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.A MONG the different methods that use molecular markers for selection, genomic selection (GS) has received considerable attention in the last decade. The objective of this approach is to predict the breeding values of candidates based on their molecular marker genotypes. A prediction formula is developed using the genotypes and phenotypes of reference individuals forming a calibration set (Meuwissen

Chromosomal rearrangements can be triggered by recombination between distinct but related regions. Brassica napus (AACC; 2n ¼ 38) is a recent allopolyploid species whose progenitor genomes are widely replicated.In this article, we analyze the extent to which chromosomal rearrangements originate from homeologous recombination during meiosis of haploid B. napus (n ¼ 19) by genotyping progenies of haploid 3 euploid B. napus with molecular markers. Our study focuses on three pairs of homeologous regions selected for their differing levels of divergence (N1/N11, N3/N13, and N9/N18). We show that a high number of chromosomal rearrangements occur during meiosis of B. napus haploid and are transmitted by first division restitution (FDR)-like unreduced gametes to their progeny; half of the progeny of Darmor-bzh haploids display duplications and/or losses in the chromosomal regions being studied. We demonstrate that half of these rearrangements are due to recombination between regions of primary homeology, which represents a 10-to 100-fold increase compared to the frequency of homeologous recombination measured in euploid lines. Some of the other rearrangements certainly result from recombination between paralogous regions because we observed an average of one to two autosyndetic A-A and/or C-C bivalents at metaphase I of the B. napus haploid. These results are discussed in the context of genome evolution of B. napus.

Phosphorus (P) availability in soils limits crop yields in many regions of the World, while excess of soil P triggers aquatic eutrophication in other regions. Numerous processes drive the global spatial distribution of P in agricultural soils, but their relative roles remain unclear. Here, we combined several global data sets describing these drivers with a soil P dynamics model to simulate the distribution of P in agricultural soils and to assess the contributions of the different drivers at the global scale. We analysed both the labile inorganic P (P ), a proxy of the pool involved in plant nutrition and the total soil P (P ). We found that the soil biogeochemical background corresponding to P inherited from natural soils at the conversion to agriculture (BIOG) and farming practices (FARM) were the main drivers of the spatial variability in cropland soil P content but that their contribution varied between P vs. P . When the spatial variability was computed between grid cells at half-degree resolution, we found that almost all of the P spatial variability could be explained by BIOG, while BIOG and FARM explained 38% and 63% of P spatial variability, respectively. Our work also showed that the driver contribution was sensitive to the spatial scale characterizing the variability (grid cell vs. continent) and to the region of interest (global vs. tropics for instance). In particular, the heterogeneity of farming practices between continents was large enough to make FARM contribute to the variability in P at that scale. We thus demonstrated how the different drivers were combined to explain the global distribution of agricultural soil P. Our study is also a promising approach to investigate the potential effect of P as a limiting factor for agroecosystems at the global scale.