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Génétique Quantitative et Évolution - Le Moulon

Post-doc offer in quantitative genetics

Postdoc job offer: Dissecting the genetic mechanisms of environmental adaptation in beans

Key words : Association studies, Statistical modeling, GxE interactions, Common bean, Local adaptation, Adaptation to environment, Genomic selection

GQE-Le Moulon

Context :

In the context of climate change, breeding for plants adapted to environmental constraints is of major importance to ensure human food safety. Molecular information that are now available in numerous species can be used to identify loci involved in complex traits variation through Genome-wide association studies (GWAS) and improve breeding schemes efficiency using Genomic Selection (GS) prediction models. Once calibrated on individuals both phenotyped and genotyped, GS models can be used to predict performances of plants in environments where they have not been observed yet, and also to identify original sources of diversity. The application of GWAS and GS in crops such as beans (Phaseolus vulgaris) holds significant promise for improving breeding strategies and enhancing crop productivity. The INCREASE [1] project combines cutting-edge approaches in plant genetics and genomics, high throughput phenotyping, including molecular phenotyping (e.g. transcriptomics and metabolomics) to boost the conservation of European food legumes genetic resources, especially common bean, and promote their use and valorization. In this project, phenotype data on more than 300 common bean sequenced lines have been collected in 6 environments, well characterized (T-core). These data offer the opportunity of better understanding the responses of these lines to several environments and of investigating the genetic determinism of these responses and to develop GS prediction models. Climate of the geographical origin of the domesticated lines will also be considered in order to identify climate-related patterns of selection that shaped the current genetic diversity of common bean. In addition to the T-core a larger collection of lines (the R-core) have been genotyped but not phenotyped. One challenge would be to predict the performances of these lines and to identify among them original sources of diversity.

The recruited post-doc will :

• perform genome-wide association studies on several traits of interest related to bean development in different environments and evaluate the impact of GxE interactions on the expression of target traits, and perform a meta-analysis of GWAS to identify loci involved in GxE interactions [2]. • identify genomic regions associated with specific environmental factors (e.g., temperature, water availability, soil fertility) and evaluate their contributions to phenotypic variation in order to better understand local adaptation of local varieties • investigate the potential overlap between GxE interaction loci and environmental association loci to understand whether specific genetic variants mediate the response to particular environmental cues • develop genomic-prediction models on several traits of interest related to bean development and growth and investigate the potential gain of including loci identified in GWAS approach in prediction models [3] • Use GS prediction to predit the performances of the R-core panel and identify within this panel original sources of diversity [4].

Expected outcomes:

  • identification of genomic regions associated with agronomic traits in bean crops, providing valuable insights for marker-assisted selection and breeding programs
  • characterization of GxE interactions, allowing breeders to develop environment-specific cultivars and optimize production strategies
  • identification of genetic variants responsible for adaptation to specific environmental conditions, aiding in the development of stress-tolerant bean varieties
  • enhancing our understanding of the complex interplay between genetics and environment in shaping the phenotypic variation in beans, contributing to the broader field of plant genetics and breeding.
  • identification of lines from the R-core panel which might be interesting to phenotype / use as future sources of diversity Required skills The candidate should have a PhD in quantitative genetics (including experience in GWAS analysis), Statistics/ Biostatistics or Computational biology applied to quantitative genetics. Good experience in R programming, versioning, management of large datasets and data visualization. Some prior knowledge on common bean and/or climate variables would be a plus. Working environment, starting date This post-doctoral position is funded by the European INCREASE project. The candidate will be hosted in the GQE Le Moulon laboratory, and supervised by Elodie Marchadier (Associate professor in biology) and Tristan Mary-Huard (Senior researcher in statistics), in close collaboration with Laurence Moreau (Senior researcher in quantitative genetics) and Christine Dillmann (Professor in evolutionary biology). The starting date could be as soon as Sept. 1, 2023, and last 18 months (with a possible extension up to 6 months).

Contact :

To apply to this offer, candidates should send a CV and a motivation letter to: Elodie Marchadier : elodie.marchadier@universite-paris-saclay.fr Tristan Mary-Huard : tristan.mary-huard@agroparistech.fr

References :

[1] Bellucci, E., Mario Aguilar, O., Alseekh, S., Bett, K., Brezeanu, C., Cook, D., … & Papa, R. (2021). The INCREASE project: Intelligent Collections of food‐legume genetic resources for European agrofood systems. The Plant Journal

[2] De Walsche, A., Vergne, A., Rincent, R., Roux, F., Nicolas, S. D., Welcker, C., … & Mary-Huard, T. (2023). metaGE: Investigating Genotype-by-Environment interactions through meta-analysis. bioRxiv.

[3] Roth, M., Beugnot, A., Mary-Huard, T., Moreau, L., Charcosset, A., & Fiévet, J. B. (2022). Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Genetics

[4] Allier, A., Teyssèdre, S., Lehermeier, C., Charcosset, A., & Moreau, L. (2020). Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs. Theoretical and Applied Genetics, 133(1), 201-215.