Integration of pedigrees and methods for demographic inference in livestock population genomics

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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

Demographic inference is a central tool for reconstructing the genetic history of a population. Recently, several new approaches have brought this tool into a new era, that of the exhaustive use of whole-genome sequence. These approaches can be divided into two categories: demographic inference based on ancestral recombination graphs (ARGs¹) and deep learning based inference^2,3. However, without any other source of information, it is difficult to assess the actual degree of accuracy of these methods. In the case of livestock species, we usually have access to an additional information, rather unique and valuable, the pedigree over several generations. This set of relationships makes it possible to compare inferred demographic histories with the exact, albeit incomplete, history of the population.

In this thesis project, we aim to use the pedigree information on the one hand, to assess the results obtained with the new approaches of demographic inference, and, on the other hand, to refine methods for ARG estimation. Therefore, we propose a research program divided into three main tasks: (i) to appropriate new approaches (ARG, deep learning) in demographic inference on goat and sheep datasets, (ii) to compare these approaches together, in particular to evaluate their inference of the effective size of a population in the light of genealogical information, and (iii) to integrate this information into the inference of ARGs.

1. Kelleher, J. et al. Inferring whole-genome histories in large population datasets. Nature Genetics 51, 1330–1338 (2019).
2. Schrider, D. R. & Kern, A. D. Supervised Machine Learning for Population Genetics: A New Paradigm. Trends in Genetics 34, 301–312 (2018).
3. Korfmann, K., Gaggiotti, O. E. & Fumagalli, M. Deep learning in population genetics. Genome Biology and Evolution 15, evad008 (2023).

You will be welcomed in the CHAMADE team (“CHAracterization and MAnagement of Diversity”) of the GenPhySE research unit (https://genphyse.inrae.fr/), located in the Occitanie-Toulouse research centre (31320, Castanet-Tolosan). The CHAMADE team is part of the “Diversity and Selection” scientific division of the research unit. The team is interested in methodological issues in the field of population genomics, genetic evaluation of livestock species and quantitative and evolutionary genetics. On deep learning approaches for demographic inference, collaboration is also planned with the BioInfo team from the Laboratoire Interdisciplinaire des Sciences du Numérique (LISN, Paris-Saclay University).

Training and skills

Recommended training: Master’s degree in population genetics/genomics, or bioinformatics, or applied statistics

Knowledge required: Advanced knowledge of population genetics/genomics and minimal knowledge of biology and genetics

Appreciated experience: Master internship/dissertation (or equivalent experience) in population genomics (that is, experience with large genomic datasets, on population issues – e.g. structure, interbreeding, history, etc.)

Skills sought: Programming in Linux/Bash and at least one language such as Python, R or Matlab. Advanced skills in Python, particularly in the use of deep learning libraries (PyTorch, Tensorflow) are not required but would be highly appreciated.

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