Assessing sugarcane brown rust resistance using Image analysis

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.

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

Sugarcane 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.

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

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.

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

We 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.