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Key Message: Genetic dissection of a QTL from wild emmer wheat, QGpc.huj.uh-5B.2, introgressed into bread wheat, identified candidate genes associated with tolerance to nitrogen deficiency, and potentially useful for improving nitrogen-use efficiency. Nitrogen (N) is an important macronutrient critical to wheat growth and development; its deficiency is one of the main factors causing reductions in grain yield and quality. N availability is significantly affected by drought or flooding, that are dependent on additional factors including soil type or duration and severity of stress. In a previous study, we identified a high grain protein content QTL (QGpc.huj.uh-5B.2) derived from the 5B chromosome of wild emmer wheat, that showed a higher proportion of explained variation under water-stress conditions. We hypothesized that this QTL is associated with tolerance to N deficiency as a possible mechanism underlying the higher effect under stress. To validate this hypothesis, we introgressed the QTL into the elite bread wheat var. Ruta, and showed that under N-deficient field conditions the introgression IL99 had a 33% increase in GPC (p < 0.05) compared to the recipient parent. Furthermore, evaluation of IL99 response to severe N deficiency (10% N) for 14 days, applied using a semi-hydroponic system under controlled conditions, confirmed its tolerance to N deficiency. Fine-mapping of the QTL resulted in 26 homozygous near-isogenic lines (BC 4 F 5 ) segregating to N-deficiency tolerance. The QTL was delimited from - 28.28 to - 1.29 Mb and included 13 candidate genes, most associated with N-stress response, N transport, and abiotic stress responses. These genes may improve N-use efficiency under severely N-deficient environments. Our study demonstrates the importance of WEW as a source of novel candidate genes for sustainable improvement in tolerance to N deficiency in wheat., (© 2024. The Author(s).)

Key Message: Here, we provide an updated set of guidelines for naming genes in wheat that has been endorsed by the wheat research community. The last decade has seen a proliferation in genomic resources for wheat, including reference- and pan-genome assemblies with gene annotations, which provide new opportunities to detect, characterise, and describe genes that influence traits of interest. The expansion of genetic information has supported growth of the wheat research community and catalysed strong interest in the genes that control agronomically important traits, such as yield, pathogen resistance, grain quality, and abiotic stress tolerance. To accommodate these developments, we present an updated set of guidelines for gene nomenclature in wheat. These guidelines can be used to describe loci identified based on morphological or phenotypic features or to name genes based on sequence information, such as similarity to genes characterised in other species or the biochemical properties of the encoded protein. The updated guidelines provide a flexible system that is not overly prescriptive but provides structure and a common framework for naming genes in wheat, which may be extended to related cereal species. We propose these guidelines be used henceforth by the wheat research community to facilitate integration of data from independent studies and allow broader and more efficient use of text and data mining approaches, which will ultimately help further accelerate wheat research and breeding., (© 2023. The Author(s).)

Key Message: rAMP-seq based genomic selection for agronomic traits has been shown to be a useful tool for winter wheat breeding programs by increasing the rate of genetic gain. Genomic selection (GS) is an effective strategy to employ in a breeding program that focuses on optimizing quantitative traits, which results in the ability for breeders to select the best genotypes. GS was incorporated into a breeding program to determine the potential for implementation on an annual basis, with emphasis on selecting optimal parents and decreasing the time and costs associated with phenotyping large numbers of genotypes. The design options for applying repeat amplification sequencing (rAMP-seq) in bread wheat were explored, and a low-cost single primer pair strategy was implemented. A total of 1870 winter wheat genotypes were phenotyped and genotyped using rAMP-seq. The optimization of training to testing population size showed that the 70:30 ratio provided the most consistent prediction accuracy. Three GS models were tested, rrBLUP, RKHS and feed-forward neural networks using the University of Guelph Winter Wheat Breeding Program (UGWWBP) and Elite-UGWWBP populations. The models performed equally well for both populations and did not differ in prediction accuracy (r) for most agronomic traits, with the exception of yield, where RKHS performed the best with an r = 0.34 and 0.39 for each population, respectively. The ability to operate a breeding program where multiple selection strategies, including GS, are utilized will lead to higher efficiency in the program and ultimately lead to a higher rate of genetic gain., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0 M ) or environments (CV0), a subset of lines that have been evaluated in either management (CV2 M ) or some environments (CV2), and the performance of newly developed lines in either management (CV1 M ) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: Using phenotypic data of four biparental spring wheat populations evaluated at multiple environments under two management systems, we discovered 152 QTL and 22 QTL hotspots, of which two QTL accounted for up to 37% and 58% of the phenotypic variance, consistently detected in all environments, and fell within genomic regions harboring known genes. Identification of the physical positions of quantitative trait loci (QTL) would be highly useful for developing functional markers and comparing QTL results across multiple independent studies. The objectives of the present study were to map and characterize QTL associated with nine agronomic and end-use quality traits (tillering ability, plant height, lodging, grain yield, grain protein content, thousand kernel weight, test weight, sedimentation volume, and falling number) in hard red spring wheat recombinant inbred lines (RILs) using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. We evaluated a total of 698 RILs from four populations derived from crosses involving seven parents at 3-8 conventionally (high N) and organically (low N) managed field environments. Using the phenotypic data combined across all environments per management, and the physical map between 1058 and 6526 markers per population, we identified 152 QTL associated with the nine traits, of which 29 had moderate and 2 with major effects. Forty-nine of the 152 QTL mapped across 22 QTL hotspot regions with each region coincident to 2-6 traits. Some of the QTL hotspots were physically located close to known genes. QSv.dms-1A and QPht.dms-4B.1 individually explained up to 37% and 58% of the variation in sedimentation volume and plant height, respectively, and had very large LOD scores that varied from 19.0 to 35.7 and from 16.7 to 55.9, respectively. We consistently detected both QTL in the combined and all individual environments, laying solid ground for further characterization and possibly for cloning., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: The first cytological characterization of the 2N v S segment in hexaploid wheat; complete de novo assembly and annotation of 2N v S segment; 2N v S frequency is increasing 2N v S and is associated with higher yield. The Aegilops ventricosa 2N v S translocation segment has been utilized in breeding disease-resistant wheat crops since the early 1990s. This segment is known to possess several important resistance genes against multiple wheat diseases including root knot nematode, stripe rust, leaf rust and stem rust. More recently, this segment has been associated with resistance to wheat blast, an emerging and devastating wheat disease in South America and Asia. To date, full characterization of the segment including its size, gene content and its association with grain yield is lacking. Here, we present a complete cytological and physical characterization of this agronomically important translocation in bread wheat. We de novo assembled the 2N v S segment in two wheat varieties, 'Jagger' and 'CDC Stanley,' and delineated the segment to be approximately 33 Mb. A total of 535 high-confidence genes were annotated within the 2N v S region, with > 10% belonging to the nucleotide-binding leucine-rich repeat (NLR) gene families. Identification of groups of NLR genes that are potentially N genome-specific and expressed in specific tissues can fast-track testing of candidate genes playing roles in various disease resistances. We also show the increasing frequency of 2N v S among spring and winter wheat breeding programs over two and a half decades, and the positive impact of 2N v S on wheat grain yield based on historical datasets. The significance of the 2N v S segment in wheat breeding due to resistance to multiple diseases and a positive impact on yield highlights the importance of understanding and characterizing the wheat pan-genome for better insights into molecular breeding for wheat improvement.

