[PhD’s Corner] Vincent Le: New genetic modeling of animal robustness using high throughput phenotyping data

Vincent Le is one of the #DigitAg labelled PhDs

New genetic modeling of animal robustness using high throughput phenotyping data

  • Start Date:  1st November 2019
  • University: INP Toulouse
  • PhD School: SEVAB
  • Field(s): Agronomy, Applied Mathematics
  • Doctoral Thesis Advisor: Ingrid DAVID, GenPHyse, INRA
  • Co-supervisors : Ingrid DAVID, GenPHyse, INRA
  • Funding: INRA – Alliance R&D
  • #DigitAg: Labelled PhD – Axis 6 – Challenge 4

Keywords: Robustness, genetic, modelisation

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 3180 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.

 

Contact:  giang-nam.le [AT] inrae.fr​, Cell: 06 43 79 29 45