Deciphering genotype-by-environment interaction for targeting test environments and genotypes resistant to wheat stem rust disease (original) (raw)
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Frontiers in Plant Science, 2019
Rust caused by Uromyces viciae-fabae is a major biotic constraint to field pea (Pisum sativum L.) cultivation worldwide. Deployment of host-pathogen interaction and resistant phenotype is a modest strategy for controlling this intricate disease. However, resistance against this pathogen is partial and influenced by environmental factors. Therefore, the magnitude of environmental and genotype-by-environment interaction was assessed to understand the dynamism of resistance and identification of durable resistant genotypes, as well as ideal testing locations for rust screening through multi-location and multi-year evaluation. Initial screening was conducted with 250 diverse genotypes at rust hot spots. A panel of 23 promising field pea genotypes extracted from initial evaluation was further assessed under inoculated conditions for rust disease for two consecutive years at six locations in India. Integration of GGE biplot analysis and multiple comparisons tests detected a higher proport...
Genomic Selection for Quantitative Adult Plant Stem Rust Resistance in Wheat
The Plant Genome, 2014
Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequencing (GBS). Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G-BLUP), Bayesian Lasso (BL), and Bayes Cp (BCp). We found the Sr2 region to play an important role in APR in this germplasm. A model using Sr2 linked markers as fixed effects in G-BLUP was more accurate than MLR with Sr2 linked markers (p-value = 0.12), and ordinary G-BLUP (p-value = 0.15). Incorporating seedling phenotype information as fixed effects in G-BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.
Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
Theoretical and Applied Genetics, 2017
estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spacesmarkers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/ or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31-0.74 for LR seedling, 0.12-0.56 for LR APR, 0.31-0.65 for SR APR, 0.70-0.78 for YR seedling, and 0.34-0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline. Abbreviations APR Adult plant resistance BLUP Best linear unbiased prediction BVs Breeding values CIMMYT Centro Internacional de Mejoramiento de Maíz y Trigo GBLUP Genomic best linear unbiased prediction GBLUP A Genomic best linear unbiased prediction with selected loci as fixed effects GBS Genotyping-by-sequencing
Agronomy
Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all tra...
Genomic Prediction of Genetic Values for Resistance to Wheat Rusts
The Plant Genome, 2012
Durable resistance to the rust diseases of wheat (Triticum aestivum L.) can be achieved by developing lines that have racenonspecifi c adult plant resistance conferred by multiple minor slow-rusting genes. Genomic selection (GS) is a promising tool for accumulating favorable alleles of slow-rusting genes. In this study, fi ve CIMMYT wheat populations evaluated for resistance were used to predict resistance to stem rust (Puccinia graminis) and yellow rust (Puccinia striiformis) using Bayesian least absolute shrinkage and selection operator (LASSO) (BL), ridge regression (RR), and s upport vector regression with linear or radial basis function kernel models. All parents and populations were genotyped using 1400 Diversity Arrays Technology markers and different prediction problems were assessed. Results show that prediction ability for yellow rust was lower than for stem rust, probably due to differences in the conditions of infection of both diseases. For within population and environment, the correlation between predicted and observed values (Pearson's correlation [ρ]) was greater than 0.50 in 90% of the evaluations whereas for yellow rust, ρ ranged from 0.0637 to 0.6253. The BL and RR models have similar prediction ability, with a slight superiority of the BL confi rming reports about the additive nature of rust resistance. When making predictions between environments and/or between populations, including information from another environment or environments or another population or populations improved prediction. R UST DISEASES are an important cause of wheat production losses worldwide. Puccinia graminis (stem rust) and Puccinia striiformis (yellow rust) continue to cause major economic losses in various parts of the world and hence receive attention in wheat breeding programs. New races of these fungi have caused yield losses even in areas where the rusts have rarely been detected, and they are more threatening to wheat worldwide than older races (Singh et al., 2011). Regions with vulnerability to yellow rust include, among others, the United States, Asia, and Oceania (Wellings, 2011). Stem rust has also become epidemic in Africa (Singh et al., 2011). Inheritance of rust resistance in wheat can be either qualitative or quantitative. Quantitative disease resistance is more durable but more diffi cult to evaluate because it is expressed in mature plants (adult plant resistance) (Rutkoski et al., 2011). Phenotyping adult plant resistance in large populations is expensive and labor intensive. Expertise is needed because expression is aff ected by, among other factors, the inoculum load and sequential infection (Hickey et al., 2012). Regarding fi nancial costs, in CIMMYT the most basic fi eld assay costs around US$30 to 40 per genotype (with two replications per location); however, if greenhouse screening is required, the cost increases signifi cantly. In
