PhD defense of Clément GAIN from TIMC MAGe on wednesday, the 10th of may, at 2pm:

" Population genetic offset in the face of climate change. "
 

Jury

• OLIVIER FRANÇOIS, Professeur des Universités, Grenoble INP, Supervisor
• LAURENCE DESPRES, Professeure des Universités, Université Grenoble Alpes, Examiner
• MATHIEU  GAUTIER, Directeur de recherche, INRAE Centre Occitanie-Montpellier, Reporter
• YVAN  SCOTTI, Directeur de recherche, INRAE Centre Provence-Alpes-Côte d'Azur, Reperter
• SIMON BOITARD, Chargé de recherche HDR, INRAE Centre Occitanie-Montpellier, Examiner

Keywords

Machine learning ; Latent variables ; Landscape genomics ; Climate change

Abstract

Global climate change is altering habitats at an unprecedented rates. These environmental changes are having a significant impact on biodiversity and there is a growing interest in understanding the response of populations to these changes. This PhD focuses on using intraspecific genomic data to inform the prediction of these responses. More specifically, we contribute to the understanding and improvement of the concept of genetic offset. Genetic offset aims at quantifying genetic population maladaptation. Genetic maladaptation occurs when the genetic composition of a population does not match that required for the habitat in which it evolves. We focus our work on different axes. First, we will present a new measure of genetic offset, called genetic gap, aiming at solving some limitations of existing methods, such as taking into account confounding factors of genetic data and the polygenic aspect of adaptation. We will also establish a theoretical framework allowing the establishment of a relationship between the genetic gap and the fitness value of an individual in a modified environment. More specifically, we will show that the genetic gap is proportional to the logarithm of the fitness value in the modified environment. We will validate this theoretical result on simulated data using SLiM software, and on real data using a common garden experiment for pearl millet (Pennisetum glaucum) populations. In parallel to this work on genetic offset, we will establish a theoretical relationship between principal component analysis (PCA) and Wright's fixation index, two essential approaches in understanding the population structure existing in sampled individuals. This relationship tells us that in a model with K discrete populations, the average value of Fst along the genome is approximated by the (K-1) largest eigenvalues of the scaled PCA. Our PhD thus contributes to a better interpretation of the genetic offset by setting up a theoretical framework around this notion and also facilitates its use by implementing a function for computing the genetic gap in the R library LEA 3. It also contributes to justify the use of PCA to describe the genetic structure of populations by specifying the link between this method and Wright's fixation index.