Searching for Germplasm Resistant to Sheath Blight from the USDA Rice Core Collection

Jia, Yulin; Wang, Yan; Agrama, H. A.; Yeater, Kathleen M.; Li, Xiaobai; Hu, Biaolin; Moldenhauer, K. A. K.; McClung, Anna M.; Wu, Dianxing

doi:10.2135/cropsci2010.10.0581

Cited by 18 publications

(14 citation statements)

References 38 publications

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“…Table 2 shows that the panicle-related traits, straighthead susceptibility and blast resistance frequently appeared across all sets. This may be related to previous knowledge that the variations in panicle characteristics, straighthead susceptibility and blast resistance are strongly associated with the indica and japonica differentiation: panicles are long and sparse in indica but short and dense in japonica (Bai et al, 2016); straighthead resistance and blast resistance are greater in indica than japonica (Yan et al, 2005; Jia et al, 2011).…”

Section: Discussionsupporting

confidence: 53%

Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

Kim

2019

PeerJ

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In Oryza sativa, indica and japonica are pivotal subpopulations, and other subpopulations such as aus and aromatic are considered to be derived from indica or japonica. In this regard, Oryza sativa accessions are frequently viewed from the indica/japonica perspective. This study introduces a computational method for indica/japonica classification by applying phenotypic variables to the logistic regression model (LRM). The population used in this study included 413 Oryza sativa accessions, of which 280 accessions were indica or japonica. Out of 24 phenotypic variables, a set of seven phenotypic variables was identified to collectively generate the fully accurate indica/japonica separation power of the LRM. The resulting parameters were used to define the customized LRM. Given the 280 indica/japonica accessions, the classification accuracy of the customized LRM along with the set of seven phenotypic variables was estimated by 100 iterations of ten-fold cross-validations. As a result, the classification accuracy of 100% was achieved. This suggests that the LRM can be an effective tool to analyze the indica/japonica classification with phenotypic variables in Oryza sativa.

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Section: Discussionsupporting

confidence: 53%

Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

Kim

2019

PeerJ

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“…Most R genes in plants that have been cloned to date encode NBS-LRR proteins, and these large and abundant proteins are involved in the detection of diverse pathogens, including bacteria, fungi, viruses, nematodes, insects, and oomycetes (Meyers et al 2003;Belkhadir et al 2004). Many studies have shown pathogen differentiation in genetic structure and geographic distribution in rice (O. sativa) (Shang et al 2009;Jia et al 2011Jia et al , 2012, wheat (Triticum aestivum) (Stukenbrock et al 2006), barley (Hordeum uhulgare) (Zhang et al 1987), and banana (Musa ×paradisiaca) (Carlier et al 1996).…”

Section: Rga Markers Reveal Genetic Differentiation Of Resistant Genesmentioning

confidence: 99%

“…Similarly, the resistant alleles to sheath blight in IND and AUS are greater than in ARO, TEJ, and TRJ. Jia et al (2011) reported most germplasms resistant to sheath blight derived from Southern Asia (31%), followed by China (29%), Africa (15%), South America (13%), Central America (4%), Oceania (4%), and the North Pacific (2%).…”

Section: Rga Markers Reveal Genetic Differentiation Of Resistant Genesmentioning

confidence: 99%

Application of resistance gene analog markers to analyses of genetic structure and diversity in rice

Ren

Gao

et al. 2013

Genome

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Plant disease resistance gene analog (RGA) markers were designed according to the conserved sequence of known RGAs and used to map resistance genes. We used genome-wide RGA markers for genetic analyses of structure and diversity in a global rice germplasm collection. Of the 472 RGA markers, 138 were polymorphic and these were applied to 178 entries selected from the USDA rice core collection. Results from the RGA markers were similar between two methods, UPGMA and STRUCTURE. Additionally, the results from RGA markers in our study were agreeable with those previously reported from SSR markers, including cluster of ancestral classification, genetic diversity estimates, genetic relatedness, and cluster of geographic origins. These results suggest that RGA markers are applicable for analyses of genetic structure and diversity in rice. However, unlike SSR markers, the RGA markers failed to differentiate temperate japonica, tropical japonica, and aromatic subgroups. The restricted way for developing RGA markers from the cDNA sequence might limit the polymorphism of RGA markers in the genome, thus limiting the discriminatory power in comparison with SSR markers. Genetic differentiation obtained using RGA markers may be useful for defining genetic diversity of a suite of random R genes in plants, as many studies show a differentiation of resistance to a wide array of pathogens. They could also help to characterize the genetic structure and geographic distribution in crops, including rice, wheat, barley, and banana.

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“…The highest AUC obtained by the LRM accounts for 0.9977 (Table 1) Importantly, the panicle-related traits, straighthead susceptibility and blast resistance frequently appeared across all sets. These findings may be related to previous knowledge that the variability in panicle characteristics, straighthead susceptibility and blast resistance is strongly associated with the indica and japonica classification; panicles are long and sparse in indica but short and dense in japonica (Bai et al, 2016); straighthead resistance and blast resistance are greater in indica than japonica (Yan et al, 2005;Jia et al, 2011). Though I have presented only the best selection of predictors in each set, other selections of predictors producing a certain AUC level or greater (e.g.…”

Section: Discussionmentioning

confidence: 71%

Classifying Asian Rice Cultivars (Oryza sativa L.) into Indica and Japonica Using Logistic Regression Model with Publicly Available Phenotypic Data

Kim

2018

Preprint

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This article introduces how to implement the logistic regression model (LRM) with phenotypic variables for classifying Asian rice (Oryza sativa L.) cultivars into two pivotal subpopulations, indica and japonica. This study took advantage of publicly available data attached to a previous paper. The classification accuracy was assessed using an area under curve (AUC) of a receiver operating characteristic (ROC) curve. Given 24 phenotypic variables for 280 indica/japonica accessions, the LRMs were fitted with up to six phenotypic variables of all possible combinations; the highest AUC accounts for 0.9977, obtained with six variables including panicle number per plant, seed number per panicle, florets per panicle, panicle fertility, straighthead susceptibility and blast resistance. Overall, the more variables there are, the higher the resulting AUCs are. The ultimate purpose of this study is to demonstrate the indica/japonica prediction ability of the LRM when applied to unclassified Asian rice cultivars. To estimate the indica/japonica prediction accuracy, ten-fold cross-validations were conducted 100 times with the 280 indica/japonica accessions using the LRM with parameters that yielded the highest AUC. The resulting prediction accuracy accounted for 0.9779. This suggests that the LRM promises to be a highly effective indica/japonica prediction tool using phenotypic variables in Asian cultivated rice.

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Searching for Germplasm Resistant to Sheath Blight from the USDA Rice Core Collection

Cited by 18 publications

References 38 publications

Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

Classifying Oryza sativa accessions into Indica and Japonica using logistic regression model with phenotypic data

Application of resistance gene analog markers to analyses of genetic structure and diversity in rice

Classifying Asian Rice Cultivars (Oryza sativa L.) into Indica and Japonica Using Logistic Regression Model with Publicly Available Phenotypic Data

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