Genome-wide approaches for the identification of markers and genes associated with sugarcane yellow leaf virus resistance

Abstract

Sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV) is a major disease affecting sugarcane, a leading sugar and energy crop. Despite damages caused by SCYLV, the genetic base of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic base of SCYLV resistance using dominant and codominant markers and genotypes of interest for sugarcane breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them and demonstrated their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.

Multiomic investigation of sugarcane mosaic virus resistance in sugarcane

“…In addition to performing a GWAS, ML algorithms coupled with FS were employed to predict genotype resistance or susceptibility to SCMV. Similar to previous works in which this genomic prediction methodology was applied to sugarcane to evaluate resistance to brown rust (Aono et al, 2020) and sugarcane yellow leaf virus (Pimenta et al, 2021), very promising results for several metrics were achieved. These results are considerably superior to those obtained by Barnes et al (1997), who predicted sugarcane resistance to SCMV with an accuracy of 76% based on random amplified polymorphic DNA markers.…”

“…Despite recent advances in sequencing technologies, genomic studies in sugarcane remain considerably hindered by the complexity of sugarcane's genome (Thirugnanasambandam et al, 2018). Studies focusing on resistance to viruses are particularly limited due to sugarcane plants' large size and vegetative propagation, which limit the size of controlled experiments and the number of genotypes that can be evaluated (da Silva et al, 2015;Pimenta et al, 2021).…”

“…The plant material employed in the present study has been described elsewhere (Pimenta et al, 2021). The experimental population consisted of a panel of 97 sugarcane genotypes comprising wild accessions of S. officinarum, S. spontaneum and Saccharum robustum; traditional sugarcane and energy cane clones; and commercial cultivars from Brazilian breeding programs.…”

“…Following a genomic prediction approach previously employed for sugarcane (Aono et al, 2020;Pimenta et al, 2021), ML algorithms coupled with FS were used to predict the attribution of genotypes to two groups: those that presented mosaic symptoms at any block or year (susceptible) and those that did not present symptoms in any case (resistant). Eight ML algorithms implemented in the scikit-learn Python 3 module (Pedregosa et al, 2011) were tested: adaptive boosting (AB) (Freund and Schapire, 1997), decision tree (DT) (Quinlan, 1986), Gaussian naive Bayes (GNB) (Friedman et al, 1997), Gaussian process (GP) (Rasmussen, 2003), K-nearest neighbor (KNN) (Cover and Hart, 1967), multilayer perceptron (MLP) neural network (Popescu et al, 2009), random forest (RF) (Breiman, 2001) and support vector machine (SVM) (Cristianini and Shawe-Taylor, 2000).…”

“…These hybrids have large (D'Hont et al, 1998), highly polyploid (D'Hont andGlaszmann, 2001), aneuploid (Sforça et al, 2019) and duplicated (Aono et al, 2021) genomes that hinder sugarcane breeding research. Additionally, studies suggest that the majority of sugarcane traits are controlled by many small-effect loci (Gouy et al, 2015;Fickett et al, 2019;Pimenta et al, 2021). However, given the existence of Scmv1 and Scmv2 in maize, it is odd that no major loci controlling SCMV resistance in sugarcane have been identified.…”