Mots-Clés

adaptation genetics GWAS climate bean

Description

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 (GWA) studies 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 450 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
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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 GWA 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

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investigate the potential overlap between GxE interaction loci and environmental
association loci to identify the genetic variants that mediate the response to specific
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 GWA approach in prediction models [3]
Use GS prediction to predict 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), Christine
Dillmann (Professor in evolutionary biology) and Maud Tenaillon (Senior researcher in
population genomics).

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.