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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
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Aono, Alexandre Hild [1] ; Costa, Estela Araujo [2] ; Silva Rody, Hugo Vianna [2] ; Nagai, James Shiniti [2] ; Gonzaga Pimenta, Ricardo Jose [1] ; Mancini, Melina Cristina [1] ; Camilo dos Santos, Fernanda Raquel [3] ; Pinto, Luciana Rossini [3] ; de Andrade Landell, Marcos Guimaraes [3] ; de Souza, Anete Pereira [1, 4] ; Kuroshu, Reginaldo Massanobu [2]
Total Authors: 11
Affiliation:
[1] Univ Estadual Campinas, Mol Biol & Genet Engn Ctr CBMEG, UNICAMP, Campinas, SP - Brazil
[2] Univ Fed Sao Paulo UNIFESP, Inst Ciencia & Tecnol ICT, Sao Jose Dos Campos, SP - Brazil
[3] Agron Inst Campinas IAC, Adv Ctr Sugarcane Agrobusiness Technol Res, Ribeirao Preto, SP - Brazil
[4] Univ Estadual Campinas, Inst Biol IB, Dept Plant Biol, UNICAMP, Campinas, SP - Brazil
Total Affiliations: 4
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 10, n. 1 NOV 18 2020.
Web of Science Citations: 1
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. (AU)

FAPESP's process: 18/18588-8 - Association mapping in sugarcane targeting tolerance to yellow leaf virus
Grantee:Ricardo José Gonzaga Pimenta
Support type: Scholarships in Brazil - Master
FAPESP's process: 19/03232-6 - Genome wide selection in sugarcane using machine learning and complex networks for economically important traits
Grantee:Alexandre Hild Aono
Support type: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 05/55258-6 - Genetic mapping and identification of genetic and functional molecular markers associated with agronomic characteristics of interest in sugarcane
Grantee:Luciana Rossini Pinto Machado da Silva
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 08/52197-4 - Genomic-assisted breeding of sugarcane: using molecular markers for understanding the genetic architecture of quantitative traits and to implement marker assisted selection
Grantee:Anete Pereira de Souza
Support type: Program for Research on Bioenergy (BIOEN) - Thematic Grants
FAPESP's process: 14/11482-9 - Evaluation of genomic synteny between sorghum (Sorghum bicolor) and sugarcane (Saccharum officinarum): comparision of genes involved in a QTL for Brix in sorghum to syntenic region in sugarcane
Grantee:Melina Cristina Mancini
Support type: Scholarships in Brazil - Post-Doctorate