Genome wide selection in sugarcane using machine learning and complex networks for economically important traits

• Grant number: 19/03232-6
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Effective date (Start): August 01, 2019
• Effective date (End): February 29, 2024
• Field of knowledge: Biological Sciences - Genetics - Plant Genetics
• Principal Investigator: Anete Pereira de Souza
• Grantee: Alexandre Hild Aono
• Host Institution: Centro de Biologia Molecular e Engenharia Genética (CBMEG). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated scholarship(s): 19/26858-8 - Simulation of plant complex trait architectures using deep neural networks, BE.EP.DD

Abstract

The use of molecular markers SNPs (Single Nucleotide Polymorphisms), although made a major contribution to genetic improvement programs, has limited application in the case of sugarcane, a species with high genomic complexity. Due to the lack of biocomputational tools compatible with the uniqueness of the species, specific methodologies have to be used. With immeasurable importance to the sectors of biofuels and sugar manufacturing, the improvement of the species and the cultivars with characters of economic importance represent a major contribution to the world economy. Due to the polygenic nature of quantitative phenotypic characteristics and the consequent complexity of application of methods for genotype-phenotype association, genomic selection methodologies have been shown to be an alternative to traditional approaches for marker-assisted selection. The present work proposes an innovative approach for realization of selection in cane. Using an array of putative SNPs identified with the use of sequencing genotyping data and software adapted to polyploidy and aneuploidy of the species, an association modeling will be used between these markers using complex networks. By means of detection methods of communities in the built network, it is intended to carry out data imputation, dimensionality and identification of SNPs with related biological categories. For constructionof the selection model will be used machine learning techniques as models nonlinear, constructed to capture the specificity of the cane genome. (AU)

Scientific publications

(6)

(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)

FERREIRA FILHO, JAIRE A.; ROSOLEN, RAFAELA R.; ALMEIDA, DEBORAH A.; DE AZEVEDO, PAULO HENRIQUE C.; MOTTA, MARIA LORENZA L.; AONO, ALEXANDRE H.; DOS SANTOS, CLELTON A.; HORTA, MARIA AUGUSTA C.; DE SOUZA, ANETE P.. Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi. 3 BIOTECH, v. 11, n. 11, NOV 2021. (15/09202-0, 19/03232-6, 20/13420-1, 18/19660-4, 20/10536-9)

MARTINS, FELIPE BITENCOURT; MORAES, ALINE COSTA LIMA; AONO, ALEXANDRE HILD; FERREIRA, REBECCA CAROLINE ULBRICHT; CHIARI, LUCIMARA; SIMEAO, ROSANGELA MARIA; BARRIOS, SANZIO CARVALHO LIMA; SANTOS, MATEUS FIGUEIREDO; JANK, LIANA; DO VALLE, CACILDA BORGES; et al. Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasse. FRONTIERS IN PLANT SCIENCE, v. 12, OCT 22 2021. (18/19219-6, 19/03232-6)

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; et al. 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. (19/03232-6, 15/24346-9, 18/18588-8, 19/21682-9)

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; et al. Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance. SCIENTIFIC REPORTS, v. 10, n. 1, NOV 18 2020. (18/18588-8, 05/55258-6, 14/11482-9, 08/52197-4, 19/03232-6)

FRANCISCO, FELIPE ROBERTO; AONO, ALEXANDRE HILD; DA SILVA, CARLA CRISTINA; GONCALVES, PAULO S.; SCALOPPI JUNIOR, ERIVALDO J. J.; LE GUEN, VINCENT; FRITSCHE-NETO, ROBERTO; SOUZA, LIVIA MOURA; SOUZA, ANETE PEREIRA DE. Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches. FRONTIERS IN PLANT SCIENCE, v. 12, DEC 21 2021. (18/18985-7, 19/03232-6)

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; et al. The Wild Sugarcane and Sorghum Kinomes: Insights Into Expansion, Diversification, and Expression Patterns. FRONTIERS IN PLANT SCIENCE, v. 12, JUL 7 2021. (14/11482-9, 18/18588-8, 19/03232-6, 15/22993-7, 15/16399-5, 08/52197-4, 19/21682-9, 05/55258-6, 19/19340-2)