Phd position - Precision phenotyping for genetic improvement of disease resistance and relilience, with focus on mastitis in cattle - a modelling approach

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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

Summary of the thesis proposal:

This PhD proposes to develop new health traits that reflect individuals’ genetic capacity for resistance and resilience in the face of infection, with a view to improved genetic selection. The development of new health traits will be carried out using dynamic models applied to high-frequency health and performance time-series data. The central challenge of this project is to capture the dynamic and continuous nature of an individual’s health status relative to disease events, and accounting for the recurrence of such events. With a strong genetic focus, this thesis will offer new perspectives on selection to facilitate the transition toward more sustainable and agroecological livestock production. 

This PhD position will be based at the Animal Genetics and Integrative Biology Unit (GABI - https://gabi.jouy.hub.inrae.fr/), within the Genetics for the Sustainability of Cattle Farming (GBOS) team, at the INRAE site in Jouy-en-Josas (78). It will be carried out in close collaboration with the units: Systemic Modeling Applied to Ruminants (MoSAR - https://mosar.versailles-
saclay.hub.inrae.fr/), and Physiology, Environment, and Genetics for Animals and Livestock Systems (PEGASE - https://pegase.rennes.hub.inrae.fr/).  

Context and Research Questions :

Health data from farm animals is generally viewed and recorded in binary terms: the animal is either healthy or sick with respect to a given condition at a given time. This approach to phenotyping health status leaves no room for nuance, and it seems likely that a more detailed description of the variability in animals’ responses would be useful for genetic selection of disease resistance. In recent years, the deployment of sensors and high-throughput technologies in a growing number of farms has provided access to time-series of numerous automated measurements at the animal level (Delaval OCC, Herd Navigator, O-CMT, etc.) (Sørensen et al., 2016; Deng et al., 2020). These data open up the possibility of tracking the evolution of health indicators over time. Better integration of the dynamic dimension of health status into a quantitative measure would constitute a real advance in phenotyping, but also presents a methodological challenge regarding the extraction of biologically relevant indicators from such longitudinal measurements. 
A few studies have already begun to explore this question (e.g., Detilleux et al., 2006), and recently, a model representing the degree of infection as a continuous trait during a health disturbance and incorporating an animal-specific genetic component was proposed by the researchers supervising this thesis. The model describes the change over time in the degree of infection (DOI) using three coefficients that account for individual variations among animals in terms of their ability to clear the infection, i.e., their resistance. Furthermore, by incorporating changes in performance over time (e.g., milk production), the model also describes the effect of infection on performance, thereby allowing for the estimation of individual variation in resilience to infection. 

The proposed modelling approach is based on biological principles similar to those used by Detilleux and colleagues. However, instead of applying them in an epidemiological context (modelling disease transmission within a herd), it is here designed to enable the extraction of genetic parameters related to the response to the disease. To our knowledge, this is the first model of its kind intended for use in a genetic context. As part of a proof-of-concept study, the preliminary model was evaluated using 15 individual time series of somatic cell counts in milk associated with episodes of mastitis, and the results are promising. Following this initial step, however, the model still needs to be adapted for use on large datasets and its performance evaluated across a wider range of farming systems and data sources. Furthermore, the model’s structure is designed to facilitate the estimation of animal parameters using statistical tools for dynamic modelling. This extension would provide the appropriate statistical framework for addressing measurement uncertainty, an aspect that has not yet been addressed. Another key aspect to evaluate concerns the issue of repeated infections during the same lactation. Should they be considered as related but genetically distinct, or as repeated measurements of the same underlying trait? 

In this PhD, you will conduct research at the intersection of genetics and modelling to address the following questions: 

-    Do phenotypes derived from mechanistic models of disease resistance or resilience provide a level of precision that is both meaningful and practical for health-based selection compared to current traits?
-    Are genetic differences in resistance and resilience observed when comparing different pathogens, or between repeated infections.

To address such questions you will use the pseudo-phenotypes derived from the model, whose underlying principles are described in the article “Phenotyping health status on a continuous scale using a degree of infection approach: a case study using mastitis” (Martin et al., under review). You will be required to adapt this model to make it better suited for large-scale data, and will also need to develop new modules to account for the recurrence of events experienced by the animals. Finally, you will use classical methods of quantitative genetics (estimation of genetic parameters, GWAS and post-GWAS analyses) to test the suitability of your pseudo-phenotypes for use in breeding. Depending on the progress of the thesis, a pilot evaluation of the selected traits may also be conducted. 
Various types of data will be made available to you: 

-    data from the Le Pin experimental unit (https://uep.isc.inrae.fr/), which contains several thousand lactations with biweekly somatic cell counts, including approximately 200 lactations in which the pathogens causing mastitis have been identified. This data continues to grow as part of the ongoing GLOBAL experiment, with cell counts now performed three times a week and pathogen identification now conducted systematically. This new experiment also provides extensive knowledge about the animals, particularly regarding numerous blood parameters related to immunity.
-    large-scale data from commercial farms equipped with milking robots and provided by the French dairy industry. International data could also be shared as part of this thesis.

 

 

Références  :

Deng, Z., Hogeveen, H., Lam, T.J.G.M., van der Tol, R., Koop, G., 2020. Performance of Online Somatic Cell Count Estimation in Automatic Milking Systems. Frontiers in Veterinary Science 7. doi:10.3389/fvets.2020.00221 

Detilleux, J., Vangroenweghe, F., Burvenich, C., 2006. Mathematical model of the acute inflammatory response to Escherichia coli in intramammary challenge. Journal of Dairy Science 89, 3455–3465. doi:10.3168/jds.S0022-0302(06)72383-9 

Martin P., Foucras G., Muñoz-Tamayo R., Friggens N.C.. Phenotyping health status on a continuous scale using a degree of infection approach: a case study using mastitis, Animal, under review. 

Martin P., Foucras G., Muñoz-Tamayo R., Friggens N.C. (2024). Exploring modelling approaches to address the dynamic nature of animal health. Presented at: 75. Annual Meeting of the European Federation of Animal Science (EAAP), Florence, Italy (2024-09-01 - 2024-09-04). 

Sørensen, L.P., Bjerring, M., Løvendahl, P., 2016. Monitoring individual cow udder health in automated milking systems using online somatic cell counts. Journal of Dairy Science 99, 608–620. doi:10.3168/jds.2014-8823

 
West, M., Harrison, J., 1997. Bayesian Forecasting and Dynamic Models, Springer. ed, Springer Series in Statistics. New York, USA. 

 

 

 

No specific conditions

Training and skills

A Master’s degree or equivalent (5 years of higher education) is required. A background in animal science, genetics, or mathematical modelling is strongly recommended.  

Highly motivated by science, you should demonstrate independence, curiosity, rigor, and a genuine sense of initiative. Fluency in English is required; knowledge of French is optional.   

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