Multiomic investigation of Sugarcane mosaic virus resistance in sugarcane

被引:3
|
作者
Pimenta, Ricardo Jose Gonzaga [1 ]
Aono, Alexandre Hild [1 ]
Burbano, Roberto Carlos Villavicencio [2 ]
Silva, Marcel Fernando da [3 ]
Anjos, Ivan Antonio dos [3 ]
Landell, Marcos Guimaraes de Andrade [3 ]
Goncalves, Marcos Cesar [4 ]
Pinto, Luciana Rossini [3 ]
Souza, Anete Pereira de [1 ,5 ]
机构
[1] Univ Estadual Campinas, Ctr Mol Biol & Genet Engn, Campinas, Brazil
[2] Littoral Polytech Super Sch, Gustavo Galindo Velasco Campus, Guayaquil, Ecuador
[3] Agron Inst Campinas, Adv Ctr Technol Res Sugarcane Agribusiness, Ribeirao Preto, Brazil
[4] Biol Inst, Plant Protect Res Ctr, Sao Paulo, Brazil
[5] Univ Estadual Campinas, Inst Biol, Dept Plant Biol, Campinas, Brazil
来源
CROP JOURNAL | 2023年 / 11卷 / 06期
基金
巴西圣保罗研究基金会;
关键词
Saccharum; SCMV; GWAS; Machine learning; RNA-Seq; VARIANT CALL FORMAT; GENE-EXPRESSION; IDENTIFICATION; PROTEIN; ASSOCIATION; INFECTION; INTERACTS; GENOTYPES; SACCHARUM; ALIGNMENT;
D O I
10.1016/j.cj.2023.06.009
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
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 control-ling 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 pre-dicted 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.(c) 2023 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:1805 / 1815
页数:11
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