Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae)

Huang, Mao Fang; Balimponya, Elias G; Mgonja, Emmanuel; McHale, Leah K.; Luzi-Kihupi, Ashura; Wang, Guo‐Liang; Sneller, Clay

doi:10.1007/s11032-019-1023-2

Cited by 48 publications

(31 citation statements)

References 48 publications

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“…In GS, a training "population" comprising individuals with wholegenome marker data and phenotypes is used to train prediction models and estimate marker effects, which are then used to obtain genomic-estimated breeding values of individuals that have not been phenotyped but only genotyped (referred to as "selection candidates") (Meuwissen et al, 2001). As several studies have demonstrated GS to be promising for rice blast (Huang et al, 2019) and wheat diseases such FHB, rusts, Septoria tritici blotch, Stagonospora nodorum blotch, and tan spot (Rutkoski et al, 2012;Juliana et al, 2017a,b), it is an attractive breeding strategy that can be effectively integrated in wheat blast resistance breeding to minimize time, cost, and resources for blast phenotyping in the field. In addition, GS can be potentially used by breeding programs to select individuals for resistant line advancement and crossing prior to phenotyping, and to increase the selection intensity by scaling-up selections for blast resistance to early generations of the breeding cycle, where large segregating populations pose a challenge for blast evaluation.…”

Section: Genomic Selectionmentioning

confidence: 99%

Wheat Blast: A Disease Spreading by Intercontinental Jumps and Its Management Strategies

et al. 2021

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Wheat blast (WB) caused by Magnaporthe oryzae pathotype Triticum (MoT) is an important fungal disease in tropical and subtropical wheat production regions. The disease was initially identified in Brazil in 1985, and it subsequently spread to some major wheat-producing areas of the country as well as several South American countries such as Bolivia, Paraguay, and Argentina. In recent years, WB has been introduced to Bangladesh and Zambia via international wheat trade, threatening wheat production in South Asia and Southern Africa with the possible further spreading in these two continents. Resistance source is mostly limited to 2NS carriers, which are being eroded by newly emerged MoT isolates, demonstrating an urgent need for identification and utilization of non-2NS resistance sources. Fungicides are also being heavily relied on to manage WB that resulted in increasing fungal resistance, which should be addressed by utilization of new fungicides or rotating different fungicides. Additionally, quarantine measures, cultural practices, non-fungicidal chemical treatment, disease forecasting, biocontrol etc., are also effective components of integrated WB management, which could be used in combination with varietal resistance and fungicides to obtain reasonable management of this disease.

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Section: Genomic Selectionmentioning

confidence: 99%

Wheat Blast: A Disease Spreading by Intercontinental Jumps and Its Management Strategies

et al. 2021

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“…Genomic selection has been implemented in many crops, including wheat, chickpea, cassava and rice (Roorkiwal et al, 2016;Crossa et al, 2017;Wolfe et al, 2017;Huang et al, 2019), and the number of programs that are moving from "conventional" to GS is growing. Results in wheat show that genomic predictions used early in the breeding cycle led to a substantial increase in performance in later generations (Bonnett et al, 2021 this issue).…”

Section: Why Genomics For Improving Breeding?mentioning

confidence: 99%

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data

et al. 2021

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“…For these reasons, GS has been implemented in many crops such as maize and wheat ( Triticum aestivum L.) (Crossa et al., 2017), chickpea ( Cicer arietinum L.) (Roorkiwal et al., 2016), oil palm ( Elaeis guineensis Jacq.) (Kwong et al., 2017), cassava ( Manihot esculenta Crantz) (Wolfe et al., 2017), and rice ( Oryza sativa L.) (Huang et al., 2019), among others. Nowadays, many breeding programs around the world are starting to move from conventional breeding programs (based on phenotyping) to GS (based on predictions of breeding values or phenotypic values), because there is increasing evidence that GS can significantly save resources and shorten the generation interval, as mentioned above (Crossa et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Application of a Poisson deep neural network model for the prediction of count data in genome‐based prediction

et al. 2021

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Genomic selection (GS) is revolutionizing conventional ways of developing new plants and animals. However, because it is a predictive methodology, GS strongly depends on statistical and machine learning to perform these predictions. For continuous outcomes, more models are available for GS. Unfortunately, for count data outcomes, there are few efficient statistical machine learning models for large datasets or for datasets with fewer observations than independent variables. For this reason, in this paper, we applied the univariate version of the Poisson deep neural network (PDNN) proposed earlier for genomic predictions of count data. The model was implemented with (a) the negative log-likelihood of Poisson distribution as the loss function, (b) the rectified linear activation unit as the activation function in hidden layers, and (c) the exponential activation function in the output layer. The advantage of the PDNN model is that it captures complex patterns in the data by implementing many nonlinear transformations in the hidden layers. Moreover, since it was implemented in Tensorflow as the back-end, and in Keras as the front-end, the model can

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Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae)

Cited by 48 publications

References 48 publications

Wheat Blast: A Disease Spreading by Intercontinental Jumps and Its Management Strategies

Wheat Blast: A Disease Spreading by Intercontinental Jumps and Its Management Strategies

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data

Application of a Poisson deep neural network model for the prediction of count data in genome‐based prediction

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