Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.)

Chantier qualité GAGenomic 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

Structure–Function Relationships of Microbial Communities

International audienceMicrobial community structure is the result of environmental conditions that vary significantly and frequently. In laboratory conditions, associations from two microorganisms to complex microbial communities are used to mimic real ecosystems and their functions. Nevertheless, the composition of individual members of complex ecosystems and their relationships, as well the environmental conditions needed to re-create microcosms and their associated activities, are hard to reproduce. The present chapter gives a laboratory-based methodological approach to study structure-function relationships. The results obtained from a systematic study of various ecosystems and the extent of environmental conditions that dictate the structure of communities and the link with ecosystem function are discussed. We also comment on, to what extent the results obtained in laboratory conditions are transposable to natural ecosystems. Finally, three specific case-studies related to cheese ripening are developed to illustrate how microbial ecology can be integrated into food microbiology for better quality and safety of smear cheeses