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

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

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Genome-Wide Association Study Unravels Quantitative Trait Loci and Genes Associated with Yield-Related Traits in Sugarcane

Sugarcane, a major sugar and energy crop worldwide faces an increasing demand for higher yields. Identifying yield-related markers and candidate genes is valuable for breeding high-yield varieties using molecular techniques. In this work, seven yield-related traits were evaluated in a diversity panel of 159 genotypes, derived from Tripidium arundinaceum, Saccharum spontaneum, and modern sugarcane genotypes. All traits exhibited significant genetic variance with high heritability and high correlations. Genetic diversity analysis reveals a genomic decay of 23 kb and an average single nucleotide polymorphism (SNP) number of 25,429 per genotype. These 159 genotypes were divided into 4 subgroups. Genome-wide association analysis identified 47 SNPs associated with brix, spanning 36 quantitative trait loci (QTLs), and 138 SNPs for other traits across 104 QTLs, covering all 32 chromosomes. Interestingly, 12 stable QTLs associated with yield-related traits were identified, which contained 35 candidate genes. This work provides markers and candidate genes for marker-assisted breeding to improve sugarcane yields.

Yellow Leaf Disease Resistance and Melanaphis sacchari Preference in Commercial Sugarcane Cultivars

Sugarcane yellow leaf disease (YLD) caused by sugarcane yellow leaf virus (ScYLV) is a major threat for the sugarcane industry worldwide, and the aphid Melanaphis sacchari is its main vector. Breeding programs in Brazil have provided cultivars with intermediate resistance to ScYLV, whereas the incidence of ScYLV has been underestimated partly due to the complexity of YLD symptom expression and identification. Here, we evaluated YLD symptoms in a field assay using eight sugarcane genotypes comprising six well-established commercial high-sucrose cultivars, one biomass yield cultivar, and a susceptible reference under greenhouse conditions, along with estimation of virus titer through RT-qPCR from leaf samples. Additionally, a free-choice bioassay was used to determine the number of aphids feeding on the SCYLV-infected cultivars. Most of the cultivars showed some degree of resistance to YLD, while also revealing positive RT-qPCR results for ScYLV and virus titers with non-significant correlation with YLD severity. The cultivars IACSP01-5503 and IACBIO-266 were similar in terms of aphid preference and ScYLV resistance traits, whereas the least preferred cultivar by M. sacchari, IACSP96-7569, showed intermediate symptoms but similar virus titer to the susceptible reference, SP71-6163. We conclude that current genetic resistance incorporated into sugarcane commercial cultivars does not effectively prevent the spread of ScYLV by its main aphid vector.

A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane

The sugarcane ratooning ability (RA) is the most important target trait for breeders seeking to enhance the profitability of sugarcane production by reducing the planting cost. Understanding the genetics governing the RA could help breeders by identifying molecular markers that could be used for genomics-assisted breeding (GAB). A replicated field trial was conducted for three crop cycles (plant cane, first ratoon, and second ratoon) using 432 sugarcane clones and used for conducting genome-wide association and genomic prediction of five sugar and yield component traits of the RA. The RA traits for economic index (EI), stalk population (SP), stalk weight (SW), tonns of cane per hectare (TCH), and tonns of sucrose per hectare (TSH) were estimated from the yield and sugar data. A total of six putative quantitative trait loci and eight nonredundant single-nucleotide polymorphism (SNP) markers were associated with all five tested RA traits and appear to be unique. Seven putative candidate genes were colocated with significant SNPs associated with the five RA traits. The genomic prediction accuracies for those tested traits were moderate and ranged from 0.21 to 0.36. However, the models fitting fixed effects for the most significant associated markers for each respective trait did not give any advantages over the standard models without fixed effects. As a result of this study, more robust markers could be used in the future for clone selection in sugarcane, potentially helping resolve the genetic control of the RA in sugarcane.

