum) is a hexaploid crop comprising three diploid sub-genomes labeled A, B, and D. The objective of this study is to investigate whether there is a discernible influence pattern from the D sub-genome with epistasis in genomic models for wheat diseases. Four genomic statistical models were employed; two models considered the linear genomic relationship of the lines. The first model (G) utilized all molecular markers, while the second model (ABD) utilized three matrices representing the A, B, and D sub-genomes. The remaining two models incorporated epistasis, one (GI) using all markers and the other (ABDI) considering markers in sub-genomes A, B, and D, including inter- and intra-sub-genome interactions. The data utilized pertained to three diseases: tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB), for synthetic hexaploid wheat (SHW) lines. The results (variance components) indicate that epistasis makes a substantial contribution to explaining genomic variation, accounting for approximately 50% in SNB and SB and only 29% for TS. In this contribution of epistasis, the influence of intra- and inter-sub-genome interactions of the D sub-genome is crucial, being close to 50% in TS and higher in SNB (60%) and SB (60%). This increase in explaining genomic variation is reflected in an enhancement of predictive ability from the G model (additive) to the ABDI model (additive and epistasis) by 9%, 5%, and 1% for SNB, SB, and TS, respectively. These results, in line with other studies, underscore the significance of the D sub-genome in disease traits and suggest a potential application to be explored in the future regarding the selection of parental crosses based on sub-genomes.

Background: Tunisia harbors a rich collection of unexploited durum wheat landraces (Triticum durum ssp. durum) that have been gradually replaced by elite cultivars since the 1970s. These landraces represent an important potential source for broadening the genetic background of elite durum wheat cultivars and for the introgression of novel genes for key traits, including disease resistance, into these cultivars.Methods: In this study, single nucleotide polymorphism (SNP) markers were used to investigate the genetic diversity and population structure of a core collection of 235 durum wheat accessions consisting mainly of landraces. The high phenotypic and genetic diversity of the fungal pathogen Pyrenophora tritici-repentis (cause of tan spot disease of wheat) in Tunisia allowed the assessment of the accessions for tan spot resistance at the adult plant stage under field conditions over three cropping seasons. A genome-wide association study (GWAS) was performed using a 90k SNP array.Results: Bayesian population structure analysis with 9191 polymorphic SNP markers classified the accessions into two groups, where groups 1 and 2 included 49.79% and 31.49% of the accessions, respectively, while the remaining 18.72% were admixtures. Principal coordinate analysis, the unweighted pair group method with arithmetic mean and the neighbor-joining method clustered the accessions into three to five groups. Analysis of molecular variance indicated that 76% of the genetic variation was among individuals and 23% was between individuals. Genome-wide association analyses identified 26 SNPs associated with tan spot resistance and explained between 8.1% to 20.2% of the phenotypic variation. The SNPs were located on chromosomes 1B (1 SNP), 2B (4 SNPs), 3A (2 SNPs), 3B (2 SNPs), 4A (2 SNPs), 4B (1 SNP), 5A (2 SNPs), 5B (4 SNPs), 6A (5 SNPs), 6B (2 SNPs), and 7B (1 SNP). Four markers, one on each of chromosomes 1B, and 5A, and two on 5B, coincided with previously reported SNPs for tan spot resistance, while the remaining SNPs were either novel markers or closely related to previously reported SNPs. Eight durum wheat accessions were identified as possible novel sources of tan spot resistance that could be introgressed into elite cultivars.Conclusion: The results highlighted the significance of chromosomes 2B, 5B, and 6A as genomic regions associated with tan spot resistance.

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

Wheat is a staple food in many areas around the World. In the 20th century, breeders and scientists were able to boost wheat yield considerably. However, a yield plateau has become a concern and is threatening food security. Investments in cutting-edge technologies, including genomics and precision phenology measurements, can provide valuable tools to drive crop improvement. The objectives of this study were to (i) investigate the genetic diversity in a set of winter wheat lines, (ii) characterize their phenological response under different vernalization and photoperiod conditions, and (iii) identify effective markers associated with the phenological traits. A total of 249 adapted genotypes of different geographical origin were genotyped using the 35K Axiom® Wheat Breeder’s Array. A total of 11,476 SNPs were used for genetic analysis. The set showed an average polymorphism information content of 0.37 and a genetic diversity of 0.43. A population structure analysis revealed three distinct subpopulations mainly related to their geographical origin (Europe, North America, and Western Asia). The lines of CGIAR origin showed the largest diversity and the lowest genetic distance to all other subpopulations. The phenology of the set was studied under controlled conditions using four combinations of long (19 h light) and short photoperiod (13 h light) and long vernalization (49 days at 5 °C) and no vernalization. With this, phenological traits such as earliness per se (Eps), relative response to vernalization (RRV), and relative response to photoperiod (RRP) were calculated. The phenotypic variation of growing degree days was significant in all phenology combinations. RRV ranged from 0 to 0.56, while RRP was higher with an overall average of 0.25. The GWAS analysis detected 30 marker-trait associations linked to five phenological traits. The highest significant marker was detected on chromosome 2D with a value of −log10(p) = 11.69. Only four loci known to regulate flowering exceeded the Bonferroni correction threshold of −log10(p) > 5.1. These results outline a solid foundation to address global food security and offer tremendous opportunities for advancing crop improvement strategies.

