scholarly journals Expression polymorphism at theARPC4locus links the actin cytoskeleton with quantitative disease resistance toSclerotinia sclerotioruminArabidopsis thaliana

2018 ◽  
Vol 222 (1) ◽  
pp. 480-496 ◽  
Author(s):  
Thomas Badet ◽  
Ophélie Léger ◽  
Marielle Barascud ◽  
Derry Voisin ◽  
Pierre Sadon ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Actin Cytoskeleton ◽  
Quantitative Disease Resistance ◽  
Expression Polymorphism

BnaMPK6 is a determinant of quantitative disease resistance against Sclerotinia sclerotiorum in oilseed rape

Plant Science ◽  
2020 ◽  
Vol 291 ◽  
pp. 110362
Author(s):  
Zheng Wang ◽  
Feng-Yun Zhao ◽  
Min-Qiang Tang ◽  
Ting Chen ◽  
Ling-Li Bao ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Oilseed Rape ◽  
Sclerotinia Sclerotiorum ◽  
Quantitative Disease Resistance

scholarly journals Robustness of plant quantitative disease resistance is provided by a decentralized immune network

2020 ◽  
Vol 117 (30) ◽  
pp. 18099-18109 ◽  
Author(s):  
Florent Delplace ◽  
Carine Huard-Chauveau ◽  
Ullrich Dubiella ◽  
Mehdi Khafif ◽  
Eva Alvarez ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Xanthomonas Campestris ◽  
Mutational Analysis ◽  
Molecular Networks ◽  
Integrative Approach ◽  
Immune Network ◽  
Functional Modules ◽  
Limited Information ◽  
Quantitative Disease Resistance ◽  
Gene Modules

Quantitative disease resistance (QDR) represents the predominant form of resistance in natural populations and crops. Surprisingly, very limited information exists on the biomolecular network of the signaling machineries underlying this form of plant immunity. This lack of information may result from its complex and quantitative nature. Here, we used an integrative approach including genomics, network reconstruction, and mutational analysis to identify and validate molecular networks that control QDR inArabidopsis thalianain response to the bacterial pathogenXanthomonas campestris. To tackle this challenge, we first performed a transcriptomic analysis focused on the early stages of infection and using transgenic lines deregulated for the expression ofRKS1, a gene underlying a QTL conferring quantitative and broad-spectrum resistance toX.campestris.RKS1-dependent gene expression was shown to involve multiple cellular activities (signaling, transport, and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) responses already characterized inA.thaliana. Protein–protein interaction network reconstitution then revealed a highly interconnected and distributed RKS1-dependent network, organized in five gene modules. Finally, knockout mutants for 41 genes belonging to the different functional modules of the network revealed that 76% of the genes and all gene modules participate partially in RKS1-mediated resistance. However, these functional modules exhibit differential robustness to genetic mutations, indicating that, within the decentralized structure of the QDR network, some modules are more resilient than others. In conclusion, our work sheds light on the complexity of QDR and provides comprehensive understanding of a QDR immune network.

Shades of gray: the world of quantitative disease resistance

2009 ◽  
Vol 14 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Jesse A. Poland ◽  
Peter J. Balint-Kurti ◽  
Randall J. Wisser ◽  
Richard C. Pratt ◽  
Rebecca J. Nelson
Keyword(s):  
Disease Resistance ◽  
Quantitative Disease Resistance ◽  
The World

scholarly journals Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize

Genetics ◽  
2014 ◽  
Vol 198 (1) ◽  
pp. 333-344 ◽  
Author(s):  
Tiffany M. Jamann ◽  
Jesse A. Poland ◽  
Judith M. Kolkman ◽  
Laurie G. Smith ◽  
Rebecca J. Nelson
Keyword(s):  
Disease Resistance ◽  
Resistance Locus ◽  
Quantitative Disease Resistance ◽  
Genomic Complexity

scholarly journals Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

2021 ◽  
Vol 12 ◽  
Author(s):  
Lance F. Merrick ◽  
Adrienne B. Burke ◽  
Xianming Chen ◽  
Arron H. Carter
Keyword(s):  
Disease Resistance ◽  
Genomic Selection ◽  
Stripe Rust ◽  
Fixed Effects ◽  
Prediction Accuracy ◽  
Major Gene ◽  
Gene Markers ◽  
Quantitative Disease Resistance ◽  
Minor Genes ◽  
Population Type

Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016–2020) and 475 diversity panel (DP) lines from 4 years (2013–2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

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