We used the Roche-454 platform to sequence from normalized cDNA libraries from each of two inbred lines of onion (OH1 and 5225). From approximately 1.6 million reads from each inbred, 27,065 and 33,254 cDNA contigs were assembled from OH1 and 5225, respectively. In total, 3,364 well supported single nucleotide polymorphisms (SNPs) on 1,716 cDNA contigs were identified between these two inbreds. One SNP on each of 1,256 contigs was randomly selected for genotyping. OH1 and 5225 were crossed and 182 gynogenic haploids extracted from hybrid plants were used for SNP mapping. A total of 597 SNPs segregated in the OH1 × 5225 haploid family and a genetic map of ten linkage groups (LOD ≥8) was constructed. Three hundred and thirty-nine of the newly identified SNPs were also mapped using a previously developed segregating family from BYG15-23 × AC43, and 223 common SNPs were used to join the two maps. Because these new SNPs are in expressed regions of the genome and commonly occur among onion germplasms, they will be useful for genetic mapping, gene tagging, marker-aided selection, quality control of seed lots, and fingerprinting of cultivars.
Quantitative trait loci (QTL) have been identified using traditional linkage mapping and positional cloning identified several QTLs. However linkage mapping is limited to the analysis of traits differing between two lines and the impact of the genetic background on QTL effect has been underlined. Genome-wide association studies (GWAs) were proposed to circumvent these limitations. In tomato, we have shown that GWAs is possible, using the admixed nature of cherry tomato genomes that reduces the impact of population structure. Nevertheless, GWAs success might be limited due to the low decay of linkage disequilibrium, which varies along the genome in this species. Multi-parent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage and GWAs by increasing the precision of QTL mapping. We have developed a MAGIC population by crossing eight tomato lines whose genomes were resequenced. We showed the potential of the MAGIC population when coupled with whole genome sequencing to detect candidate single nucleotide polymorphisms (SNPs) underlying the QTLs. QTLs for fruit quality traits were mapped and related to the variations detected at the genome sequence and expression levels. The advantages and limitations of the three types of population, in the context of the available genome sequence and resequencing facilities, are discussed.
Fruit quality is polygenic; each component has variable heritability and is difficult to assess. Genomic selection, which allows the prediction of phenotypes based on the whole-genome genotype, could vastly help to improve fruit quality. The goal of this study is to evaluate the accuracy of genomic selection for several metabolomic and quality traits by cross-validation and to estimate the impact of different factors on its accuracy. We analyzed data from 45 phenotypic traits and genotypic data obtained from a previous study of genetic association on a collection of 163 tomato accessions. We tested the influence of (1) the size of training population, (2) the number and density of molecular markers and (3) individual relatedness on the accuracy of prediction. The prediction accuracy of phenotypic values was largely related to the heritability of the traits. The size of training population increased the accuracy of predictions. Using 122 accessions and 5995 single nucleotide polymorphisms (SNPs) was the optimal condition. The density of markers and their numbers also affected the accuracy of the prediction. Using 2313 SNP markers distributed 0.1 cM or more apart from each other reduced the accuracy of prediction, and no gain in prediction accuracy was found when more markers were used in the model. Additionally, the more accessions were related, the more accurate were the predictions. Finally, the structure of the population negatively affected the prediction accuracy. In conclusion, the results obtained by cross-validation illustrated the effect of several parameters on the accuracy of prediction and revealed the potential of genomic selection in tomato breeding programs
Knowledge of agro-morphological genetic variation and cropping conditions on vegetative and yield-related traits plays a significant role in varietal improvement and production of eggplant (Solanum melongena L.). Following this premise, the current study was conducted to critically asses the genetic variation of 29 eggplant accessions by using agro-morphological characterization evaluated under two cropping conditions, namely, glasshouse and open field. The experiments were laid out in randomized complete block design (RCBD) with three replications. Data on vegetative and yield characteristics were collected and subjected to analysis of variance (ANOVA) using SAS 9.4, while variance components were estimated manually. The results obtained from the analysis of variance indicated a highly significant difference (p ≤ 0.01) for all characteristics studied in both cropping conditions. The evaluated accessions were grouped into six major clusters based on agro-morphological traits using Unweighted Pair Group Method with Arithmetic mean (UPGMA) dendrogram. Hence, crosses between group I with VI or V could be used to attain higher heterosis and vigor among the accessions. Also, this evaluation could be used as a selection criterion for important yield agronomic traits in eggplant. The methodology and the approaches used may provide a model for the enhancement of other vegetable crop diversity towards adaptability to the cropping condition decision. This result displayed importance for preserving eggplant germplasm for future varietal development and revealed that open field cropping condition is more suitable under Malaysia’s agroecology.
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