A rapid phenotyping method for adult plant resistance to leaf rust in wheat
Background: Leaf rust (LR), caused by Puccinia triticina and is an important disease of wheat (Triticum aestivum L.). The most sustainable method for controlling rust diseases is deployment of cultivars incorporating adult plant resistance (APR). However, phenotyping breeding populations or germplasm collections for resistance in the field is dependent on weather conditions and limited to once a year. In this study, we explored the ability to phenotype APR to LR under accelerated growth conditions (AGC; i.e. constant light and controlled temperature) using a method that integrates assessment at both seedling and adult growth stages. A panel of 21 spring wheat genotypes, including disease standards carrying known APR genes (i.e. Lr34 and Lr46) were characterised under AGC and in the field. Results: Disease response displayed by adult wheat plants grown under AGC (i.e. flag-2 leaf) was highly correlated with field-based measures (R 2 = 0.77). The integrated method is more efficient—requiring less time, space, and labour compared to traditional approaches that perform seedling and adult plant assays separately. Further, this method enables up to seven consecutive adult plant LR assays compared to one in the field. Conclusion: The integrated seedling and adult plant phenotyping method reported in this study provides a great tool for identifying APR to LR. Assessing plants at early growth stages can enable selection for desirable gene combinations and crossing of the selected plants in the same plant generation. The method has the potential to be scaled-up for screening large numbers of fixed lines and segregating populations. This strategy would reduce the time required for moving APR genes into adapted germplasm or combining traits in top crosses in breeding programs. This method could accelerate selection for resistance factors effective across diverse climates by conducting successive cycles of screening performed at different temperature regimes.
Uganda Journal of Agricultural Sciences
The objective of this study was to assess diversity for stem rust and stripe rust resistance in an international wheat screening nursery under greenhouse conditions using pathotypes with known avirulence/ virulence profiles. A set of 95 entries of an international wheat screening nursery collected from material generated by staff of the International Maize and Wheat Improvement Centre (CIMMYT) was tested against seven Australian Pgt and five Pst pathotypes through artificial inoculation under the greenhouse conditions using standard procedures. Ten all-stage stem rust resistance genes (Sr8a, Sr8b, Sr9b, Sr12, Sr17, Sr23, Sr24, Sr30, Sr31 and Sr38) and seven all-stage stripe rust resistance genes (Yr3, Yr4, Yr6, Yr9, Yr17, Yr27 and Yr34) were postulated either singly or in combinations based on seedling responses of test entries against pathotypes differing in virulence for commonly deployed genes. Sr30 and Sr38 were the most common stem rust resistance genes in this nursery. The Sr3...
Theoretical and Applied Genetics
Key message We used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods. Abstract Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008–2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive a...
Mapping and Validation of Stem Rust Resistance Loci in Spring Wheat Line CI 14275
Frontiers in Plant Science, 2021
Stem rust caused by Puccinia graminis f. sp. tritici (Pgt) remains a constraint to wheat production in East Africa. In this study, we characterized the genetics of stem rust resistance, identified QTLs, and described markers associated with stem rust resistance in the spring wheat line CI 14275. The 113 recombinant inbred lines, together with their parents, were evaluated at the seedling stage against Pgt races TTKSK, TRTTF, TPMKC, TTTTF, and RTQQC. Screening for resistance to Pgt races in the field was undertaken in Kenya, Ethiopia, and the United States in 2016, 2017, and 2018. One gene conferred seedling resistance to race TTTTF, likely Sr7a. Three QTL were identified that conferred field resistance. QTL QSr.cdl-2BS.2, that conferred resistance in Kenya and Ethiopia, was validated, and the marker Excalibur_c7963_1722 was shown to have potential to select for this QTL in marker-assisted selection. The QTL QSr.cdl-3B.2 is likely Sr12, and QSr.cdl-6A appears to be a new QTL. This is...