Summary

The purpose of this thesis is to develop statistical methods for the detection of QTLs from panels characterised in multi-environment experiments. Meta-analysis methods, already widely used in human genetics, will be considered as the starting methodological basis. However, the existing MA methods do not take into account the specificities of the envisaged application. The doctoral student will therefore have to adapt the methods to - Take into account the dependencies between measurements performed on related panels, - Take into account the heterogeneity of the inter-environmental effects to be detected, - Develop innovative strategies to detect QTLs whose effect is confined to a limited number of environments to solve problems similar to those addressed by artificial networks. To answer these questions, it is important to determine whether there are information processing motifs in natural biological networks, particularly those containing microorganisms that can be engineered. It is also necessary to check whether natural biological networks, like metabolic networks, can be used as a machine learning architecture for training. The purpose of this thesis will be to answer these questions using computational methods to search for learning motifs and train machine learning, and synthetic biology methods to build biological systems (cellular or cell-free) capable of handling complex learning problems.

Contact

• Annaig de Walsche (UMR GQE Le Moulon)
• Linkedin

Publications

Journal articles

• Annaïg de Walsche, Alexis Vergne, Renaud Rincent, Fabrice Roux, Stéphane Nicolas, et al.. metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. PLoS Genetics, 2025, 21, pp.e1011553. ⟨10.1371/journal.pgen.1011553⟩. ⟨hal-04994005⟩
• Annaïg de Walsche, Franck Gauthier, Alain Charcosset, Tristan Mary-Huard. Large-scale composite hypothesis testing for omics analyses.. 2024. ⟨hal-04508559⟩