Société Française de Bioinformatique

Mots-Clés

population genetics gene regulatory networks polygenic selection complex traits maize adaptation

Description

Work environment, missions and activities

You will be welcomed in the GEvAD team (Evolutionary Genetics and Crops Adaptation, http://moulon.inrae.fr/en/equipes/gevad/) at the UMR Quantitative genetics and Evolution (GQE) – Le Moulon (Gif-sur-Yvette, France). The team combines various approaches (experiments, computational models, population genetics, genomics, systems biology) to understand the evolutionary mechanisms behind the domestication and environmental adaptation of crops. GQE is part of IDEEV (the Institute for the Diversity, Ecology and Evolution of the Living World, https://www.ideev.universite-paris-saclay.fr/en/), located on the Paris-Saclay campus. The postdoc will participate to the ANR JCJC NETWITS project, led by M. Fagny, which aims at exploring the role of gene regulatory networks structure in maize response to water deficit.

The specialist in population genetics will aim to develop a pipeline to detect signatures of polygenic selection in the context of epistasis and pleiotropy, in order to study the link between gene regulatory network topology and natural selection. He/She will use maize adaptation to water deficit as a model. He/She will collaborate closely with the Postdoctoral fellow hired as part of the NETWITS project. His/Her missions will be to:

• Simulate various polygenic selection scenarios targeting gene expression regulation;
• Develop a pipeline to detect polygenic selection signatures;
• Test the statistical power of this pipeline and its sensitivity to various confounding factors;
• Apply this pipeline to genomic and transcriptomic data from maize grown with and without water deficit;
• Prepare data for publication in publicly available repository (e.g.: dataverse INRAE);
• Contribute to writing articles.

The work can be performed partly remotely (3 days/week maximum).

Required skills and knowledges

• Diploma: PhD ;
• Recommended training: Theoretical and/or applied population genetics.
• Required skills and knowledge:
  • Forward-in-time population evolution models, summary statistics computation.
  • Bioinformatics: programming skills in python, shell, R, SLiM, FAIR data management, knows how to use computational clusters;
  • Scientific articles writing;
  • Students supervision;
  • Spoken & written English: B1 to B2 level (Common European Framework of Reference for Languages);
  • Knowledges in systems biology/gene regulatory networks will be appreciated but are not necessary.