Multiomic investigation of Sugarcane mosaic virus resistance in sugarcane  

作　　者：Ricardo JoséGonzaga Pimenta Alexandre Hild Aono Roberto Carlos Villavicencio Burbano Marcel Fernando da Silva Ivan Antônio dos Anjos Marcos Guimarães de Andrade Landell Marcos Cesar Gonçalves Luciana Rossini Pinto Anete Pereira de Souza 

机构地区：[1]Centre for Molecular Biology and Genetic Engineering,University of Campinas,Campinas,Brazil [2]Gustavo Galindo Velasco Campus,Littoral Polytechnic Superior School,Guayaquil,Ecuador [3]Advanced Centre for Technological Research in Sugarcane Agribusiness,Agronomic Institute of Campinas,Ribeirão Preto,Brazil [4]Plant Protection Research Centre,Biological Institute,São Paulo,Brazil [5]Department of Plant Biology,Institute of Biology,University of Campinas,Campinas,Brazil

出　　处：《The Crop Journal》2023年第6期1805-1815,共11页作物学报（英文版）

基　　金：the São Paulo Research Foundation(FAPESP),the Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq),the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior(CAPES,Computational Biology Program),the Littoral Polytechnic Superior School(ESPOL)and the Secretaría Nacional de Ciencia y Tecnología(SENESYT).;Ricardo JoséGonzaga Pimenta received an MSc fellowship from CAPES(grant 88887.177386/2018-00)and MSc and Ph.D.fellowships from FAPESP(grants 2018/18588-8 and 2019/21682-9);Alexandre Hild Aono received a Ph.D.fellowship from FAPESP(grant 2019/03232-6);Roberto Carlos Burbano Villavicencio received a Ph.D.fellowship from PAEDEx-AUIP.Anete Pereira de Souza received a research fellowship from CNPq(grant 312777/2018-3).

摘　　要：Sugarcane mosaic virus(SCMV)is the main etiological agent of sugarcane mosaic disease,which affects sugarcane and other grass crops.Despite the extensive characterization of quantitative trait loci controlling resistance to SCMV in maize,the genetic basis of this trait in sugarcane is largely unexplored.Here,a genome-wide association study was performed and machine learning coupled with feature selection was used for genomic prediction of resistance to SCMV in a diverse sugarcane panel.Nine single-nucleotide polymorphisms(SNPs)explained up to 29.9%of the observed phenotypic variance and a 73-SNP set predicted resistance with high accuracy,precision,recall,and F1 scores(the harmonic mean of precision and recall).Both marker sets were validated in additional sugarcane genotypes,in which the SNPs explained up to 23.6%of the phenotypic variation and predicted resistance with a maximum accuracy of 69.1%.Synteny analyses suggested that the gene responsible for the majority of SCMV resistance in maize is absent in sugarcane,explaining why this major resistance source has not been identified in this crop.Finally,using sugarcane RNA-Seq data,markers associated with resistance to SCMV were annotated,and a gene coexpression network was constructed to identify the predicted biological processes involved in resistance.This network allowed the identification of candidate resistance genes and confirmed the involvement of stress responses,photosynthesis,and the regulation of transcription and translation in resistance to SCMV.These results provide a practical marker-assisted breeding approach for sugarcane and identify target genes for future studies of SCMV resistance.

关 键 词：SACCHARUM SCMV GWAS Machine learning RNA-SEQ 

分 类 号：S566.1[农业科学—作物学]