Genomic Prediction of Rust Resistance in Tetraploid Wheat under Field and Controlled Environment Conditions

Azizinia, Shiva; Bariana, Harbans; Kolmer, J. A.; Pasam, Raj K.; Bhavani, Sridhar; Chhetri, Mumta; Toor, Arvinder; Miah, Hanif; Hayden, Matthew; Carpio, Dunia Pino Del; Bansal, Urmil; Daetwyler, Hans D.

doi:10.3390/agronomy10111843

Cited by 7 publications

(10 citation statements)

References 66 publications

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“…The application of genomic prediction depends on the population size, marker density, model performance, heritability of the trait, training population size, and breeding population relatedness ( Daetwyler et al, 2008 ; Bassi et al, 2016 ). In wheat, genomic prediction studies have been reported to predict rust resistance in diverse wheat landraces ( Daetwyler et al, 2014 ; Crossa et al, 2016 ), landraces from Afghanistan ( Tehseen et al, 2021 ), tetraploid wheat ( Azizinia et al, 2020 ), and improved wheat germplasm ( Ornella et al, 2012 ; Rutkoski et al, 2014 ; Bassi et al, 2016 ; Juliana et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats

et al. 2022

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Stripe rust caused by Puccnina striiformis (Pst) is an economically important disease attacking wheat all over the world. Identifying and deploying new genes for Pst resistance is an economical and long-term strategy for controlling Pst. A genome-wide association study (GWAS) using single nucleotide polymorphisms (SNPs) and functional haplotypes were used to identify loci associated with stripe rust resistance in synthetic-derived (SYN-DER) wheats in four environments. In total, 92 quantitative trait nucleotides (QTNs) distributed over 65 different loci were associated with resistance to Pst at seedling and adult plant stages. Nine additional loci were discovered by the linkage disequilibrium-based haplotype-GWAS approach. The durable rust-resistant gene Lr34/Yr18 provided resistance in all four environments, and against all the five Pst races used in this study. The analysis identified several SYN-DER accessions that carried major genes: either Yr24/Yr26 or Yr32. New loci were also identified on chr2B, chr5B, and chr7D, and 14 QTNs and three haplotypes identified on the D-genome possibly carry new alleles of the known genes contributed by the Ae. tauschii founders. We also evaluated eleven different models for genomic prediction of Pst resistance, and a prediction accuracy up to 0.85 was achieved for an adult plant resistance, however, genomic prediction for seedling resistance remained very low. A meta-analysis based on a large number of existing GWAS would enhance the identification of new genes and loci for stripe rust resistance in wheat. The genetic framework elucidated here for stripe rust resistance in SYN-DER identified the novel loci for resistance to Pst assembled in adapted genetic backgrounds.

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Section: Introductionmentioning

confidence: 99%

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats

et al. 2022

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“…In all three models, the mean stripe rust prediction accuracies of the six Canadian spring wheat populations in the CV2 and CV0 scenarios varied from 0.54 to 0.84 (Table 2), which was greater than several other studies conducted on wheat. Previous GS studies conducted in non-Canadian wheat germplasm reported highly variable accuracies, which ranged from 0.39 to 0.56 in tetraploid wheat [37], from 0.16 to 0.72 in winter wheat [18][19][20]38], and from 0.12 to 0.79 in diverse spring wheat populations [17,18,21,39,40]. Overall, our results provided a strong justification for wheat breeders for integrating GS in developing stripe rust resistance spring wheat germplasm using the CV2 and/or CV0 scenarios regardless of the models and genetic background, which would reduce the cost, resources, and logistics associated with field evaluation by at least 25%.…”

Section: Discussionmentioning

confidence: 98%

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

Semagn

Iqbal

Jarquín

et al. 2022

Plants

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Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

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“…This can invoke a strong discrepancy between the training population and selection candidates in the genomic prediction of disease resistance, since the phenotypic data of the training population does not reflect the current situation of the selection candidates if new virulent pathogen races occur in a given year of selection. Despite the presence of strong race dynamics, few genomic prediction studies distinguishing between race-nonspecific QDR and R-gene-mediated race-specific resistance for the wheat rusts (Rutkoski et al 2011(Rutkoski et al , 2015bJuliana et al 2017;Azizinia et al 2020). Focusing exclusively on the genetic improvement of QDR by using training and selection populations that were devoid of race-specific R-genes resulted thereby in some genetic gain for stem rust resistance in wheat (Rutkoski et al 2015b), while high prediction accuracies were observed in the presence of race-specific R-genes if they were effective in both the training population and the population of selection candidates (Juliana et al 2017;Azizinia et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection

Michel

Löschenberger²,

Ametz³

et al. 2023

Theor Appl Genet

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Key message A novel genomic selection strategy offers the unique opportunity to develop qualitative race-specific resistant varieties that possess high levels of the more durable quantitative race-nonspecific resistance in their genetic background. Abstract Race-specific qualitative resistance genes (R-genes) are conferring complete resistance in many pathosystems, but are frequently overcome by new virulent pathogen races. Once the deployed R-genes are overcome, a wide variation of quantitative disease resistance (QDR) can be observed in a set of previously race-specific, i.e., completely resistant genotypes—a phenomenon known as “vertifolia effect.” This race-nonspecific QDR is considered to be more durable in the long term, but provides merely a partial protection against pathogens. This simulation study aimed to detangle race-specific R-gene-mediated resistance of pending selection candidates and the QDR in their genetic background by employing different genomic selection strategies. True breeding values that reflected performance data for rust resistance in wheat were simulated, and used in a recurrent genomic selection based on several prediction models and training population designs. Using training populations that were devoid of race-specific R-genes was thereby pivotal for an efficient improvement of QDR in the long term. Marker-assisted preselection for the presence of R-genes followed by a genomic prediction for accumulating the many small to medium effect loci underlying QDR in the genetic background of race-specific resistant genotypes appeared furthermore to be a promising approach to select simultaneously for both types of resistance. The practical application of such a knowledge-driven genomic breeding strategy offers the opportunity to develop varieties with multiple layers of resistance, which have the potential to prevent intolerable crop losses under epidemic situations by displaying a high level of QDR even when race-specific R-genes have been overcome by evolving pathogen populations.

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Genomic Prediction of Rust Resistance in Tetraploid Wheat under Field and Controlled Environment Conditions

Cited by 7 publications

References 66 publications

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection

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