HOLOBIONTS

Animals and their microbiota form a composite organism called the holobiont, which can be considered the ultimate unit on which evolution and selection act. The host's genes and the environment influence the colonization, development, and functioning of various microbiotas, which, in turn, contribute to shaping the host's phenotypes. In the context of livestock systems adhering to agroecological principles, the goals are to reduce environmental footprints (greenhouse gas emissions, water and energy supply, input reduction, etc.). Animals must adapt to changing and complex environments (climate change, diverse non-competitive nutritional resources, less controlled and protective environments against pathogens, etc.). In this context, it is important to determine the role of host/microbiota components in the variability of relevant phenotypes from an agroecological perspective and to understand how the host's genetics control the symbiotic microbiota.

The overall objective is to develop integrative hologenetic approaches for animal selection, using the most innovative technologies to generate, process, and analyze genetic and genomic data of the host and its microbiota, as well as the phenotypes and environmental parameters in which holobionts evolve. For this purpose, the project consists of four work axes:

• Develop state-of-the-art phenotyping data analysis methods (mainly high-throughput and continuous) for the holobiont, their modeling, and the analysis of their interrelationships with microbiota data.
• Optimize joint modeling methods for host allelic variability, metagenomic indicators, and their interaction to better understand the genetic determinism of traits involved in animal adaptation to agroecological production systems and improve selection accuracy in different species of interest.
• Produce large reference populations with the generation of high-throughput automated phenotypes, microbiota data, and high-throughput genotyping in uncontrolled and fluctuating environments at various physiological stages.
• Generate and analyze multi-omics data at the host and microbiota level to understand the mechanisms that link the microbiota to its host, identifying key players in the dialogue that enables the establishment and persistence of symbiotic interactions.

Omics data that best describe the functional characteristics of the holobiont will be explored to implement predictive models capable of predicting functional features of metagenomes in data sets. These results will refine selection objectives.