Missions

Genome-wide association studies (GWAS) on several traits of interest related to bean development in different environments have been conducted, along with a GWAS meta-analysis that enabled the identification of loci involved in GxE (genotype-by-environment) interactions.
The recruited postdoctoral researcher will be responsible for:
• Reflecting on how to account for admixture between the Andean and Mesoamerican gene pools in GWAS;
• Exploring the potential overlap between GxE interaction loci and environmental association loci, in order to identify genetic polymorphisms involved in responses to specific environmental cues;
• Developing genomic prediction models for multiple traits of interest related to bean development and growth, and analyzing the potential benefit of including the GWAS-identified loci in these models;
• Using genomic selection (GS) to predict the performance of a large panel and to identify original sources of diversity within this panel.

Activités

Analysis of the genetic mechanisms of environmental adaptation in common bean.

Compétences

The candidate must hold a PhD in quantitative genetics (including experience with GWAS), statistics/biostatistics, or computational biology applied to quantitative genetics. Solid experi-ence in R programming, handling large datasets, data visualization, and knowledge of version control tools are required. Prior knowledge of common bean and/or climate variables would be an asset.

Contexte de travail

In the context of climate change, a major challenge in crop improvement is to develop varieties adapted to environmental constraints in order to ensure food security. The molecular information now available for many species makes it possible to identify loci involved in the variation of complex traits through genome-wide association studies (GWAS) and to improve the efficiency of breeding schemes using prediction models in genomic selection (GS). Once calibrated on individuals that have both phenotypic and genotypic data, GS models can predict plant performance in environments where they have not yet been observed and identify original sources of diversity. Applying GWAS and GS to crops such as common bean (Phaseolus vulgaris) should help improve breeding strategies and increase crop productivity.
The INCREASE project [1] combines cutting-edge approaches in plant genetics and genomics, high-throughput phenotyping—including molecular phenotyping (e.g., transcriptomics and metabolomics)—to enhance the conservation of genetic resources of European food legumes, particularly the common bean, and to promote their use and valorization.
In this project, phenotypic data on more than 450 sequenced common bean lines have been collected across six well-characterized environments (T-core). These data provide an opportunity to better understand the responses of these lines to various environments, to study the genetic basis of these responses, and to develop GS prediction models. The climate of the lines' geographical origins will also be taken into account to identify how selection driven by climatic pressures has shaped the current genetic diversity of common bean. In addition to the T-core, a broader collection of lines (R-core) has been genotyped but not phenotyped. One of the challenges will be to predict the performance of these lines and to identify among them original sources of diversity.
This postdoctoral position is funded by the European INCREASE project. The selected candidate will join the GQE-Le Moulon laboratory and work under the supervision of Tristan Mary-Huard (Senior Researcher in Statistics) and Élodie Marchadier (Associate Professor in Biology), 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).

Contraintes et risques

NC