ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Improvement of fruit quality traits is a major goal for tomato breeding. Deciphering the genetic diversity and inheritance of fruit quality components is thus necessary. For this purpose, we carried out a large multi-level omic experiment. Eight contrasted lines and 4 of their F-1 hybrids were phenotyped for fruit development traits. Fruit pericarp samples were analysed at 2 stages (cell expansion and orange ripe) and different scales: (1) untargeted profiling of major polar metabolites, (2) activities of 28 enzymes involved in primary metabolism, (3) proteome profiles revealed by 2D-PAGE and identification of 470 protein spots showing quantitative variations and (4) gene expression analysis by Digital Gene Expression. In parallel, the 8 lines were resequenced and more than 3 million SNPs identified when aligned on the reference tomato genome. This experiment allowed us to assess and compare the range of variability and inheritance mode of the metabolic traits and expression data. Correlation networks were constructed within and between levels of analysis to identify regulatory networks. Diversity of candidate genes could thus be analysed, relating the polymorphisms at the sequence levels with their expression.

In the context of the sequencing project for the grapevine reference genome sequence, two genetic maps were completed with SSR markers in order to align the sequence contigs of the Vv12X.0 version of the genome assembly along the chromosomes. For this purpose, two full-sib families, 'Syrah' x 'Grenache' (SxG, 192 individuals) and 'Chardonnay' x 'Bianca' (CxB, 358 individuals) were respectively scored with 223 and 401 SSR. These two maps, supplemented with former data, allowed anchoring over 90% of the reference genome sequence. SNP markers were then defined from 37 anchored but not oriented and 44 not oriented and not anchored scaffolds of sequence. So far 110 new markers have been mapped, allowing anchoring 31 not yet anchored scaffolds (similar to 9 Mb) and orienting 35 scaffolds (similar to 15.5 Mb). In parallel, the recombination rate along the chromosomes is under study, in relation with sequence features (gene/repeat density, %GC, distance to the centromere/telomere). Finally, we are developing a cytogenetic map in Vitis vinifera using BAC-FISH. All these approaches will be combined to study the structure of the Vitis vinifera genome, including the factors affecting recombination rates and the dynamics of the genome evolution.

Genetic and phenotypic analysis of two complementary maize panels revealed an important variation for biomass yield. Flowering and biomass QTL were discovered by association mapping in both panels.

Genomic selection refers to the use of genotypic information for predicting breeding values of selection candidates. A prediction formula is calibrated with the genotypes and phenotypes of reference individuals constituting the calibration set. The size and the composition of this set are essential parameters affecting the prediction reliabilities. The objective of this study was to maximize reliabilities by optimizing the calibration set. Different criteria based on the diversity or on the prediction error variance (PEV) derived from the realized additive relationship matrix-best linear unbiased predictions model (RA-BLUP) were used to select the reference individuals. For the latter, we considered the mean of the PEV of the contrasts between each selection candidate and the mean of the population (PEVmean) and the mean of the expected reliabilities of the same contrasts (CDmean). These criteria were tested with phenotypic data collected on two diversity panels of maize (Zea mays L.) genotyped with a 50k SNPs array. In the two panels, samples chosen based on CDmean gave higher reliabilities than random samples for various calibration set sizes. CDmean also appeared superior to PEVmean, which can be explained by the fact that it takes into account the reduction of variance due to the relatedness between individuals. Selected samples were close to optimality for a wide range of trait heritabilities, which suggests that the strategy presented here can efficiently sample subsets in panels of inbred lines. A script to optimize reference samples based on CDmean is available on request.