Multiomic investigation of sugarcane mosaic virus resistance in sugarcane

Abstract

Sugarcane mosaic virus (SCMV) is the main etiological agent of sugarcane mosaic disease, which affects sugarcane, maize and other economically important grass species. Despite the extensive characterization of quantitative trait loci controlling resistance to SCMV in maize, the genetic basis of this trait is largely unexplored in sugarcane. Here, a genome-wide association study was performed and machine learning coupled to feature selection was used for the genomic prediction of resistance to SCMV in a diverse panel of sugarcane accessions. This ultimately led to the identification of nine single nucleotide polymorphisms (SNPs) explaining up to 29.9% of the phenotypic variance and a 73-SNP set that predicted resistance with high accuracy, precision, recall, and F1 scores. 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 showed that the gene responsible for the major SCMV resistance in maize is probably absent in sugarcane, explaining why such a major resistance source is thus far unknown in this crop. Lastly, using sugarcane RNA sequencing data, markers associated with the resistance to SCMV in sugarcane were annotated and a gene coexpression network was constructed to identify the predicted biological processes involved in SCMV resistance. This allowed the identification of candidate resistance genes and confirmed the involvement of stress responses, photosynthesis and regulation of transcription and translation in the resistance to this virus. These results provide a viable marker-assisted breeding approach for sugarcane and identify target genes for future molecular studies on resistance to SCMV.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inUrochloa ruziziensis

“…As an initial step to achieve this objective, we aimed to establish potential marker-phenotype associations. To this end, the strong performance improvement observed using the FS/FI approach indicates that the selected sets of markers are likely to be near QTLs, and can therefore be used to define genomic regions involved in phenotypic variation (Steinfath et al, 2010; Heer et al, 2018; Zhou et al, 2019; Aono et al, 2020; Pimenta et al, 2021; Aono et al, 2022; Pimenta et al, 2022). In contrast to other approaches aimed at identifying genotype-phenotype associations, FS techniques do not rely on specific biparental populations (RILs, NILs, F2, etc.…”

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

“…As an initial step to achieve this objective, we aimed to establish potential marker-phenotype associations. To this end, the strong performance improvement observed using the FS/FI approach indicates that the selected sets of markers are likely to be near QTLs, and can therefore be used to define genomic regions involved in phenotypic variation ( Steinfath et al., 2010 ; Heer et al., 2018 ; Zhou et al., 2019 ; Aono et al., 2020 ; Pimenta et al., 2021 ; Aono et al., 2022 ; Pimenta et al., 2022 ). Additionally, by utilizing allele proportions for genotyping the family, this approach can be extended to other crop species that employ the family as the unit for conducting GS.…”

Genome-wide association reveals novel insights into the molecular mechanisms regulating stem volume inPinus taeda

“…Multiomics analyses have emerged as powerful tools for revealing the intricate genetic architecture underlying complex traits and mapping genetic variants with unknown biological roles to molecular mechanisms through integrative methodologies such as gene coexpression networks [26,86,90].…”