Molecular Diagnostic of Both Brown and Orange Sugarcane Rust and Evaluation of Sugarcane Brown Rust Resistance in Tucuman, Argentina, Using Molecular Markers Associated with Bru1 a Broad-Range Resistance Allele

Sugar Tech ◽  
2015 ◽  
Vol 18 (4) ◽  
pp. 414-419 ◽  
Author(s):  
J. Racedo ◽  
M. F. Perera ◽  
R. Bertani ◽  
C. Funes ◽  
V. González ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Rust Resistance ◽  
Brown Rust ◽  
Molecular Diagnostic ◽  
Resistance Allele ◽  
Sugarcane Brown Rust

Developing genetically segregating populations for localization of novel sugarcane brown rust resistance genes

Euphytica ◽  
2019 ◽  
Vol 215 (10) ◽  
Author(s):  
Xiao-Yan Wang ◽  
Wen-Feng Li ◽  
Ying-Kun Huang ◽  
Hong-Li Shan ◽  
Rong-Yue Zhang ◽  
...  
Keyword(s):  
Resistance Genes ◽  
Rust Resistance ◽  
Brown Rust ◽  
Rust Resistance Genes ◽  
Sugarcane Brown Rust

scholarly journals Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandre Hild Aono ◽  
Estela Araujo Costa ◽  
Hugo Vianna Silva Rody ◽  
James Shiniti Nagai ◽  
Ricardo José Gonzaga Pimenta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rust Resistance ◽  
Predictive Accuracy ◽  
Explanatory Power ◽  
Genotyping By Sequencing ◽  
Brown Rust ◽  
Learning Approaches ◽  
Molecular Approaches ◽  
Genomic Regions ◽  
Sugarcane Brown Rust

AbstractSugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

Screening of Polymorphic SSR Molecular Markers Between Resistant and Susceptible Parents for Localization of Brown Rust Resistance Gene

Sugar Tech ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 1-7
Author(s):  
Hongli Shan ◽  
Wenfeng Li ◽  
Yingkun Huang ◽  
Xiaoyan Wang ◽  
Rongyue Zhang ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Resistance Gene ◽  
Rust Resistance ◽  
Brown Rust ◽  
Rust Resistance Gene

scholarly journals Assessing sugarcane brown rust resistance using Image analysis

Bionatura ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 1698-1703
Author(s):  
Yaquelin Puchades-Izaguirre Puchades-Izaguirre ◽  
Mónica Tamayo-Isaac ◽  
Wilfre Abiche-Maceo ◽  
Reynaldo Rodríguez-Gross ◽  
María La O Hechavarría ◽  
...  
Keyword(s):  
Image Analysis ◽  
Disease Resistance ◽  
Rust Resistance ◽  
Digital Image Analysis ◽  
Brown Rust ◽  
Disease Assessment ◽  
Visual Estimation ◽  
Precise Method ◽  
Sugarcane Brown Rust

Image analysis provides an accurate and precise method of pest evaluation. This work's objective was to compare the usefulness of the ImageJ® 1.43u image processor and visual estimation as methods to characterize brown rust lesions and estimate the resistance of new sugarcane cultivars. For this, leaves images of 10 cultivars were captured, and the parameters quantity, most regular size of the pustules, and leaf area affected were determined. The data were correlated with the eight control (standard) genotypes' evaluations to obtain a classification of disease resistance. The results showed that the software's determinations were the most accurate, although all the methods were reliable for rating the reaction to brown rust. Therefore, it is proposed to move away from visual disease assessment toward a system based on digital image analysis.

scholarly journals Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

2020 ◽  
Author(s):  
Alexandre Hild Aono ◽  
Estela Araujo Costa ◽  
Hugo Vianna Silva Rody ◽  
James Shiniti Nagai ◽  
Ricardo José Gonzaga Pimenta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Rust Resistance ◽  
Predictive Accuracy ◽  
Explanatory Power ◽  
Genotyping By Sequencing ◽  
Brown Rust ◽  
Learning Approaches ◽  
Genomic Regions ◽  
Sugarcane Brown Rust

ABSTRACTSugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

Identification and Validation of Molecular Markers Associated with Pachymetra Root Rot and Brown Rust Resistance in Sugarcane Using Map- and Association-based Approaches

2005 ◽  
Vol 16 (2) ◽  
pp. 151-161 ◽  
Author(s):  
C. L. McIntyre ◽  
V. A. Whan ◽  
B. Croft ◽  
R. Magarey ◽  
G. R. Smith
Keyword(s):  
Molecular Markers ◽  
Root Rot ◽  
Rust Resistance ◽  
Brown Rust

Genetic analysis of sugarcane brown rust resistance genes in wild sugarcane germplasm Erianthus rockii ‘Yundian 95-19’ and Erianthus rockii ‘Yundian 95-20’

2019 ◽  
Vol 17 (5) ◽  
pp. 460-463
Author(s):  
Rong-Yue Zhang ◽  
Wen-Feng Li ◽  
Ying-Kun Huang ◽  
Xin Lu ◽  
Xiao-Yan Wang ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Rust Resistance ◽  
Dominant Gene ◽  
Segregation Ratio ◽  
Brown Rust ◽  
Inheritance Of Resistance ◽  
Single Dominant Gene ◽  
Sugarcane Germplasm ◽  
Rust Resistance Genes ◽  
Sugarcane Brown Rust

AbstractWe assessed inheritance of resistance to sugarcane brown rust (Puccinia melanocephala) in selfing F1 populations of wild sugarcane germplasm Erianthus rockii ‘Yundian 95-19’ and E. rockii ‘Yundian 95-20’. We tested parent and selfing F1 individuals for the brown rust resistance gene, Bru1, that has been shown to confer resistance to brown rust in sugarcane. The Bru1 gene was not detected in E. rockii ‘Yundian 95-19’, E. rockii ‘Yundian 95-20’ or their selfing F1 individuals, and we found there was segregation of resistance in the two selfing F1 populations (segregation ratio: 3:1). The results confirmed resistance in E. rockii ‘Yundian 95-19’ and E. rockii ‘Yundian 95-20’ to sugarcane brown rust is controlled by a novel, single dominant gene.

Sign in / Sign up

Export Citation Format

Share Document