Dissecting quantitative trait variation in the resequencing era: complementarity of bi-parental, multi-parental and association panels

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.This work was supported by CEA-IG/CNG, by performing the DNA QC and providing access to INRA-EPGV to their Illumina Sequencing Platform. We acknowledge groups of Anne Boland (DNA and Cell Bank service) and Marie-Thérèse Bihoreau (Illumina HT Sequencing). The ANR MAGIC-Tom SNP project 09-GENM-109G and the European Solanaceae Integrated Project EUSOL (Food-CT-2006-016214) supported this work. LP was supported by a postdoctoral INRA fellowship, EA by an INRA PhD fellowship and JD by a grant from the Embassy of France in Thailand in Junior Research Fellowship Program 2014.Peer reviewe

Efficiency of genomic selection for tomato fruit quality

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

Genetic dissection of tomato fruit quality in the genome era: new tools for in depth QTL characterization

Tomato fruit quality is characterized by a large number of components influenced by the genotypes and the environment. To better understand t~e genetic control of these components, quantitative trait loci (QTL) have been mapped for years. Today the genome sequence availability changes the paradigm of genetic approaches. QTL have been first identified using linkage mapping populations and positional cloning identified a few QTLs. However Iinkage mapping is Iimited to the analysis of traits differing between two parentallines. Genome-wide association (GWA) has then been proposed to assess a large range ofvariability. ln tomato, we have shown that GWAstudy is possible, using the admixed nature of cherry tomato genomes that reduces the impact of population structure. Nevertheless, GWAsuccess is limited in sorne regions due to the low decay of linkage disequilibrium, which varies along the genome. Rare alleles are also difficult to detect with GWA studies. Multi-parent advanced generation intercross (MAGIC) populations offer an alternative to traditionallinkage and GWAs by increasing the precision of QTLmapping but with equilibrated allelic frequencies. We have developed a MAGICpopulation by crossing eight tomato lines whose genomes were resequenced. We showed the potential of the MAGIC population when coupled with whole genome sequèncing to detect candidate single nuc\eotide polymorphisms (SNP) 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, will be discussed

Genetic variability of eggplant germplasm evaluated under open field and glasshouse cropping conditions

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