Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Aono AH

PROVIDER: S-EPMC7676261 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Aono Alexandre Hild AH Costa Estela Araujo EA Rody Hugo Vianna Silva HVS Nagai James Shiniti JS Pimenta Ricardo José Gonzaga RJG Mancini Melina Cristina MC Dos Santos Fernanda Raquel Camilo FRC Pinto Luciana Rossini LR Landell Marcos Guimarães de Andrade MGA de Souza Anete Pereira AP Kuroshu Reginaldo Massanobu RM

Scientific reports 20201118 1

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, ...[more]

PMID: 33208862

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Dataset Information

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

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

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