PhD position - Multi-omics analyses of DNA methylation and gene expression to elucidate mechanisms of heat stress resistance in laying chickens

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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

Abstract of the Thesis Proposal 

Climate change is one of the major challenges that animal production is facing worldwide. Hot climate extremes are increasing not only in tropical regions but also in temperate areas. Chickens are highly vulnerable to heat stress, due to their poor thermoregulatory capacity due to feather insulation and the absence of sweat glands (Zerjal et al., 2013). Heat stress acts through complex physiological changes (Brugaletta et al.,2022) and epigenetic mechanisms as the DNA methylation, appear to mediate physiological responses and adaptation (Pitel et al., 2022; Karami et al., 2025). 

High-throughput technologies now enable comprehensive analysis of multiple layers of molecular information. This thesis will make use of a unique dataset combining transcriptomic and DNA methylation data obtained from five chicken genotypes exhibiting contrasting levels of heat resistance and feed efficiency, either exposed or not to heat stress.

The aim is to apply and evaluate statistical methods for the integrative analysis of high-dimensional multi-omics data, and to better understand the molecular basis of heat resistance. This includes the use of existing integrative statistical approaches (e.g., MOFA, DIABLO, Bayesian models, network-based methods), the assessment of their relevance and limitations in this context, and the testing of new methodologies developed within the research group (Majumdar et al., 2024; Tomilina et al., 2024). The results will then be basis for identifying stress-related molecular signatures that are either shared across genotypes or specific to particular ones, and to link these markers to the extensive set of available phenotypes (production, feed efficiency, quality, physiology). The ultimate goal is to identify predictive biomarkers of thermotolerance and to improve our understanding of the biological mechanisms underlying resistance or sensitivity to heat stress in laying hens.

Purpose and Background 

The central hypothesis of this project is that differences in heat tolerance across chicken genotypes are mediated by heat-induced epigenetic and transcriptomic modifications, which affect key molecular pathways related to resilience. Multi-omics integration will reveal these molecular mechanisms and identify predictive markers of thermotolerance.

Based on this hypothesis, the thesis will pursue several key objectives. First, the project will apply and evaluate statistical methods for the integrative analysis of high-dimensional multi-omics data, including existing approaches as well as new methodologies developed within the research group. Particular attention will be paid to assessing their relevance, limitations, and potential improvements in the context of complex biological data. 

Second, the thesis aims to characterize heat stress-induced DNA methylation changes in liver and blood, investigate their relationship with gene expression, and identify methylation marks and genes exhibiting genotype x environment (G×E) interactions across chicken genotypes with contrasting heat resistance and feed efficiency. 

To this end the questions we will try to answer are: 

• What is the role of methylation in liver and blood in the response to heat stress? 
• Are methylation changes comparable among tissues? 
• Do environmental factors induce methylation changes that correlate with expression differences? 
• Are there genotype-specific methylation patterns that mediate expression responses (GxE at the epigenetic level)?
• To what extent are methylation and transcriptome related to the variability of phenotypic traits?

Methods

This project disposes of multiple omics datasets produced on the same samples from 5 different chicken breeds and lines presenting contrasted levels of heat resistance and feed efficiency. 

Statistical analysis methods will include:

• differential analysis of omics data to identify Differentially Expressed Genes (DEG) (e.g. with R packages DESeq2) and Differentially Methylated Cytosines and Regions (DMC and DMR) (e.g. with DSS) in order to identify an initial list of candidate markers and for subsequent gene set enrichment analyses; 
• differential methylation analysis at the gene level (detecting changes in the full shape of their methylation profile) and at the region level (promoter, gene body, …), will also be explored in order to better understand the link between methylation and gene expression; 
• multi-omics integrative analyses to understand the between-omics relations, by using unsupervised (e.g. multiple factor analysis such as MOFA) and supervised approaches (e.g. multiblocks sPLS-DA such as DIABLO), and subsequently link these patterns with the available phenotypic traits;
• Clustering, functional and gene set enrichment analysis (WGCNA and GSEA, for example with clusterProfiler) to identify biological paths implicated in the heat stress resistance (or sensitivity); 
• identification of heat stress (sensitivity) markers using machine learning methods such as regularized (logistic) regression, or random forests after an initial pre-selection of the potential markers;
• methods recently developed within GiBBS team (GABI, INRAE) for multi-omics analyses, such as:
  • BayesOmics¹ (taking into account correlation within omics),
  • Idiffomix² (methylation and gene expression altogether - from paired samples),
  • Heterocop³, to infer multi-omics correlation networks (after a first pre-selection of candidate methylation sites and genes).