Abstract Exploring the genetic variability in yield and yield-related traits is essential to continue improving genetic gains. Fifty-nine Argentinian durum wheat cultivars were analyzed for important agronomic traits in three field experiments. The collection was genotyped with 3565 genome-wide SNPs and functional markers in order to determine the allelic variation at Rht-B1 and Ppd-A1 genes. Population structure analyses revealed the presence of three main groups, composed by old, modern and genotypes with European or CIMMYT ancestry. The photoperiod sensitivity Ppd-A1b allele showed higher frequency (75%) than the insensitivity one Ppd-A1a (GS105). The semi-dwarfism Rht-B1b and the Ppd-A1a (GS105) alleles were associated with increases in harvest index and decreases in plant height, grain protein content and earlier heading date, although only the varieties carrying the Rht-B1 variants showed differences in grain yield. Out of the two main yield components, grain number per plant was affected by allelic variants at Rht-B1 and Ppd-A1 loci, while no differences were observed in thousand kernel weight. The increases in grain number per spike associated with Rht-B1b were attributed to a higher grain number per spikelet, whereas Ppd-A1a (GS105) was associated with higher grain number per spikelet, but also with lower spikelets per spike.

The connection between expression variation and phenotypic diversity in the populations of polyploid crops remains elusive. Here, the authors reveal the impact of genetic variants leading to biased expression of homoeologous genes in hexaploid wheat on agronomic traits.

AbstractGenomic selection (GS) in wheat breeding programs is of great interest for predicting the genotypic values of individuals, where both additive and nonadditive effects determine the final breeding value of lines. While several simulation studies have shown the efficiency of rapid-cycling GS strategies for parental selection or population improvement, their practical implementations are still lacking in wheat and other crops. In this study, we demonstrate the potential of rapid-cycle recurrent GS (RCRGS) to increase genetic gain for grain yield (GY) in wheat. Our results showed a consistent realized genetic gain for GY after 3 cycles of recombination (C123103−10−13−1

AbstractLinking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment–Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as “covariable selection” unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a “reinforcement learner” algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

Abstract The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger.

Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of interest. This study evaluated the performance of BLUP (Best Linear Unbiased Prediction) in predicting resistance to tan spot, spot blotch and Septoria nodorum blotch in synthetic hexaploid wheat. BLUP was implemented in single-trait and multi-trait models with three variations: (1) the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP), and (3) a combination of the two matrices (A+G BLUP). In all three diseases, the A-BLUP model had a lower performance, and the G-BLUP and A+G BLUP were statistically similar (p ≥ 0.05). The prediction accuracy with the single trait was statistically similar (p ≥ 0.05) to the multi-trait accuracy, possibly due to the low correlation of severity between the diseases.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Rising temperatures due to climate change threaten agricultural crop productivity. As a cool-season crop, wheat is heat-sensitive, but often exposed to high temperatures during the cultivation period. In the current study, a bread wheat panel of spring wheat genotypes, including putatively heat-tolerant Australian and CIMMYT genotypes, was exposed to a 5-day mild (34°C/28°C, day/night) or extreme (37°C/27°C) heat stress during the sensitive pollen developmental stage. Worsening effects on anther morphology were observed, as heat stress increased from mild to extreme. Even under mild heat, a significant decrease in pollen viability and number of grains per spike from primary spike was observed compared with the control (21°C/15°C), with Sunstar and two CIMMYT breeding lines performing well. A heat-specific positive correlation between the two traits indicates the important role of pollen fertility for grain setting. Interestingly, both mild and extreme heat induced development of new tillers after the heat stress, providing an alternative sink for accumulated photosynthates and significantly contributing to the final yield. Measurements of flag leaf maximum potential quantum efficiency of photosystem II (Fv/Fm) showed an initial inhibition after the heat treatment, followed by a full recovery within a few days. Despite this, model fitting using chlorophyll soil plant analysis development (SPAD) measurements showed an earlier onset or faster senescence rate under heat stress. The data presented here provide interesting entry points for further research into pollen fertility, tillering dynamics, and leaf senescence under heat. The identified heat-tolerant wheat genotypes can be used to dissect the underlying mechanisms and breed climate-resilient wheat.

Abstract Background Durum wheat (Triticum turgidum L. ssp. durum Desf. Husn) is the main staple crop used to make pasta products worldwide. Under the current climate change scenarios, genetic variability within a crop plays a crucial role in the successful release of new varieties with high yields and wide crop adaptation. In this study we evaluated a durum wheat collection consisting of 197 genotypes that mainly comprised a historical set of Argentinian germplasm but also included worldwide accessions. Results We assessed the genetic diversity, population structure and linkage disequilibrium (LD) patterns in this collection using a 35 K SNP array. The level of polymorphism was considered, taking account of the frequent and rare allelic variants. A total of 1547 polymorphic SNPs was located within annotated genes. Genetic diversity in the germplasm collection increased slightly from 1915 to 2010. However, a reduction in genetic diversity using SNPs with rare allelic variants was observed after 1979. However, larger numbers of rare private alleles were observed in the 2000–2009 period, indicating that a high reservoir of rare alleles is still present among the recent germplasm in a very low frequency. The percentage of pairwise loci in LD in the durum genome was low (13.4%) in our collection. Overall LD and the high (r 2 > 0.7) or complete (r 2 = 1) LD presented different patterns in the chromosomes. The LD increased over three main breeding periods (1915–1979, 1980–1999 and 2000–2020). Conclusions Our results suggest that breeding and selection have impacted differently on the A and B genomes, particularly on chromosome 6A and 2A. The collection was structured in five sub-populations and modern Argentinian accessions (cluster Q4) which were clearly differentiated. Our study contributes to the understanding of the complexity of Argentinian durum wheat germplasm and to derive future breeding strategies enhancing the use of genetic diversity in a more efficient and targeted way.

