Genetic modeling of animal’s robustness using high-throughput numerical data

My name is Vincent Le and I'm a PhD student at INRAE Occitanie-Toulouse. After a Master 2 degree in "Applied mathematics, statistics, applied statistics and decision analysis" at the University of Caen, I wanted to continue my studies by doing a thesis and discover the world of research.

Tomorrow's breeding will be precision breeding, and tomorrow's animal must be robust. Measuring the robustness of each animal is therefore an essential prerequisite for its selection. However, few tools are available to carry out such measurements. One of my main missions is therefore to extract new robustness criteria and find the best indicator of animal robustness.

After doing my M2 internship at INRA on the "Description de Trajectoires de Poids Vif d'Ovins" (Description of Sheep Live Weight Trajectories), I really enjoyed working in the world of agriculture and genetics. What's more, having done my Masters in Maths-Info, my knowledge of modeling and statistics will be useful for the thesis.

• Starting date: 1st November 2019
• University : INP Toulouse
• PhD school: SEVAB
• Scientific field: Agronomy, applied maths
• Thesis management: Ingrid DAVID, GenPHyse, INRAE
• Thesis management: Ingrid DAVID, GenPHyse, INRAE
• Funding: INRA – Alliance R&D
• #DigitAg : Labeled PhD – Axe 6 – Challenge 4

Keywords: Robustness, genetics, modeling

Abstract: Robustness of an animal corresponds to its ability to maintain performances in different environments. A robust animal is thus less sensitive to environmental variations which is important in the context of climate change and for animal welfare. Thanks to the development of electronic identification and electronic devices in farm, it becomes possible to record individually and repeatedly over time numerous phenotypes. The analysis of such longitudinal data offers the possibility to measure robustness of animals and its 2 components: resistance and resilience. Different methods have been proposed in the literature to extract robustness criteria from such analysis but have never been used in the context and under the constraints of genetic studies. Our aim is to test such approaches in the context of genetic selection : 1/ apply the different modeling on a large dataset in order to estimate heritability of the different criteria extracted from the different types of analysis 2/ estimate the genetic correlation with traits under selection in order to propose new criteria for genetic selection of robustness. Daily feed intake records from 7500 large white pigs will be used to test the different methods. This dataset, provided by the French pig industry, meets the constraints that the modeling will have to face with to be useful for genetic selection: large dataset, field data, heterogeneous, numerous and unknown environmental challenges.

Jury compound:

Sandrine Mignon-Grasteau, INRAE Tours, rapporteure

Rafael Munoz-Tamayo, INRAE Paris, rapporteur

Tristan Mary-Huard, AgroParisTech, examinateur

Catherine Larzul, INRAE Toulouse, examinatrice

Florence Ytournel, Choice Genetics, examinatrice

Ingrid David, INRAE Toulouse, directrice de thèse