Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.).

BACKGROUND: Genomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively. RESULTS: Large phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594-0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780-0.835), fruit width (0.791-0.865), and pericarp thickness (0.643-0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768-12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets. CONCLUSION: Our study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.

Low-density SNP markers with high prediction accuracy of genomic selection for bacterial wilt resistance in tomato.

Bacterial wilt (BW) is a soil-borne disease that leads to severe damage in tomato. Host resistance against BW is considered polygenic and effective in controlling this destructive disease. In this study, genomic selection (GS), which is a promising breeding strategy to improve quantitative traits, was investigated for BW resistance. Two tomato collections, TGC1 (n = 162) and TGC2 (n = 191), were used as training populations. Disease severity was assessed using three seedling assays in each population, and the best linear unbiased prediction (BLUP) values were obtained. The 31,142 SNP data were generated using the 51K Axiom array™ in the training populations. With these data, six GS models were trained to predict genomic estimated breeding values (GEBVs) in three populations (TGC1, TGC2, and combined). The parametric models Bayesian LASSO and RR-BLUP resulted in higher levels of prediction accuracy compared with all the non-parametric models (RKHS, SVM, and random forest) in two training populations. To identify low-density markers, two subsets of 1,557 SNPs were filtered based on marker effects (Bayesian LASSO) and variable importance values (random forest) in the combined population. An additional subset was generated using 1,357 SNPs from a genome-wide association study. These subsets showed prediction accuracies of 0.699 to 0.756 in Bayesian LASSO and 0.670 to 0.682 in random forest, which were higher relative to the 31,142 SNPs (0.625 and 0.614). Moreover, high prediction accuracies (0.743 and 0.702) were found with a common set of 135 SNPs derived from the three subsets. The resulting low-density SNPs will be useful to develop a cost-effective GS strategy for BW resistance in tomato breeding programs.

Assessment of Temperature-Independent Resistance against Bacterial Wilt Using Major QTL in Cultivated Tomato (Solanum lycopersicum L.).

Bacterial wilt (Ralstonia solanacearum) is a devastating disease of cultivated tomato resulting in severe yield loss. Since chemicals are often ineffective in controlling this soil-borne pathogen, quantitative trait loci (QTL) conferring host resistance have been extensively explored. In this study, we investigated effects of ambient temperature and major QTL on bacterial wilt resistance in a collection of 50 tomato varieties. The five-week-old seedlings were inoculated using the race 1 (biovar 4 and phylotype I) strain of R. solanacearum and placed at growth chambers with three different temperatures (24 °C, 28 °C, and 36 °C). Disease severity was evaluated for seven days after inoculation using the 1-5 rating scales. Consistent bacterial wilt resistance was observed in 25 tomato varieties (R group) with the means of 1.16-1.44 for disease severity at all three temperatures. Similarly, 10 susceptible varieties with the means of 4.37-4.73 (S group) were temperature-independent. However, the other 15 varieties (R/S group) showed moderate levels of resistance at both 24 °C (1.84) and 28 °C (2.16), while they were highly susceptible with a mean of 4.20 at 36 °C. The temperature-dependent responses in the R/S group were supported by pairwise estimates of the Pearson correlation coefficients. Genotyping for three major QTL (Bwr-4, Bwr-6 and Bwr-12) found that 92% of varieties in the R group had ≥ two QTL and 40% of varieties in the R/S group had one or two QTL. This suggests that these QTL are important for stability of resistance against bacterial wilt at high ambient temperature. The resulting 25 varieties with temperature-independent resistance will be a useful resource to develop elite cultivars in tomato breeding programs.