Abstract The International Maize and Wheat Improvement Center (CIMMYT) annually distributes advanced wheat (Triticum aestivum L.) breeding lines to collaborators worldwide through the International Wheat Improvement Network. Lines are disseminated through international nurseries, including the Elite Spring Wheat Yield Trial (ESWYT) targeted to optimal (irrigated and high production) wheat production areas and the Semi‐arid Wheat Yield Trial (SAWYT) targeted to low rainfall environments. A total of 2,184 wheat lines that formed the ESWYT and SAWYT since 1979 and 1992, respectively, were genotyped using genotyping‐by‐sequencing to explore trends of genetic diversity and selection footprints associated with continuous crop improvement and adaptation. Due to a small population size of each trial, adjacent year trials were pooled into subpopulations. Population structure was evaluated using discriminant analysis of principal components and fixation index. High levels of admixture within and across the ESWYT and SAWYT subpopulations were revealed, indicating that the entire genetic diversity in the overall CIMMYT germplasm pool is harnessed to target core traits to individual mega‐environments. Genome wide scans of deviations of minor allele frequencies at each marker identified large linkage blocks in several chromosomes. The scans also revealed that 9.8 and 2.0% of the SNP markers could be associated to selection signatures over time and to environmental adaptation (significant deviations between ESWYT and SAWYT), respectively. Several known genes and previously identified haplotypes associated with grain yield in more recent CIMMYT elite germplasm did fall into genomic regions with directional selection.

Stem rust caused by the fungus Puccinia graminis f.sp. tritici Eriks. & E. Henn. (Pgt) threatens the global production of both durum wheat (Triticum turgidum L. ssp. durum (Desf.) Husnot) and common wheat (Triticum aestivum L.). The objective of this study was to evaluate a durum wheat recombinant inbred line (RIL) population from a cross between a susceptible parent ‘DAKIYE’ and a resistant parent ‘Reichenbachii’ developed by the International Center for the Improvement of Maize and Wheat (CIMMYT) 1) for seedling response to races JRCQC and TTRTF and 2) for field response to a bulk of the current Pgt races prevalent in Ethiopia and Kenya and 3) to map loci associated with seedling and field resistances in this population. A total of 224 RILs along with their parents were evaluated at the seedling stage in the Ethiopian Institute for Agricultural Research greenhouse at Debre Zeit, Ethiopia and in the EIAR and KALRO fields in Ethiopia and Kenya, for two seasons from 2019 to 2020. The lines were genotyped using the genotyping-by-sequencing approach. A total of 843 single nucleotide polymorphism markers for 175 lines were used for quantitative trait locus (QTL) analyses. Composite interval mapping (CIM) identified three QTL on chromosomes 3B, 4B and 7B contributed by the resistant parent. The QTL on chromosome 3B was identified at all growth stages and it explained 11.8%, 6.5%, 6.4% and 15.3% of the phenotypic variation for responses to races JRCQC, TTRTF and in the field trials ETMS19 and KNMS19, respectively. The power to identify additional QTL in this population was limited by the number of high-quality markers, since several markers with segregation distortion were eliminated. A cytological study is needed to understand the presence of chromosomal rearrangements. Future evaluations of additional durum lines and RIL families identification of durable adult plant resistance sources is crucial for breeding stem rust resistance in durum wheat in the future.

We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds.

Tan spot (TS) and Septoria nodorum blotch (SNB), caused by Pyrenophora tritici-repentis and Parastagonospora nodorum, respectively, are important fungal leaf-spotting diseases of wheat that cause significant losses in grain yield. In this study, two recombinant inbred line populations, ‘Bartai’ × ‘Ciano T79’ (referred to as B × C) and ‘Cascabel’ × ‘Ciano T79’ (C × C) were tested for TS and SNB response in order to determine the genetic basis of seedling resistance. Genotyping was performed with the DArTseq genotyping-by-sequencing (GBS) platform. A chromosome region on 5AL conferred resistance to TS and SNB in both populations, but the effects were larger in B × C (R2 = 11.2%–16.8%) than in C × C (R2 = 2.5%–9.7%). Additionally, the chromosome region on 5BL (presumably Tsn1) was significant for both TS and SNB in B × C but not in C × C. Quantitative trait loci (QTL) with minor effects were identified on chromosomes 1B, 2A, 2B, 3A, 3B, 4D, 5A, 5B, 5D, 6B, and 6D. The two CIMMYT breeding lines ‘Bartai’ and ‘Cascabel’ contributed resistance alleles at both 5AL and 5BL QTL mentioned above. The QTL on 5AL showed linkage with the Vrn-A1 locus, whereas the vrn-A1 allele conferring lateness was associated with resistance to TS and SNB. Keywords: Parastagonospora nodorum, Pyrenophora tritici-repentis, QTL mapping, Resistance breeding, Triticum aestivum

