Semi-dwarfing Rht-B1 and Rht-D1 loci of wheat differ significantly in their influence on resistance to Fusarium head blight

Srinivasachary,; Gosman, N.; Steed, Andrew; Hollins, T. W.; Bayles, R. A.; Jennings, P.J.; Nicholson, P.

doi:10.1007/s00122-008-0930-0

Cited by 125 publications

(126 citation statements)

References 27 publications

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“…The latter pair of scenarios is motivated by the fact that taller plants are less susceptible to Fusarium than shorter plants. The LD analysis in Mackay et al (2014) suggests that this SNP is located in chromosome 4D in this population and that it may be tagging RhtD1b, a dwarfing gene that is also closely associated with resistance to Fusarium (Srinivasachary et al 2009). In addition, G266 (BobWhite_c30043_150) appears to be located in chromosome 2D and to be tagging Ppd-D1, which controls photoperiod response.…”

Section: Resultsmentioning

confidence: 80%

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Multiple Quantitative Trait Analysis Using Bayesian Networks

et al. 2014

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Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

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Section: Resultsmentioning

confidence: 80%

“…This is expected because susceptibility to Fusarium is known to be positively related to HT (Srinivasachary et al 2009), which in turn affects YLD. Conditional on each quartile of HT, FUS has a negative effect on YLD ranging from 20.04 to 20.06.…”

Section: Resultsmentioning

confidence: 99%

Multiple Quantitative Trait Analysis Using Bayesian Networks

et al. 2014

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“…Rht-D1b on chromosome 4D is strongly associated with reduced resistance to initial infection, while the role of the homoeologous allele Rht-B1b on chromosome 4B is less clear (Draeger et al 2007;Holzapfel et al 2008;Srinivasachary et al 2008Srinivasachary et al , 2009Voss et al 2008;Miedaner et al 2011).…”

Section: Morphological and Phenological Traitsmentioning

confidence: 99%

Breeding strategies and advances in line selection for Fusarium head blight resistance in wheat

Steiner

Buerstmayr

Michel

et al. 2017

Trop. plant pathol.

140

143

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Fusarium head blight (FHB) is a fungal disease of worldwide importance to small grain cereals that may lead to severe losses in both yield and quality. The development of resistant varieties is the most effective approach for managing the disease. Genetic variation for FHB resistance is large, including 'exotic' and 'native' wheat germplasm. Methods for selecting improved lines include: 1) phenotypic selection with direct symptom evaluation; 2) marker-assisted selection for well-characterized QTL and 3) genomic selection employing genome-wide prediction models. Breeding programs need to find the optimal deployment of the complementary approaches according to their available facilities, resources and requirements. This review aims to summarize recent advances in FHB resistance breeding, thereby discussing the importance of morphological traits like the extent of retained anthers after flowering, its suitability for indirect selection and the pronounced association of the semi-dwarfing allele Rht-D1b with increased anther retention and FHB severity. Markerassisted selection is successfully applied to select for largeeffect QTL, especially for the most prominent resistance QTL Fhb1 in bread wheat, as well as in durum wheat as recently demonstrated. The resistance locus Fhb1 has been partly elucidated, a pore-forming toxin-like gene confers resistance against fungal spread. Genomic selection for FHB resistance appears promising especially for breeding programs deploying 'native' resistance sources with many small-effect QTL.

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“…The marker-trait association for Rht-B1 was not significant (P40.05), which could be explained by the low allele frequency of the mutant allele detected in only 26 of the investigated varieties. Another explanation may be given by the known difference in the effects of Rht-B1 and Rht-D1 on FHB (Srinivasachary et al, 2009). The Rht-D1 gene significantly influences both type 1 resistance (resistance to initial infection) and type 2 resistance (resistance to spread of the fungus within the spike), whereas Rht-B1 has significant influence only on type 1 resistance.…”

Section: Marker-assisted Selection For Fhb Resistancementioning

confidence: 99%

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.)

et al. 2014

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Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold crossvalidations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker-trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low-to medium-density SNP arrays.

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Semi-dwarfing Rht-B1 and Rht-D1 loci of wheat differ significantly in their influence on resistance to Fusarium head blight

Cited by 125 publications

References 27 publications

Multiple Quantitative Trait Analysis Using Bayesian Networks

Multiple Quantitative Trait Analysis Using Bayesian Networks

Breeding strategies and advances in line selection for Fusarium head blight resistance in wheat

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.)

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