Comparative transcriptome profiling of the response to Pyrenochaeta lycopersici in resistant tomato cultivar Mogeor and its background genotype-susceptible Moneymaker.

Breeding for resistance is the most effective tool for controlling the corky root disease of tomato caused by Pyrenochaeta lycopersici. A comparative RNA-Seq-based transcriptomic analysis was conducted at 96 hpi (hours post infection) on two tomato cultivars: resistant Mogeor and its genetic background, and susceptible Moneymaker to investigate the differences in their transcriptomic response and identify the molecular bases of this plant-pathogen interaction. The number of differentially expressed genes (DEGs) identified was much higher in the susceptible than in the resistant genotype; however, the proportion of upregulated genes was higher in Mogeor (70.81%) than in Moneymaker (52.95%). Gene Ontology (GO) analysis enabled identification of 24 terms shared by the two cultivars that were consistent with responses to external stimulus, such as fungal infection. On the other hand, as many as 54 GO were enriched solely in Moneymaker, including terms related to defense response and cell wall metabolism. Our results could support the previous observations in other pathosystems, that susceptibility and resistance have overlapping signaling pathways and responses, suggesting that the P. lycopersici resistance gene pyl might be a recessive allele at a susceptibility locus, for which different candidate genes were identified based on the differences in induction or expression levels, observed between the resistant and susceptible genotype. MapMan analysis highlighted a complex hormone and transcription factors interplay where SA- and JA-induced pathways are modulated in a similar way in both genotypes and thus take part in a common response while the ethylene signaling pathways, induced mainly in susceptible Moneymaker, seem putatively contribute to its susceptibility.

Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel.

KEY MESSAGE: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5-F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r 2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41-0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.