Spot blotch (SB) caused by Bipolaris sorokiniana (Sacc.) Shoem is a destructive fungal disease affecting wheat and many other crops. Synthetic hexaploid wheat (SHW) offers opportunities to explore new resistance genes for SB for introgression into elite bread wheat. The objectives of our study were to evaluate a collection of 441 SHWs for resistance to SB and to identify potential new genomic regions associated with the disease. The panel exhibited high SB resistance, with 250 accessions showing resistance and 161 showing moderate resistance reactions. A genome-wide association study (GWAS) revealed a total of 41 significant marker–trait associations for resistance to SB, being located on chromosomes 1B, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4D, 5A, 5D, 6D, 7A, and 7D; yet none of them exhibited a major phenotypic effect. In addition, a partial least squares regression was conducted to validate the marker–trait associations, and 15 markers were found to be most important for SB resistance in the panel. To our knowledge, this is the first GWAS to investigate SB resistance in SHW that identified markers and resistant SHW lines to be utilized in wheat breeding.

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.

Tan spot, caused by Pyrenophora tritici-repentis, is a serious foliar disease of wheat in Kazakhstan with reported yield losses as high as 50% during epidemic years. Here, we report the evaluation of a collection of 191 hexaploid spring and winter wheat lines for tan spot resistance and its underlying genetic architecture using genome-wide association study (GWAS). Our wheat collection comprised candidate varieties from Kazakhstan, Russia, and CIMMYT. It was genotyped using the DArTseq technology and phenotyped for resistance to tan spot at seedling and adult plant stages in Kazakhstan. DArTseq SNPs revealed high genetic diversity (average polymorphic information content = 0.33) in the panel and genome-wide linkage disequilibrium decay at 22 Mb (threshold r2 = 0.1). Principal component analysis revealed a clear separation of Eurasian germplasm from CIMMYT and IWWIP lines. GWAS identified 34 marker-trait associations (MTA) for resistance to tan spot and the amount of phenotypic variation explained by these MTA ranged from 4% to 13.7%. Our results suggest the existence of novel valuable resistant alleles on chromosomes 3BS, and 5DL and 6AL for resistance to Race 1 and Race 5, respectively, in addition to known genes tsn1 and tsc2. On chromosome 6AL, a genomic region spanning 3 Mb was identified conferring resistance to both Race 1 and Race 5. Epistatic interaction of associated loci was revealed on chromosomes 1B, 5B, 7B, 5A, and 6A contributing to additional variation of 3.2–11.7%. Twenty-five lines with the best allele combinations of SNPs associated with resistance to both races have been identified as candidates for future variety release and breeding. The results of the present study will be further validated in other independent genetic backgrounds to be able to use markers in breeding.

We untangled key regions of the genetic architecture of grain yield (GY) in CIMMYT spring bread wheat by conducting a haplotype-based, genome-wide association study (GWAS), together with an investigation of epistatic interactions using seven large sets of elite yield trials (EYTs) consisting of a total of 6,461 advanced breeding lines. These lines were phenotyped under irrigated and stress environments in seven growing seasons (2011–2018) and genotyped with genotyping-by-sequencing markers. Genome-wide 519 haplotype blocks were constructed, using a linkage disequilibrium-based approach covering 14,036 Mb in the wheat genome. Haplotype-based GWAS identified 7, 4, 10, and 15 stable (significant in three or more EYTs) associations in irrigated (I), mild drought (MD), severe drought (SD), and heat stress (HS) testing environments, respectively. Considering all EYTs and the four testing environments together, 30 stable associations were deciphered with seven hotspots identified on chromosomes 1A, 1B, 2B, 4A, 5B, 6B, and 7B, where multiple haplotype blocks were associated with GY. Epistatic interactions contributed significantly to the genetic architecture of GY, explaining variation of 3.5–21.1%, 3.7–14.7%, 3.5–20.6%, and 4.4– 23.1% in I, MD, SD, and HS environments, respectively. Our results revealed the intricate genetic architecture of GY, controlled by both main and epistatic effects. The importance of these results for practical applications in the CIMMYT breeding program is discussed.

Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017–2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from −0.03 to 0.74 for AC and −0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.

Abstract Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi‐trait models (Bayesian multi‐trait multi‐environment [BMTME] and multi‐trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait ALVPL. In contrast, under Pearson's correlation, the best predictions depended on the data set. The results obtained suggest that the BMTME model should be preferred for multi‐trait prediction analyses. This model allows to obtain not only the correlation among traits, but also the correlation among environments, helping to increase the prediction accuracy.

