Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

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.

Isolation and sequencing of a single copy of an introgressed chromosome from a complex genome for gene and SNP identification.

KEY MESSAGE: This manuscript describes the identification, isolation and sequencing of a single chromosome containing high value resistance genes from a complex polyploid where sequencing the whole genome is too costly. The large complex genomes of many crops constrain the use of new technologies for genome-assisted selection and genetic improvement. One method to simplify a genome is to break it into individual chromosomes by flow cytometry; however, in many crop species most chromosomes cannot be isolated individually. Flow sorting of a single copy of a chromosome has been developed in wheat, and here we demonstrate its use to identify markers of interest in an Erianthus/Sacchurum hybrid. Erianthus/Saccharum hybrids are of interest because Erianthus is known to be highly resistant to soil borne diseases which cause extensive sugarcane yield losses in Australia. Sugarcane (Saccharum) cultivars are autopolyploids with a highly complex genome and over 100 chromosomes. Flow cytometry for sugarcane, as in most crops, does not resolve individual chromosomes to a karyotype peak for sorting. To isolate a single chromosome, we used genomic in situ hybridization (GISH) to identify the flow karyotype region containing the Erianthus chromosomes, flow sorted single chromosomes from this region, PCR screened for the Erianthus chromosomes and sequenced them. One Erianthus chromosome amplified and sequenced well, and from this data we could identify 57 resistant type genes and SNPs in nearly half of these genes. We developed KASP SNP assays and demonstrated that the identified SNP markers segregated as expected in a small introgression population. The pipeline we developed here to flow sort and sequence single chromosomes could be used in any crop with a large complex genome to rapidly discover and develop markers to important loci.

A linkage disequilibrium-based approach to position unmapped SNPs in crop species.

BACKGROUND: High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm's efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions. RESULTS: In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date. CONCLUSIONS: Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources.

Sugarcane Smut, Caused by Sporisorium scitamineum, a Major Disease of Sugarcane: A Contemporary Review.

Sugarcane smut caused by the fungus Sporisorium scitamineum is one of the major diseases of sugarcane worldwide, causing significant losses in productivity and profitability of this perennial crop. Teliospores of this fungus are airborne, can travel long distances, and remain viable in hot and dry conditions for >6 months. The disease is easily recognized by its long whiplike sorus produced on the apex or side shoots of sugarcane stalks. Each sorus can release ≤100 million teliospores in a day; the spores are small (≤7.5 µ) and light and can survive in harsh environmental conditions. The airborne teliospores are the primary mode of smut spread around the world and across cane-growing regions. The most effective method of managing this disease is via resistant varieties. Because of the complex genomic makeup of sugarcane, selection for resistant traits is difficult in sugarcane breeding programs. In recent times, the application of molecular markers as a rapid tool of discarding susceptible genotypes early in the selection program has been investigated. Large effect resistance loci have been identified and have the potential to be used for marker-assisted selection to increase the frequency of resistant breeding lines in breeding programs. Recent developments in omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have contributed to our understanding and provided insights into the mechanism of resistance and susceptibility. This knowledge will further our understanding of smut and its interactions with sugarcane genotypes and aid in the development of durable resistant varieties.

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects.

KEY MESSAGE: Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance. In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding.

KEY MESSAGE: Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

Accuracy of genomic prediction of complex traits in sugarcane.

