Search

Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35–0.74) and were reduced across environments (0.28–0.58). Prediction accuracy within the Spring set ranged from 0.30–0.69, and from 0.19–0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.

Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass (Lolium perenne L.) improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD) in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot). Genomic estimated breeding values (GEBVs) for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively). Higher accuracy of GEBVs was obtained for flowering time (up to 0.7), partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy.

Ascochyta Blight (AB) is a major disease of many cool-season legumes globally. In field pea, three fungal pathogens have been identified to be responsible for this disease in Australia, namely Peyronellaea pinodes, Peyronellaea pinodella and Phoma koolunga. Limited genomic resources for these pathogens have been generated, which has hampered the implementation of effective management strategies and breeding for resistant cultivars. Using Oxford Nanopore long-read sequencing, we report the first high-quality, fully annotated, near-chromosome-level nuclear and mitochondrial genome assemblies for 18 isolates from the Australian AB complex. Comparative genome analysis was performed to elucidate the differences and similarities between species and isolates using phylogenetic relationships and functional diversity. Our data indicated that P. pinodella and P. koolunga are heterothallic, while P. pinodes is homothallic. More homology and orthologous gene clusters are shared between P. pinodes and P. pinodella compared to P. koolunga. The analysis of the repetitive DNA content showed differences in the transposable repeat composition in the genomes and their expression in the transcriptomes. Significant repeat expansion in P. koolunga’s genome was seen, with strong repeat-induced point mutation (RIP) activity being evident. Phylogenetic analysis revealed that genetic diversity can be exploited for species marker development. This study provided the much-needed genetic resources and characterization of the AB species to further drive research in key areas such as disease epidemiology and host–pathogen interactions.

Potato (Solanum tuberosum L.) breeders consider a large number of traits during cultivar development and progress in conventional breeding can be slow. There is accumulating evidence that some of these traits, such as yield, are affected by a large number of genes with small individual effects. Recently, significant efforts have been applied to the development of genomic resources to improve potato breeding, culminating in a draft genome sequence and the identification of a large number of single nucleotide polymorphisms (SNPs). The availability of these genome-wide SNPs is a prerequisite for implementing genomic selection for improvement of polygenic traits such as yield. In this review, we investigate opportunities for the application of genomic selection to potato, including novel breeding program designs. We have considered a number of factors that will influence this process, including the autotetraploid and heterozygous genetic nature of potato, the rate of decay of linkage disequilibrium, the number of required markers, the design of a reference population, and trait heritability. Based on estimates of the effective population size derived from a potato breeding program, we have calculated the expected accuracy of genomic selection for four key traits of varying heritability and propose that it will be reasonably accurate. We compared the expected genetic gain from genomic selection with the expected gain from phenotypic and pedigree selection, and found that genetic gain can be substantially improved by using genomic selection.

Publication pdf can be found here: http://www.aaabg.org/aaabghome/fullproc24.php