Triticum aestivum L., also known as common wheat, is affected by many biotic stresses. Root diseases are the most difficult to tackle due to the complexity of phenotypic evaluation and the lack of resistant sources compared to other biotic stress factors. Soil-borne pathogens such as the root-lesion nematodes caused by the Pratylenchus species and crown rot caused by various Fusarium species are major wheat root diseases, causing substantial yield losses globally. A set of 189 advanced spring bread wheat lines obtained from the International Maize and Wheat Improvement Center (CIMMYT) were genotyped with 4056 single nucleotide polymorphisms (SNP) markers and screened for root-lesion nematodes and crown rot resistance. Population structure revealed that the genotypes could be divided into five subpopulations. Genome-Wide Association Studies were carried out for both resistances to Pratylenchus and Fusarium species. Based on our results, 11 different SNPs on chromosomes 1A, 1B, 2A, 3A, 4A, 5B, and 5D were significantly associated with root-lesion nematode resistance. Seven markers demonstrated association with P. neglectus, while the remaining four were linked to P. thornei resistance. In the case of crown rot, eight different markers on chromosomes 1A, 2B, 3A, 4B, 5B, and 7D were associated with Fusarium crown rot resistance. Identification and screening of root diseases is a challenging task; therefore, the newly identified resistant sources/genotypes could be exploited by breeders to be incorporated in breeding programs. The use of the identified markers in marker-assisted selection could enhance the selection process and cultivar development with root-lesion nematode and crown rot resistance.

Synthetic hexaploid wheat (SHW) has shown effective resistance to a diversity of diseases and insects, including tan spot, which is caused by Pyrenophora tritici-repentis, being an important foliar disease that can attack all types of wheat and several grasses. In this study, 443 SHW plants were evaluated for their resistance to tan spot under controlled environmental conditions. Additionally, a genome-wide association study was conducted by genotyping all entries with the DArTSeq technology to identify marker-trait associations for tan spot resistance. Of the 443 SHW plants, 233 showed resistant and 183 moderately resistant reactions, and only 27 were moderately susceptible or susceptible to tan spot. Durum wheat (DW) parents of the SHW showed moderately susceptible to susceptible reactions. A total of 30 significant marker-trait associations were found on chromosomes 1B (4 markers), 1D (1 marker), 2A (1 marker), 2D (2 markers), 3A (4 markers), 3D (3 markers), 4B (1 marker), 5A (4 markers), 6A (6 markers), 6B (1 marker) and 7D (3 markers). Increased resistance in the SHW in comparison to the DW parents, along with the significant association of resistance with the A and B genome, supported the concept of activating epistasis interaction across the three wheat genomes. Candidate genes coding for F-box and cytochrome P450 proteins that play significant roles in biotic stress resistance were identified for the significant markers. The identified resistant SHW lines can be deployed in wheat breeding for tan spot resistance.

Rust diseases continuously threaten global wheat production: stem rust, leaf rust, and yellow rust caused by Puccinia graminis f. sp. tritici, Puccinia triticina, and Puccinia striiformis f. sp. tritici, respectively. Recent studies indicated that the average losses from all these three rusts reached up to 15.04 million tons per year, which is equivalent to an annual average loss of around US $2.9 billion per year. The major focus of Mexican and worldwide breeding programs is the release of rust resistant cultivars, as this is considered the best option for controlling rust diseases. In Mexico, the emphasis has been placed on genes that confer partial resistance in the adult plant stage and against a broad spectrum of rust races since the 1970s. In this study, a set of the first-generation tall varieties developed and released in the 1940s and 1950s, the first semi-dwarfs, and other releases in Mexico, all of which showed different levels of rust resistance have been phenotyped for the three rust diseases and genotyped. Results of the molecular marker detection indicated that Lr34, Lr46, Lr67, and Lr68 alone or in different gene combinations were present among the wheat cultivars. Flag leaf tip necrosis was present in all cultivars and most were positive for brown necrosis or Pseudo Black Chaff associated with the Sr2 stem rust resistance complex. The phenotypic responses to the different rust infections indicate the presence of additional slow rusting and race-specific resistance genes. The study reveals the association of the slow rusting genes with durable resistance to the three rusts including Ug99 in cultivars bred before the green revolution such as Frontera, Supremo 211, Chapingo 48, Yaqui 50, Kentana 52, Bajio 52, Bajio 53, Yaqui 53, Chapingo 53, Yaktana Tardio 54, and Mayo 54 and their descendants after intercrossing and recombination. These slow rusting genes are the backbone of the resistance in the current Mexican germplasm.

