New races with wider virulence indicate rapid evolution of Puccinia striiformis f. sp. tritici in the Southern Cone of America.

Wheat yellow (stripe) rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat worldwide. Pst populations are composed of multiple genetic groups, each carrying one or more races characterized by different avirulence/virulence combinations. Since the severe epidemics in 2017, yellow rust has become the most economically important wheat foliar disease in Uruguay. A set of 124 Pst isolates collected from wheat fields in Uruguay between 2017 and 2021 were characterized phenotypically and 27 of those isolates were subsequently investigated in-depth by additional molecular genotyping and race phenotyping analyses. Three genetic groups were identified, i.e., PstS7, PstS10 and PstS13, the latter being the most prevalent. Two races previously reported in Europe, Warrior (PstS7) and Benchmark (PstS10), were detected in four and two isolates, respectively. A third race known as Triticale2015 (PstS13), first detected in Europe in 2015 and in Argentina in 2017, was detected at several locations. Additional virulence to Yr3, Yr17, Yr25, Yr27 or Yr32 was detected in three new race variants within PstS13. The identification of these new races, which have not been reported outside South America, provides strong evidence of the local evolution of virulence in Pst during the recent epidemic years.

Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America.

KEY MESSAGE: Multi-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL. Diseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.).

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

Resistance to Multiple Temperate and Tropical Stem and Sheath Diseases of Rice.

Stem rot and aggregated sheath spot are the two major stem and sheath diseases affecting rice (Oryza sativa L.) in temperate areas. A third fungal disease, sheath blight, is a major disease in tropical areas. Resistance to these diseases is a key objective in rice breeding programs but phenotyping is challenged by the confounding effects of phenological and morphological traits such as flowering time (FT) and plant height (PH). This study sought to identify quantitative trait loci (QTL) for resistance to these three diseases after removing the confounding effects of FT and PH. Two populations of advanced breeding germplasm, one with 316 tropical japonica and the other with 325 indica genotypes, were evaluated in field and greenhouse trials for resistance to the diseases. Phenotypic means for field and greenhouse disease resistance, adjusted by FT and PH, were analyzed for associations with 29,000 single nucleotide polymorphisms (SNPs) in tropical japonica and 50,000 SNPs in indica. A total of 29 QTL were found for resistance that were not associated with FT or PH. Multilocus models with selected resistance-associated SNPs were fitted for each disease to estimate their effects on the other diseases. A QTL on chromosome 9 accounted for more than 15% of the phenotypic variance for the three diseases. When resistance-associated SNPs at this locus from both the tropical japonica and indica populations were incorporated into the model, resistance was improved for all three diseases with little impact on FT and PH.

Potential for re-emergence of wheat stem rust in the United Kingdom.

Wheat stem rust, a devastating disease of wheat and barley caused by the fungal pathogen Puccinia graminis f. sp. tritici, was largely eradicated in Western Europe during the mid-to-late twentieth century. However, isolated outbreaks have occurred in recent years. Here we investigate whether a lack of resistance in modern European varieties, increased presence of its alternate host barberry and changes in climatic conditions could be facilitating its resurgence. We report the first wheat stem rust occurrence in the United Kingdom in nearly 60 years, with only 20% of UK wheat varieties resistant to this strain. Climate changes over the past 25 years also suggest increasingly conducive conditions for infection. Furthermore, we document the first occurrence in decades of P. graminis on barberry in the UK . Our data illustrate that wheat stem rust does occur in the UK and, when climatic conditions are conducive, could severely harm wheat and barley production.

Use of AFLP markers to estimate molecular diversity of Phakopsora pachyrhizi

Background Asian soybean rust (SBR) caused by Phakopsora pachyrhizi Syd. & Syd., is one of the main diseases affecting soybean and has been reported as one of the most economically important fungal pathogens worldwide. Knowledge of the genetic diversity of this fungus should be considered when developing resistance breeding strategies. We aimed to analyze the genetic diversity of P. pachyrhizi combining simple sampling with a powerful and reproducible molecular technique. Results We employed Amplified Fragment Length Polymorphism (AFLP) technique for the amplification of P. pachyrhizi DNA extracted from naturally SBR-infected plants from 23 production fields. From a total of 1919 markers obtained, 77% were polymorphic. The high percentage of polymorphism and the Nei's genetic diversity coefficient (0.22) indicated high pathogen diversity. Analysis of molecular variance showed higher genetic variation within countries than among them. Temporal analysis showed a higher genetic variation within a year than between years. Cluster, phylogenetic and principal co-ordinate analysis showed that samples group by year of collection and then by country sampled. Conclusions The study proposed combining a simple collection of urediniospore with a subsequent analysis by AFLP was useful to examine the molecular polymorphism of samples of P. pachyrhizi collected and might have a significant contribution to the knowledge of its genetic diversity. Also, AFLP analysis is an important and potent molecular tool for the study of genetic diversity and could be useful to carry out wider genetic diversity studies.