Causality models and omics data integration to identify determinants of microbiome recruitment by plants

• Project title: "Causality models and omics data integration to identify determinants of microbiome recruitment by plants"; INRAE & Institut Agro Rennes-Angers.
  • Postdoctoral researcher: Marion Naveau
  • Affiliated unit: University of Rennes, UMR6625 - IRMAR (IRMAR - Rennes Mathematical Research Institute)
  • Co-supervision:
    • Mathieu Emily (Institut Agro Rennes-Angers)
    • Anouk Zancarini (INRAE)
  • Project Duration: 2024–2026.

Project summary:

While the microbiome can improve plant growth, it remains challenging to understand how to manage this microbiome for its effective use in an agroecological context. The CausMique project explores the influence of plants on the microbial diversity of roots. To this end, the project relies on a unique dataset combining genetics, metabolomics, and metagenomics for a panel of 281 rapeseed genotypes. Since these observed variables often reflect latent, unquantifiable processes, the CausMique project aims to integrate these diverse omics data using structural equation models, which go beyond simple correlations to achieve causal inference between latent variables. Causal inference is the cornerstone of this approach, involving the development of methods to explore the causal space and define relevant causal structures. By applying this approach to the study of microbiome recruitment by plants, CausMique develops conceptual causality schemes, integrates expert knowledge, and prioritizes causal relationships to be tested. Ultimately, this will enable the selection of plant genotypes capable of recruiting beneficial microbiomes in agroecological conditions, reducing the use of inputs, and fostering more sustainable agriculture that is respectful of the environment and health.