Wheat adaptability to a wide range of environmental conditions is mostly determined by allelic diversity within genes controlling vernalization requirement (Vrn-1) and photoperiod sensitivity (Ppd-1). We characterized a panel of 151 durum wheat Mediterranean landraces and 20 representative locally adapted modern cultivars for their allelic composition at Vrn-1 and Ppd-1 gene using diagnostic molecular markers and studied their association with the time needed to reach six growth stages under field conditions over 6 years. Compared with the more diverse and representative landrace collection, the set of modern cultivars were characterized by a reduction of 50% in the number of allelic variants at the Vrn-A1 and Vrn-B1 genes, and the high frequency of mutant alleles conferring photoperiod insensitivity at Ppd-A1, which resulted on a shorter cycle length. Vrn-A1 played a greater role than Vrn-B1 in regulating crop development (Vrn-A1 > Vrn-B1). The results suggest that mutations in the Vrn-A1 gene may have been the most important in establishing the spring growth habit of Mediterranean landraces and modern durum cultivars. The allele Vrn-A1d, found in 10 landraces, delayed development. The relative effects of single Vrn-A1 alleles on delaying the development of the landraces were vrn-A1 = Vrn-A1d > Vrn-A1b > Vrn-A1c. Allele vrn-B1 was present in all except two landraces and in all modern cultivars. The null allele at Ppd-A1 (a deletion first observed in the French bread wheat cultivar ‘Capelle-Desprez’) was found for the first time in durum wheat in the present study that identified it in 30 landraces from 13 Mediterranean countries. Allele Ppd-A1a (GS105) was detected in both germplasm types, while the allele Ppd-A1a (GS100) was found only in modern North American and Spanish cultivars. The relative effect of single Ppd-A1 alleles on extending phenological development was Ppd-A1(DelCD) > Ppd-A1b > Ppd-A1a (GS105) > Ppd-A1a (GS100). Sixteen Vrn-1+Ppd-1 allelic combinations were found in landraces and six in modern cultivars, but only three were common to both panels. Differences in the number of days to reach anthesis were 10 days in landraces and 3 days in modern cultivars. Interactive effects between Vrn-1 and Ppd-1 genes were detected.

Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index Pi (measure of stability). Sixteen associations with GY were “environment-specific” with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For Pi, 8 associations were from chromosomes 4B and 7B, indicating ‘hot spot’ regions for stability. Epistatic interactions contributed to an additional 5–9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9–10% increase in prediction accuracy, whereas for Pi this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat.

The content and composition of seed storage proteins is largely responsible for wheat end-use quality. They mainly consist of polymeric glutenins and monomeric gliadins. According to their electrophoretic mobility, gliadins and glutenins are subdivided into several fractions. Glutenins are classified as high molecular weight or low molecular weight glutenin subunits (HMW-GSs and LMW-GSs, respectively). LMW-GSs are encoded by multigene families located at the orthologous Glu-3 loci. We designed a set of 16 single-nucleotide polymorphism (SNP) markers that are able to detect SDS-PAGE alleles at the Glu-A3 and Glu-B3 loci. The SNP markers captured the diversity of alleles in 88 international reference lines and 27 Mexican cultivars, when compared to SDS-PAGE and STS markers, however, showed a slightly larger percent of multiple alleles, mainly for Glu-B3. SNP markers were then used to determine the Glu-1 and Glu-3 allele composition in 54 CIMMYT historical lines and demonstrated to be useful tool for breeding programs to improve wheat end product properties.

Durum wheat grains (Triticum turgidum L. ssp. durum) are the main source for the production of pasta, bread and a variety of products consumed worldwide. The quality of pasta is mainly defined by the rheological properties of gluten, an elastic network in wheat endosperms formed of gliadins and glutenins. In this study, the allelic variation at five glutenin loci was analysed in 196 durum wheat genotypes. Two loci (Glu-A1 and Glu-B1), encoding for high-molecular-weight glutenin subunits (HMW-GS), and three loci (Glu-B2, Glu-A3 and Glu-B3), encoding for low molecular weight glutenin subunits (LMW-GS), were assessed by SDS-PAGE. The SDS-sedimentation test was used and the grain protein content was evaluated. A total of 32 glutenin subunits and 41 glutenin haplotypes were identified. Four novel alleles were detected. Fifteen haplotypes represented 85.7% of glutenin loci variability. Some haplotypes carrying the 7 + 15 and 7 + 22 banding patterns at Glu-B1 showed a high gluten strength similar to those that carried the 7 + 8 or 6 + 8 alleles. A decreasing trend in grain protein content was observed over the last 85 years. Allelic frequencies at the three main loci (Glu-B1, Glu-A3 and Glu-B3) changed over the 1915–2020 period. Gluten strength increased from 1970 to 2020 coinciding with the allelic changes observed. These results offer valuable information for glutenin haplotype-based selection for use in breeding programs.

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.

Grain yield (YLD) is a function of the total biomass (BM) and of partitioning the biomass by grains, i.e., the harvest index (HI). The most critical developmental stage for their determination is the flowering time, which mainly depends on the vernalization requirement (Vrn) and photoperiod sensitivity genes (Ppd) loci. Allelic variants at the Vrn, Ppd, and earliness per se (Eps) genes of elite spring wheat genotypes included in High Biomass Association Panel (HiBAP) I and II were used to estimate their effects on the phenological stages BM, HI, and YLD. Each panel was grown for two consecutive years in Northwest Mexico. Spring alleles at Vrn-1 had the largest effect on shortening the time to anthesis, and the Ppd-insensitive allele Ppd-D1a had the most significant positive effect on YLD in both panels. In addition, alleles at TaTOE-B1 and TaFT3-B1 promoted between 3.8% and 7.6% higher YLD and 4.2% and 10.2% higher HI in HiBAP I and II, respectively. When the possible effects of the TaTOE-B1 and TaFT3-B1 alleles on the sink and source traits were explored, the favorable allele at TaTOE-B1 showed positive effects on several sink traits mainly related to grain number. The favorable alleles at TaFT3-B1 followed a different pattern, with positive effects on the traits related to grain weight. The results of this study expanded the wheat breeders’ toolbox in the quest to breed better-adapted and higher-yielding wheat cultivars.

