Association mapping in sugarcane targeting tolerance to yellow leaf virus

• Grant number: 18/18588-8
• Support type: Scholarships in Brazil - Master
• Effective date (Start): January 01, 2019
• Effective date (End): February 29, 2020
• Field of knowledge: Biological Sciences - Genetics - Plant Genetics
• Principal researcher: Anete Pereira de Souza
• Grantee: Ricardo José Gonzaga Pimenta
• Home Institution: Centro de Biologia Molecular e Engenharia Genética (CBMEG). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The cultivation of sugarcane (Saccharum spp.) has undisputable importance in the Brazilian economy, accounting for more than 11 billion dollars in exports in 2016. The breeding of this crop is marked by its genetic complexity - in addition to being highly polyploid, its genome often presents aneuploidies; this hinders research in the area and makes the development of new cultivars a long and laborious process. One of the main diseases affecting this crop is sugarcane yellow leaf (SCYL), caused by the homonymous virus (SCYLV). The most common symptoms resulting from infection are the yellowing of leaf midribs and chlorosis, but asymptomatic cases are frequent. In the 1990s, yellow leaf was responsible for losses of 50% in Brazilian sugarcane production, and its causal agent is today endemic in the main sugar-ethanol producing regions of Brazil and of the world. Consequently, resistance to SCYLV is of great relevance in the genetic improvement of sugarcane; yet, few genetic studies have investigated this matter in detail, especially in Brazil. Therefore, this project proposes to perform a genome-wide association study in a panel of sugarcane from the Agronomic Institute of Campinas, targeting the resistance to SCYLV infection. Individuals will be genotyped using the recent genotyping-by-sequencing (GBS) method, that will allow the estimation of the allelic dose of a large number of markers. For phenotyping, viral titre will be determined by reverse transcription assays followed by quantitative polymerase chain reaction (RT-qPCR), which will estimate levels of infection with high accuracy. With the use of diverse approaches in the association analyses, we intend to identify markers and genes associated to resistance to SCYLV, which will contribute to the general understanding of this process and might be ultimately applied in the breeding of sugarcane. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GONZAGA PIMENTA, RICARDO JOSE; AONO, ALEXANDRE HILD; VILLAVICENCIO BURBANO, ROBERTO CARLOS; COUTINHO, ALISSON ESDRAS; DA SILVA, CARLA CRISTINA; ANTONIO DOS ANJOS, IVAN; PERECIN, DILERMANDO; DE ANDRADE LANDELL, MARCOS GUIMARAES; GONCALVES, MARCOS CESAR; PINTO, LUCIANA ROSSINI; DE SOUZA, ANETE PEREIRA. Genome-wide approaches for the identification of markers and genes associated with sugarcane yellow leaf virus resistance. SCIENTIFIC REPORTS, v. 11, n. 1 AUG 3 2021. Web of Science Citations: 1.

AONO, ALEXANDRE HILD; GONZAGA PIMENTA, RICARDO JOSE; BASSO GARCIA, ANA LETYCIA; CORRER, FERNANDO HENRIQUE; HOSAKA, GUILHERME KENICHI; CARRASCO, MARISHANI MARIN; CARDOSO-SILVA, CLAUDIO BENICIO; MANCINI, MELINA CRISTINA; SFORCA, DANILO AUGUSTO; DOS SANTOS, LUCAS BORGES; NAGAI, JAMES SHINITI; PINTO, LUCIANA ROSSINI; DE ANDRADE LANDELL, MARCOS GUIMARAES; CARNEIRO, MONALISA SAMPAIO; BALSALOBRE, THIAGO WILLIAN; QUILES, MARCOS GONCALVES; PEREIRA, WELISON ANDRADE; ALVES MARGARIDO, GABRIEL RODRIGUES; DE SOUZA, ANETE PEREIRA. The Wild Sugarcane and Sorghum Kinomes: Insights Into Expansion, Diversification, and Expression Patterns. FRONTIERS IN PLANT SCIENCE, v. 12, JUL 7 2021. Web of Science Citations: 0.

AONO, ALEXANDRE HILD; COSTA, ESTELA ARAUJO; SILVA RODY, HUGO VIANNA; NAGAI, JAMES SHINITI; GONZAGA PIMENTA, RICARDO JOSE; MANCINI, MELINA CRISTINA; CAMILO DOS SANTOS, FERNANDA RAQUEL; PINTO, LUCIANA ROSSINI; DE ANDRADE LANDELL, MARCOS GUIMARAES; DE SOUZA, ANETE PEREIRA; KUROSHU, REGINALDO MASSANOBU. Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance. SCIENTIFIC REPORTS, v. 10, n. 1 NOV 18 2020. Web of Science Citations: 1.