Key Message: Genetic dissection of GPC and TKW in tetraploid durum × WEW RIL population, based on high-density SNP genetic map, revealed 12 GPC QTLs and 11 TKW QTLs, with favorable alleles for 11 and 5 QTLs, respectively, derived from WEW. Wild emmer wheat (Triticum turgidum ssp. dicoccoides, WEW) was shown to exhibit high grain protein content (GPC) and therefore possess a great potential for improvement of cultivated wheat nutritional value. Genetic dissection of thousand kernel weight (TKW) and grain protein content (GPC) was performed using a high-density genetic map constructed based on a recombinant inbred line (RIL) population derived from a cross between T. durum var. Svevo and WEW acc. Y12-3. Genotyping of 208 F 6 RILs with a 15 K wheat single nucleotide polymorphism (SNP) array yielded 4166 polymorphic SNP markers, of which 1510 were designated as skeleton markers. A total map length of 2169 cM was obtained with an average distance of 1.5 cM between SNPs. A total of 12 GPC QTLs and 11 TKW QTLs were found under five different environments. No significant correlations were found between GPC and TKW across all environments. Four major GPC QTLs with favorable alleles from WEW were found on chromosomes 4BS, 5AS, 6BS and 7BL. The 6BS GPC QTL coincided with the physical position of the NAC transcription factor TtNAM-B1, underlying the cloned QTL, Gpc-B1. Comparisons of the physical intervals of the GPC QTLs described here with the results previously reported in other durum × WEW RIL population led to the discovery of seven novel GPC QTLs. Therefore, our research emphasizes the importance of GPC QTL dissection in diverse WEW accessions as a source of novel alleles for improvement of GPC in cultivated wheat.

Earliness per se regulates flowering time independent of environmental signals and helps to fine tune the time of flowering and maturity. In this study, we aimed to map earliness per se quantitative trait loci (QTLs) affecting days to flowering and maturity in a population developed by crossing two spring wheat cultivars, Cutler and AC Barrie. The population of 177 recombinant inbred lines (RILs) was genotyped for a total of 488 SSR and DArT polymorphic markers on all 21 chromosomes. Three QTLs of earliness per se affecting days to flowering and maturity were mapped on chromosomes 1B (QEps.dms-1B1 and QEps.dms-1B2) and 5B (QEps.dms-5B1), in individual environments and when all the environments were combined. A QTL affecting flowering time (QFlt.dms-4A1) was identified on chromosome 4A. Two grain yield QTLs were mapped on chromosome 5B, while one QTL was mapped on chromosome 1D. The population segregated for the photoperiod insensitive gene, Ppd-D1a, and it induced earlier flowering by 0.69 days and maturity by 1.28 days. The photoperiod insensitive allele Ppd-D1a interacted in an additive fashion with QTLs for flowering and maturity times. The earliness per se QTL QFlt.dms-5B.1 inducing earlier flowering could help to elongate grain filling duration for higher grain yield. Hence, chromosome 5B possesses promising genomic regions that may be introgressed for higher grain yield with earlier maturity through marker-assisted selection in bread wheat.