To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false-positives (with no-cleaning and/or a low threshold for minor allele frequency) and the risk of missing interesting rare alleles. Cleaning can lower the risk of the latter by making it possible to choose a higher threshold in GWAS.

Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System.

Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi-source and multi-scale data, but also because it is based on 10 yr of trial and error in our groups.

Milk acidification by Lactococcus lactis is improved by decreasing the level of dissolved oxygen rather than decreasing redox potential in the milk prior to inoculation.

Although redox potential is very rarely taken into account in food fermentation it could be as influential as pH on bacterial activities. Lactococcus lactis is already known to exhibit a powerful reducing activity in milk but its reduction activity was shown to occur prior to its acidification activity with a potential interaction between these two lactococcal activities. Therefore, acidification lag-type phase could be shortened by decreasing the redox potential of milk before inoculation. As the redox potential is highly dependent on the dissolved oxygen level, our objective was to study their separate and combined influences on acidification and growth kinetics of pure L. lactis strains in milk. Results showed that high level of dissolved oxygen is significantly more influential on growth, and even more on acidification kinetics, than initial decreased redox potential of milk. Reduction of milk was drastic and mostly due to bacterial activity. The redox potential of milk only dropped when dissolved oxygen was entirely consumed. When there was no dissolved oxygen from the beginning, L. lactis immediately decreased the redox potential of milk and acidified afterwards. When the level of dissolved oxygen was initially high, acidification and reduction of milk occurred at the same time. Acidification kinetics was then biphasic with a slower rate during the aerobic stage and a faster rate during the anaerobic stage. The seven strains tested demonstrated diversity in both their acidification kinetics and their adaptation to high level of dissolved oxygen, independent of their growth kinetics. To conclude, we have shown that the level of dissolved oxygen in milk has a dramatic influence on acidification kinetics and could be used to control acidification kinetics in dairy industries.