¹https://github.com/terenceviellard/BayesOmics

²https://cran.r-project.org/web/packages/idiffomix/vignettes/vignettes.html

³heterocop: an R package for Gaussian copula semi-parametric inference for heterogeneous data

 

Expected results

This thesis will improve knowledge of heat stress induced methylation changes in liver and blood, their relationship with transcriptomic changes and to identify methylation marks and genes exhibiting G×E interactions across five chicken genotypes. On the other hand, data integration approaches will be applied using a range of methods. The final aim is to draw a broader and more complex picture of the mechanisms involved in resistance or sensitivity to heat stress, finding molecular signatures that are correlated across omics data, and connecting phenotypic traits with intermediate phenotypes that could serve as molecular proxies. 

Supervision

The PhD student will be part of the GiBBS team and will be supervised by Tatiana Zerjal, Marie Courbariaux, and Andrea Rau. The student will be accompanied at each statistical step analyses as well as for the biological interpretation of the results. Weekly meetings will be organised to discuss about the progress and additional meeting will be schedule as needed. Methodological support will be provided, along with access to computational resources.

Scientific and logistical requirements (specific safety requirements) and financial requirements of the research project 

The Ph.D. will take place in the Animal Genetics and Integrative Biology (GABI) research unit at the INRAE Jouy-en-Josas research center, within the “Genomics, Biodiversity, Bioinformatics, Statistics” (GiBBS) team. The Ph.D. student will have access to all computational resources required for the project and will benefit from the scientific environment of the unit. Thesis project relies exclusively on already produced multi-omics data; financial needs other than salary are very limited. Expenses related to travel, conference attendance, and scientific publications will be covered by the host team’s operating budget. Half of the funding for the salary is secured. The other half may be allocated by ABIES doctoral school if the candidate succeeds in passing the doctoral school’s competition (June 3, 4, or 5, 2026, at AgroParisTech, Palaiseau).

Objectives for the dissemination of the doctoral student's research: dissemination, publication etc.

Over the course of the thesis, we plan to publish three articles in open-access international journals. The student will participate in at least one international scientific conference and take part in meetings organized within the framework of national and international collaborations.

National and international collaborations

This thesis will benefit of the collaboration with Gabriel Costa Monteiro Moreira, researcher at INRAE (BREED - Jouy-en-Josas) expect in epigenetics of adaptation in ruminants, Frederique Pitel researcher at INRAE (GENPHYSE, Toulouse) expert in avian epigenetics, and of Sandrine Lagarrigue, professor of genetics & genomics at l'Institut Agro (Rennes) expert in transcriptomics in chickens. It will also benefit of the expertise of Florence Jaffrezic in statistical approaches for data integration. 

The project is embedded in the international research consortium built in the framework of the “GEroNIMO” and Integrate Ulysses projects with established collaborations with the University of Wageningen, Uppsala University, University College Dublin, and Maynooth University. The PhD student will have opportunities to interact with these collaborators and may have the possibility of spending a few months in a partner laboratory. 

References

Brugaletta G. et al., (2022) A review of heat stress in chickens. Part I: Insights into physiology and gut health. Front. Physiol. 13:934381. doi: 10.3389/fphys.2022.934381

Karami K. et al., (2025) Molecular responses of chicken embryos to maternal heat stress through DNA methylation and gene expression: a pilot study, Environmental Epigenetics, Volume 11, Issue 1, dvaf009. 

Pitel F., et al., (2022) Epigenetics as a mediator of genome x environment interactions. 26. World’s Poultry Congress (WPC), Aug 2022, Paris, France. pp.199-212. 

Majumdar, K., et al., (2024) Integrated differential analysis of multi‑omics data using a joint mixture model: idiffomix. BMC Bioinformatics Submitted. https://arxiv. org/abs/2412.17511

Tomilina, E., et al., (2025). Gaussian copula correlation network analysis with application to multi-omics data. https://arxiv. org/abs/2506.08586.

Zerjal, T., et al., (2013). Performance comparison of laying hens segregating for the frizzle gene under thermoneutral and high ambient temperatures. Poultry science, 92(6), 1474-1485.

 

No specific conditions

Training and skills

Profile and Required Skills

We are seeking a highly motivated candidate with a good background in (bio)statistics. The applicant should also have a solid understanding of the biology underlying adaptation to environmental stress. Good programming skills in R and experience in data analysis are required. A background in bioinformatics would be a plus. The candidate should demonstrate analytical and critical thinking skills, the ability to work both independently and collaboratively, and good communication skills both in French as in English. A strong interest in interdisciplinary research at the interface between biology and data science is essential.

 

How to apply

Candidate should apply by email (tatiana.zerjal@inrae.fr or marie.courbariaux@inrae.fr) and send:

• CV
• Cover Letter
• Marks (Master 1 and Master 2, as well as current rank)

Only one applicant will be presented at the Doctoral School competition.

Deadline for receipt of applications: 04 May 2026
ED Competition date: June 3, 4, or 5, 2026, at AgroParisTech, Palaiseau
ED Competition detail: 10min oral presentation, followed by 15min questions with the jury (10-14 members).

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