Genetic resistance in the host plant is the most economical and environmentally friendly strategy for controlling wheat leaf rust, caused by Puccinia triticina Eriks. The durum wheat lines Gaza (Middle East), Arnacoris (France) and Saragolla (Italy) express high levels of resistance to the Mexican races of P. triticina. Three recombinant inbred line (RIL) populations, derived from crosses of each of these resistance sources to the susceptible line ATRED #2, were evaluated for leaf rust reactions at CIMMYT’s leaf rust nurseries in Mexico. Genetic analyses of host reactions suggested oligogenic control of resistance in all populations. The F8 RILs from each cross were genotyped using the Illumina iSelect 90K array, and high-density genetic maps were constructed for each population. Using composite interval mapping, a total of seven quantitative trait loci (QTL) that provide resistance to leaf rust were identified. Two QTL designated as QLr.usw-6BS and QLr.usw-6BL were identified on chromosome 6B in Gaza, which explained up to 78.5% and 21.3% of the observed leaf rust severity variance, respectively. A major QTL designated as QLr.usw-7BL was detected on the long arm of chromosome 7B in Arnacoris, which accounted for up to 65.9% of the disease severity variance. Arnacoris also carried a minor QTL on chromosome 1BL, designated as QLr.usw-1BL.1 that explained up to 17.7% of the phenotypic variance. Three QTL conferred leaf rust resistance in Saragolla, namely QLr.usw-2BS, QLr.usw-3B, and QLr.usw-1BL.2, which accounted for up to 42.3, 9.4, and 7.1% of the phenotypic variance, respectively. Markers flanking each QTL were physically mapped against the durum wheat reference sequence and candidate genes involved in disease resistance were identified within the QTL intervals. The QTL identified in this study and their closely linked markers are useful resources for gene pyramiding and breeding for durable leaf rust resistance in durum wheat.

Increased thousand-grain weight (TGW) is an important breeding target for indirectly improving grain yield (GY). Fourteen reported candidate genes known to enhance TGW were evaluated in two independent and existing datasets of wheat at CIMMYT, the Elite Yield Trial (EYT) from 2015 to 2016 (EYT2015-16) and the Wheat Association Mapping Initiative (WAMI) panel, to study their allele effects on TGW and to verify their suitability for marker-assisted selection. Of these, significant associations were detected for only one gene (TaGs3-D1) in the EYT2015-16 and two genes (TaTGW6 and TaSus1) in WAMI. The reported favorable alleles of TaGs3-D1 and TaTGW6 genes decreased TGW in the datasets. A haplotype-based genome wide association study was implemented to identify the genetic determinants of TGW on a large set of CIMMYT germplasm (4,302 lines comprising five EYTs), which identified 15 haplotype blocks to be significantly associated with TGW. Four of them, identified on chromosomes 4A, 6A, and 7A, were associated with TGW in at least three EYTs. The locus on chromosome 6A (Hap-6A-13) had the largest effect on TGW and additionally GY with increases of up to 2.60 g and 258 kg/ha, respectively. Discovery of novel TGW loci described in our study expands the opportunities for developing diagnostic markers and for multi-gene pyramiding to derive new allele combinations for enhanced TGW and GY in CIMMYT wheat.

In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1–M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat ( L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments.

The aim of this work was to analyze the genetic diversity and linkage disequilibrium in a collection of 168 durum wheat accessions (Triticum turgidum L. var. durum) of different origins. Our collection was mainly composed of released and unreleased Argentinian germplasm, with additional genotypes from Italy, Chile, France, CIMMYT, Cyprus, USA and WANA region. To this end, the entire collection was characterized with 85 Single Nucleotide Polymorphism (SNP) markers obtained by Kompetitive Allele Specific PCR (KASP), giving a heterozygosity (He) mean value of 0.183 and a coefficient of genetic differentiation (Gst) value of 0.139. A subset of 119 accessions was characterized with six Amplified Fragment Length Polymorphism (AFLP) primer combinations. A total of 181 polymorphic markers (125 AFLP and 56 SNP) amplified across this subset revealed He measures of 0.352 and 0.182, respectively. Of these, 134 were selected to estimate the genome-wide linkage disequilibrium obtaining low significant values (r2 = 0.11) in the subset, indicating its suitability for future genome-wide association studies (GWAS). The structure analysis conducted in the entire collection with SNP detected two subpopulations. However, the structure analysis conducted with AFLP markers in the subset of 119 accessions proved to have greater degree of resolution and detect six subpopulations. The information provided by both marker types was complementary and showed a strong association between old Argentinian and Italian germplasm and a contribution of CIMMYT germplasm to modern Argentinian, Chilean and Cypriot accessions. The influence of Mediterranean germplasm, mainly from Italy, on part of the modern Argentinian cultivars or breeding lines was also clearly evidenced. Although our analysis yields conclusive results and useful information for association mapping studies, further analyses are needed to refine the number of subpopulations present in the germplasm collection analyzed.

Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat ( L.) in CIMMYT’s elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across-nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping-by-sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item-based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree-based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full-sibs. However, GS was less advantageous for within-family selections in elite families with few full-sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early-generation within-family selections.

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.

