Statistical analysis of omics data for predictive models of feed efficiency
Deadline for applications
Date of publication


Details on the type of contract
Postdoctoral contract
Duration of contract
12 months

around 2300 € gross salary/month


Name of unit of assignment
UMR1348 PEGASE - Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage
Address of unit of assignment
INRA - Domaine de la Prise - 35590 SAINT GILLES
Website of unit of assignment
Region of assignment


Working environment

Unit and Team of assignment: UMR1348 Pegase Unit (Physiology, Environment and Genetics for Animal and Livestock Systems) gathers over 125 persons in different teams on pig, dairy ruminants or poultry and studying animal physiology and livestock production systems. The main goal is to provide the elements that will make animal production more sustainable. The team entitled “Physiology and Metabolisms of Growth” aims at a better understanding of energy homeostasis in growing pigs, and use different approaches related to molecular biology and functional genomics.  


Missions: Improving the efficiency of animals in the livestock sector is a prioritized research topic for contributing to competitive industries under the Horizon 2020 strategy. Progress must be made in identifying appropriate indicator traits that reflect better resource-use efficiency by the animals. In the framework of the Feed-a-Gene EU project (; Grant Agreement n°633531), we will explore and identify new animal traits directly or indirectly related to variation in the animal’s feed efficiency. We have already acquired many transcriptomics data (microarrays, RNAseq and/or qPCR) from pig lines divergently selected for feed efficiency and reared under different conditions. The successful candidate (post-doctoral degree) will have to use/develop statistical methods to propose combinations of gene expression levels across these datasets to predict feed efficiency. He/She will use statistical techniques (random forests, PLS, decision trees) to determine relevant models, together with bioinformatics tools (data-mining, gene networks) to give the biological significance behind the selected genes. He/She will also have to write an original paper and disseminate obtained results through scientific meetings.

Training and skills required
  • Training: Applicants must have a PhD degree in biological sciences with excellent knowledge in statistics.
  • Skills required: Statistics - Functional genomics – Programing in R language.
  • Previous: Extensive experience in analysis of transcriptomics data will be an advantage.
  • Others: Motivation to work with statistical tools, willingness to work in data mining, good oral and written communication skills. Engaged and highly motivated with a good ability to work within a research team.


Florence GONDRET and Isabelle LOUVEAU