POST-DOC : Predicting maize phenotypes under drought using biological priors

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

The postdoctoral fellow 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). 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 GEvAD team combines various approaches, including field and greenhouses experiments, theoretical (models, stat development) and applied population genetics, genomics, systems genomics, to understand the evolutionary mechanisms behind the domestication and environmental adaptation of crops. The postdoctoral fellow will also be collaborating with members of the GQMS team (Quantitative genetics and Plant Breeding Methodology), whose research focus on developping experimental and theoretical approaches, statistical methods and decision support tools to understand maize diversity and decipher the architecture of quantitative traits to optimize maize breeding schemes.

The postdoctoral fellow will participate to the ANR JCJC NETWITS project, led by Maud Fagny, which aims at exploring the role of gene regulatory networks structure in maize response to drought. GQE has an important expertise on the molecular bases of maize drought response, including association studies and the inference of gene regulatory networks. We thus have identified numerous loci, both genes and regulatory elements, potentially involved in determining maize yield in response to drought.

The postdoctoral fellow, specialist in quantitative genetics, will develop a yield prediction method for maize in drought condition that will leverage the available information. The aim will be to integrate prior biological information about gene expression regulation and natural selection within the model. Working with data generated by the members of the NETWITS project and others, the postdoctoral fellow will pursue the following integration steps:

1/ Classify the polymorphisms into different categories according to their expected importance in the regulatory network, GWAS, eQTL and population genetics analyses.

2/ Use bio-informed methods such as (Bertolini 2025), GFBLUP (Edwards 2016, Fang 2017), or BayesRC+ (Fikere 2018, Mollandin 2022) to directly integrate the regulatory network based classification in the model.

3/ Eventually implement a bio-informed neural network to directly integrate the regulatory interactions in the model (NetGP, Zhao 2025; DLGBLUP, Shokor 2025).

The predictive abilities of these models will be compared to reference models such as GBLUP in different prediction scenarios potentially involving genotype x environment interactions. For this, the postdoctoral fellow will design cross-validation scenarios based on a multi-environment trial of 250 maize hybrids evaluated in 25 environments. They will particularly focus on the prediction of genetically distant material. All datasets are already curated and ready to use.

The postdoctoral fellow will be supervised by M. Fagny (GEvAD), Renaud Rincent and Tristan Mary-Huard (GQMS). They will collaborate closely with the other participants of the NETWITS project, in order to integrate their results in the model. The postdoctoral fellow will also be responsible for supervising interns (licence or master students) and to help training the PhD students of the team in quantitative genetics.

The work can be performed partly remotely (2 days/week maximum). 

Training and skills

- The candidate is required to hold a PhD.

- Academic knowledges: Advanced knowledges in quantitative genetics are required; an experience with deep neural network models would be preferred, but not indispensable. Knowledges in systems genomics/gene regulatory networks or in population genetics will be appreciated but are not necessary.

- Bioinformatics: programming skills are required in at least one of the following languages: python or R. Skills in shell and SLURM-based computational clusters will be appreciated. Basic knowledge of the FAIR principles and about git usage are required as all scripts will be developed and made publicly available following the FAIR management standards.

- Communications skills: writing scientific articles, giving poster presentations and talks are required skills to valorize the scientific results. Spoken & written English: B1 to B2 level (Common European Framework of Reference for Languages) is required.

- Interest in supervising students will be appreciated.

Bibliography

Bertolini E., et al., 2024. Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize. Genetics228(4): iyae162. https://doi.org/10.1093/genetics/iyae162

Edwards, S. M., Sorensen, I. F., Sarup, P., Mackay, T. F., & Sorensen, P. (2016). Genomic prediction for quantitative traits is improved by mapping variants to gene ontology categories in Drosophila melanogaster. Genetics, 203(4), 1871–1883. https://doi.org/10.1534/genetics.116.187161

Fang L., et al., 2017. Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds. BMC Genomics 18:604. https://doi.org/10.1186/s12864-017-4004-z

Fikere M., et al., 2018. Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines. The Plants Genome 11:170100. https://doi.org/10.3835/plantgenome2017.11.0100

Zhao L., et al., 2025. Genomic Prediction with NetGP Based on Gene Network and Multi-omics Data in Plants. Plant Biotechnology Journal 23:1190-1201. https://doi.org/10.1111/pbi.14577

Mollandin F., et al., 2022. Accounting for overlapping annotations in genomic prediction models of complex traits. BMC Bioinformatics 23(1):365. https://doi.org/10.1186/s12859-022-04914-5

Shokor, F., Croiseau, P., Gangloff, H., Saintilan, R., Tribout, T., Mary-Huard, T., & Cuyabano, B. C. D. (2025). Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits. Journal of Dairy Science 108(6) :6174-6189. https://doi.org/10.3168/jds.2024-26057 

INRAE's life quality

By joining our teams, you benefit from (depending on the type of contract and its duration):

- up to 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.

How to apply

I send my CV and my motivation letter