Discovery of a susceptibility factor for Fusarium head blight on chromosome 7A of wheat

Chhabra, Bhavit; Tiwari, Vijay K.; Gill, Bikram S.; Dong, Yanhong; Rawat, Nidhi

doi:10.1007/s00122-021-03825-y

Cited by 17 publications

(16 citation statements)

References 67 publications

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“…Grain yield and grain protein content are highly important target traits in wheat breeding programs and for other cereal grains (Chhabra et al, 2021;Sandhu et al, 2021c). The generally negative correlation between them, along with lower heritability, creates a problem in efficiently select-ing both the traits simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

et al. 2021

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Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine-and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine-and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine-and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014)(2015)(2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0-28.5 and −0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine-and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine-and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine-and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

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Section: Discussionmentioning

confidence: 99%

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

et al. 2021

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“…A recent study by Larkin et al (2020) also discovered a QTL associated with DON reduction around the same region at 50.1 Mb independent of our locus. Recently, a major-effect susceptibility gene, Sf-Fhb-7AS, was mapped to a 48.5-50.5 Mb pericentromeric region of chromosome 7AS and deleting the loci imparted 50-60% type II FHB broad-spectrum resistance implying its potential role to enhance resistance mechanism (Chhabra et al, 2021).…”

Section: 3mentioning

confidence: 99%

Genetics of Fusarium head blight resistance in soft red winter wheat using a genome‐wide association study

et al. 2022

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Host resistance is an effective and sustainable approach to manage the negative impact of Fusarium head blight (FHB) on wheat (Triticum aestivum L.) grain yield and quality. The objective of this study was to characterize the phenotypic responses and identify quantitative trait loci (QTL) conditioning different FHB resistance types using a panel of 236 elite soft red winter wheat (SRWW) lines in a genome‐wide association study (GWAS). The panel was phenotyped for five FHB and three morphological traits under two field and two greenhouse environments in 2018–2019 and 2019–2020. We identified 160 significant marker–trait associations (MTAs) for FHB traits and 11 MTAs for plant height. Eleven QTL showed major effects and explained >10% phenotypic variation (PV) for FHB resistance. Among these major loci, three QTL were stable and five QTL exhibited a pleiotropic effect. The QTL QFhb‐3BL, QFhb‐5AS, QFhb‐5BL, QFhb‐7AS.1, QFhb‐7AS.2, and QFhb‐7BS are presumed to be novel. Pyramiding multiple resistance alleles from all the major‐effect QTL resulted in a significant reduction in FHB incidence, severity, index, deoxynivalenol (DON), and Fusarium‐damaged kernel (FDK) by 17, 43, 45, 55, and 25%, respectively. Further validation of these QTL could potentially facilitate successful introgression of these resistance loci in new cultivars for improved FHB resistance in breeding programs.

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“…EMS mutagenized populations can be utilized to find resistance against FHB (Rawat et al, 2019;Singh et al, 2019;Chhabra et al, 2021a). Chromosome arms 2DS, 3DL, 4DS and 7AS were also reported in the literature to be harboring major effect susceptibility genes for FHB (Basnet et al, 2012;Garvin et al, 2015;Hales et al, 2020;Chhabra et al, 2021b).…”

Section: Breeding For Fusarium Head Blight Resistancementioning

confidence: 99%

Important wheat diseases in the US and their management in the 21st century

Singh

Chhabra²,

Raza³

et al. 2023

Front. Plant Sci.

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Wheat is a crop of historical significance, as it marks the turning point of human civilization 10,000 years ago with its domestication. Due to the rapid increase in population, wheat production needs to be increased by 50% by 2050 and this growth will be mainly based on yield increases, as there is strong competition for scarce productive arable land from other sectors. This increasing demand can be further achieved using sustainable approaches including integrated disease pest management, adaption to warmer climates, less use of water resources and increased frequency of abiotic stress tolerances. Out of 200 diseases of wheat, 50 cause economic losses and are widely distributed. Each year, about 20% of wheat is lost due to diseases. Some major wheat diseases are rusts, smut, tan spot, spot blotch, fusarium head blight, common root rot, septoria blotch, powdery mildew, blast, and several viral, nematode, and bacterial diseases. These diseases badly impact the yield and cause mortality of the plants. This review focuses on important diseases of the wheat present in the United States, with comprehensive information of causal organism, economic damage, symptoms and host range, favorable conditions, and disease management strategies. Furthermore, major genetic and breeding efforts to control and manage these diseases are discussed. A detailed description of all the QTLs, genes reported and cloned for these diseases are provided in this review. This study will be of utmost importance to wheat breeding programs throughout the world to breed for resistance under changing environmental conditions.

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Discovery of a susceptibility factor for Fusarium head blight on chromosome 7A of wheat

Abstract: HighlightDiscovery and mapping of a conserved susceptibility factor located on the short arm of wheat chromosome 7A whose deletion makes plants resistant to Fusarium Head Blight..

Cited by 17 publications

References 67 publications

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

Genetics of Fusarium head blight resistance in soft red winter wheat using a genome‐wide association study

Important wheat diseases in the US and their management in the 21st century

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