This study analysed the effect of flowering time as influenced by photoperiod sensitivity genes on yield and yield stability in durum wheat. Twenty-three spring genotypes harbouring different allele combinations at Ppd-A1 and Ppd-B1 were grown in 15 field experiments at three sites at latitudes from 41° to 19° N (Spain, Mexico-North and Mexico-South). Low temperature and solar radiation before flowering and long day length during grain-filling characteristic for the Spanish site resulted in high grain number/m2 (GN) and yield (GY), while a moderate GN combined with high solar radiation during grain-filling at Mexico-North led to heavier grains. Allele combination GS100-Ppd-A1a/Ppd-B1a reduced the flowering time up to nine days when compared with Ppd-A1b/Ppd-B1a. Differences in flowering time caused by Ppd-A1/Ppd-B1 allele combinations did not affect yield. Combinations GS105-Ppd-A1a/Ppd-B1b and Ppd-A1b/Ppd-B1b resulted in the highest GN, linked to spikelets/spike, which was higher in GS105-Ppd-A1a/Ppd-B1b due to more grains/spikelet. Flowering time caused by Eps had a minor effect on GN, spikes/m2 and grains/spike, but late flowering resulted in reduced grain weight and GY. Allele combinations harbouring alleles conferring a similar photoperiod sensitivity response at Ppd-A1 and Ppd-B1 resulted in greater yield stability than combinations that carry alleles conferring a different response. Allele combination GS100-Ppd-A1a/Ppd-B1a was the most suitable in terms of yield and yield stability of durum wheat cultivated under irrigation within the studied latitudes.

Breeding programs for purple wheat are underway in many countries but there is a lack of information on the effects of Pp (purple pericarp) genes on agronomic and quality traits in variable environments and along the product chain (grain-flour-bread). This study was based on unique material: two pairs of isogenic lines in a spring wheat cv. Saratovskaya-29 (S29) background differing only in Pp genes and grain color. In 2017, seven experiments were conducted in Kazakhstan, Russia, and Turkey with a focus on genotype and environment interaction and, in 2018, one experiment in Turkey with a focus on grain, flour, and bread quality. The effect of environment was greater compared to genotype for the productivity and quality traits studied. Nevertheless, several important traits, such as grain color and anthocyanin content, are closely controlled by genotype, offering the opportunity for selection. Phenolic content in purple-grained lines was not significantly higher in whole wheat flour than in red-colored lines. However, this trait was significantly higher in bread. For antioxidant activities, no differences between the genotypes were detected in both experiments. Comparison of two sources of Pp genes demonstrated that the lines originating from cv. Purple Feed had substantially improved productivity and quality traits compared to those from cv. Purple.

Pronounced anther extrusion (AE) is associated with field resistance to Fusarium head blight (FHB), one of the most devastating diseases of wheat globally. In this study, two recombinant inbred line (RIL) populations were used to map quantitative trait loci (QTL) for AE and field FHB resistance and to investigate the association of both traits at the genetic level. Furthermore, two panels of International Maize and Wheat Improvement Center (CIMMYT) wheat breeding lines were evaluated to describe the phenotypic association between the two traits in detail. Highly significant negative correlation was identified between AE and FHB severity in the two populations and the two panels, with r-values ranging from 0.55 to 0.74. QTL analysis in the two RIL populations identified 12 QTL for AE and nine for FHB resistance, of which five QTL located on chromosomes 3BL, 4BS, 4DS, 5AL, and 5BL were associated with both AE and FHB, collectively explaining over 50% of phenotypic variation for FHB. The QTL on chromosomes 4BS, 4DS, 5AL, and 5BL were closely linked to Rht-B1, Rht-D1, Vrn-A1, and Vrn-B1 genes, respectively. In conclusion, AE is closely related to field FHB resistance and could be used as a morphological marker in wheat breeding for field FHB resistance.

New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1–5, 1–9, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state-of-the-art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.

The main yield components in durum wheat are grain number per unit area (GN) and thousand kernel weight (TKW), both of which are affected by environmental conditions. The most critical developmental stage for their determination is flowering time, which partly depends on photoperiod sensitivity genes at Ppd-1 loci. Fifteen field experiments, involving 23 spring durum wheat genotypes containing all known allelic variants at the PHOTOPERIOD RESPONSE LOCUS (Ppd-A1 and Ppd-B1) were carried out at three sites at latitudes ranging from 41° to 27° N (Spain, Mexico-north, and Mexico-south, the latter in spring planting). Allele GS100 at Ppd-A1, which causes photoperiod insensitivity and results in early-flowering genotypes, tended to increase TKW and yield, albeit not substantially. Allele Ppd-B1a, also causing photoperiod insensitivity, did not affect flowering time or grain yield. Genotypes carrying the Ppd-B1b allele conferring photoperiod sensitivity had consistently higher GN, which did not translate into higher yield due to under-compensation in TKW. This increased GN was due to a greater number of grains spike-1 as a result of a higher number of spikelets spike-1. Daylength from double ridge to terminal spikelet stage was strongly and positively associated with the number of spikelets spike-1 in Spain. This association was not found in the Mexico sites, thereby indicating that Ppd-B1b had an intrinsic effect on spikelets spike-1 independently of environmental cues. Our results suggest that, in environments where yield is limited by the incapacity to produce a high GN, selecting for Ppd-B1b may be advisable.