Prospecting true ScYLV resistance in Saccharum hybrid parental population in India by symptom phenotyping and viral titre quantification

In sugarcane (Saccharum spp. hybrids) cultivation, viral diseases pose a great challenge across the globe. Yellow leaf (YL) disease is one of the important viral diseases caused by Sugarcane yellow leaf virus (ScYLV), a positive-sense ssRNA virus, genus Polerovirus, family Solemoviridae. The disease symptoms appear in later stages of crop growth during grand growth to maturity phase with intense midrib yellowing in the abaxial leaf surface. At present, this disease is managed through tissue (meristem) culture and healthy seed nurseries in India. However, the virus-free plants are infected quickly by secondary inoculum from aphid vectors in the field, which necessitates the importance of developing YL-resistant varieties. We screened about 600-625 sugarcane parental clones to identify true YL resistance based on 0-5 disease rating scale since 2015 and categorised them as resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible. Leaf samples were collected from all these categories of plants during 2018-20 for the viral titre estimation through absolute quantification method (qRT-PCR assay). The viral load was invariably high in all categories of susceptible samples that ranged from 4.40 × 102 to 8.429 × 106, whereas in YL-free asymptomatic clones, the viral load ranged from 82.35 ± 5.90 to 5.121 × 104. The results clearly indicated that highest viral titre of 105-107 copies was present in all the susceptible clones irrespective of their disease severity grades. Our results clearly established that about 22.85% of apparently resistant sugarcane clones remained free from YL symptoms with significantly low ScYLV titre although we could not find a significant correlation between virus titre and symptom expression. The identified resistant parents will serve as sources of YL resistance to develop virus resistant sugarcane varieties.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03541-y.

Combining genomic selection with genome-wide association analysis identified a large-effect QTL and improved selection for red rot resistance in sugarcane

Red rot caused by the fungus Colletotrichum falcatum is the main disease limiting sugarcane productivity in several countries including the major producer India. The genetic basis for red rot resistance is unclear. We studied a panel of 305 sugarcane clones from the Australian breeding program for disease response phenotype and genotype using an Affymetrix® Axiom® array, to better understand the genetic basis of red rot resistance. SNP markers highly significantly associated with red rot response (≤ 10-8) were identified. Markers with largest effect were located in a single 14.6 Mb genomic region of sorghum (the closest diploid relative of sugarcane with a sequenced genome) suggesting the presence of a major-effect QTL. By genomic selection, the estimated selection accuracy was ~0.42 for red rot resistance. This was increased to ~0.5 with the addition of 29 highly significant SNPs as fixed effects. Analysis of genes nearby the markers linked to the QTL revealed many biotic stress responsive genes within this QTL, with the most significant SNP co-locating with a cluster of four chitinase A genes. The SNP markers identified here could be used to predict red rot resistance with high accuracy at any stage in the sugarcane breeding program.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.

Identification of potential MTAs and candidate genes for juice quality- and yield-related traits in Saccharum clones: a genome-wide association and comparative genomic study

Sugarcane is an economically important commercial crop which provides raw material for the production of sugar, jaggery, bioethanol, biomass and other by-products. Sugarcane breeding till today heavily relies on conventional breeding approaches which is time consuming, laborious and costly. Integration of marker-assisted selection (MAS) in sugarcane genetic improvement programs for difficult to select traits like sucrose content, resistance to pests and diseases and tolerance to abiotic stresses will accelerate varietal development. In the present study, association mapping approach was used to identify QTLs and genes associated with sucrose and other important yield-contributing traits. A mapping panel of 110 diverse sugarcane genotypes and 148 microsatellite primers were used for structured association mapping study. An optimal subpopulation number (ΔK) of 5 was identified by structure analysis. GWAS analysis using TASSEL identified a total of 110 MTAs which were localized into 27 QTLs by GLM and MLM (Q + K, PC + K) approaches. Among the 24 QTLs sequenced, 12 were able to identify potential candidate genes, viz., starch branching enzyme, starch synthase 4, sugar transporters and G3P-DH related to carbohydrate metabolism and hormone pathway-related genes ethylene insensitive 3-like 1, reversion to ethylene sensitive1-like, and auxin response factor associated to juice quality- and yield-related traits. Six markers, NKS 5_185, SCB 270_144, SCB 370_256, NKS 46_176 and UGSM 648_245, associated with juice quality traits and marker SMC31CUQ_304 associated with NMC were validated and identified as significantly associated to the traits by one-way ANOVA analysis. In conclusion, 24 potential QTLs identified in the present study could be used in sugarcane breeding programs after further validation in larger population. The candidate genes from carbohydrate and hormone response pathway presented in this study could be manipulated with genome editing approaches to further improve sugarcane crop.