KEY MESSAGE: Complex traits in sugarcane can be accurately predicted using genome-wide DNA markers. Genomic single-step prediction is an attractive method for genomic selection in commercial breeding programs. Sugarcane breeding programs have achieved up to 1% genetic gain in key traits such as tonnes of cane per hectare (TCH), commercial cane sugar (CCS) and Fibre content over the past decades. Here, we assess the potential of genomic selection to increase the rate of genetic gain for these traits by deriving genomic estimated breeding values (GEBVs) from a reference population of 3984 clones genotyped for 26 K SNP. We evaluated the three different genomic prediction approaches GBLUP, genomic single step (GenomicSS), and BayesR. GenomicSS combining pedigree and SNP information from historic and recent breeding programs achieved the most accurate predictions for most traits (0.3-0.44). This method is attractive for routine genetic evaluation because it requires relatively little modification to the existing evaluation and results in breeding value estimates for all individuals, not only those genotyped. Adding information from early-stage trials added up to 5% accuracy for CCS and Fibre, but 0% for TCH, reflecting the importance of competition effects for TCH. These GEBV accuracies are sufficiently high that, combined with the right breeding strategy, a doubling of the rate of genetic gain could be achieved. We also assessed the flowering traits days to flowering, gender and pollen viability and found high heritabilities of 0.57, 0.78 and 0.72, respectively. The GEBV accuracies indicated that genomic selection could be used to improve these traits. This could open new avenues for breeders to manage their breeding programs, for example, by synchronising flowering time and selecting males with high pollen viability.

Flow cytometric characterisation of the complex polyploid genome of Saccharum officinarum and modern sugarcane cultivars.

Sugarcane (Saccharum spp.) is a globally important crop for sugar and bioenergy production. Its highly polyploid, complex genome has hindered progress in understanding its molecular structure. Flow cytometric sorting and analysis has been used in other important crops with large genomes to dissect the genome into component chromosomes. Here we present for the first time a method to prepare suspensions of intact sugarcane chromosomes for flow cytometric analysis and sorting. Flow karyotypes were generated for two S. officinarum and three hybrid cultivars. Five main peaks were identified and each genotype had a distinct flow karyotype profile. The flow karyotypes of S. officinarum were sharper and with more discrete peaks than the hybrids, this difference is probably due to the double genome structure of the hybrids. Simple Sequence Repeat (SSR) markers were used to determine that at least one allelic copy of each of the 10 basic chromosomes could be found in each peak for every genotype, except R570, suggesting that the peaks may represent ancestral Saccharum sub genomes. The ability to flow sort Saccharum chromosomes will allow us to isolate and analyse chromosomes of interest and further examine the structure and evolution of the sugarcane genome.

Analysis of the resistance mechanisms in sugarcane during Sporisorium scitamineum infection using RNA-seq and microscopy.

Smut caused by biotrophic fungus Sporisorium scitamineum is a major disease of cultivated sugarcane that can cause considerable yield losses. It has been suggested in literature that there are at least two types of resistance mechanisms in sugarcane plants: an external resistance, due to chemical or physical barriers in the sugarcane bud, and an internal resistance governed by the interaction of plant and fungus within the plant tissue. Detailed molecular studies interrogating these two different resistance mechanisms in sugarcane are scarce. Here, we use light microscopy and global expression profiling with RNA-seq to investigate these mechanisms in sugarcane cultivar CP74-2005, a cultivar that possibly possesses both internal and external defence mechanisms. A total of 861 differentially expressed genes (DEGs) were identified in a comparison between infected and non-infected buds at 48 hours post-inoculation (hpi), with 457 (53%) genes successfully annotated using BLAST2GO software. This includes genes involved in the phenylpropanoid pathway, cell wall biosynthesis, plant hormone signal transduction and disease resistance genes. Finally, the expression of 13 DEGs with putative roles in S. scitamineum resistance were confirmed by quantitative real-time reverse transcription PCR (qRT-PCR) analysis, and the results were consistent with the RNA-seq data. These results highlight that the early sugarcane response to S. scitamineum infection is complex and many of the disease response genes are attenuated in sugarcane cultivar CP74-2005, while others, like genes involved in the phenylpropanoid pathway, are induced. This may point to the role of the different disease resistance mechanisms that operate in cultivars such as CP74-2005, whereby the early response is dominated by external mechanisms and then as the infection progresses, the internal mechanisms are switched on. Identification of genes underlying resistance in sugarcane will increase our knowledge of the sugarcane-S. scitamineum interaction and facilitate the introgression of new resistance genes into commercial sugarcane cultivars.