Tristan Kistler's thesis defense

The defense will be in English.

It will take place on Tuesday, April 8, at 14:00 at the INRAE Center in Jouy-en-Josas, in the amphitheater of building 440.

The jury will be composed of :

• Gregor GORJANC, University of Edinburgh (United Kingdom), Reviewer
• Anna Kristina SONESSON, Research Director, Nofima (Norway), Reviewer
• Duur AANEN, Professor, Wageningen University (Netherlands), Examiner 
• Ingrid DAVID, Chargée de Recherche, INRAE (Université de Toulouse), Examiner 
• Christine DILLMANN, Professor, Université Paris-Saclay, Examiner 

Director of the thesis :
Florence PHOCAS, INRAE, Co-Director of the thesis 
Piter BIJLA, Wageningen University (The Netherlands), Co-Director of the thesis 

Abstract
Breeding plans in honeybees are emerging worldwide, aiming to improve traits such as production and resilience. However, beekeepers lack references and methods to optimize selection. Indeed, three bee specificities complicate the application of general breeding theory: haplo-diploidy, early and polyandrous queen mating, and colony-level trait expression. Considering these specificities, this thesis explores selection strategies, estimation of genetic parameters, and contributes a stochastic simulation tool of bee breeding schemes.
Regarding selection strategies, simulations showed that monoandry (queen fertilized by a single male) over 20 years of phenotypic selection favors gain on genetic effects expressed by worker groups over those expressed by queens and leads to similar or lower total genetic gain compared to polyandry, and 25–50% higher inbreeding. Mass selection yields 20% more genetic gain than within-family selection but increases inbreeding by 33%. 
Early dam selection based on estimated breeding values shortens generation intervals by a fourth and can increase genetic gain by up to 50%, depending on the breeding goal, despite impeding late-trait phenotyping on dam selection candidates.
Regarding the estimation of genetic parameters, the simulations show that when all drones mating a queen originate from a single drone-producing queen (DPQ), standard errors of genetic estimates decrease by 10% compared to when drones originate from three sister-DPQs. However, pedigree uncertainty on which of these two mating strategies is used can severely bias genetic estimates. DPQs are often open-mated because they don’t transmit the genetic material obtained from their mates to further generations of the breeding population. It is shown that correction of DPQ-colony phenotypes, by adding a non-genetic effect in the evaluation model for the drone subpopulations mating DPQs, avoids bias in genetic parameter estimates. 
Using data from a small breeding population in Southern France, genetic parameters for beekeeping traits, fecundity, and resilience traits were estimated. Despite uncertainty due to limited data, most traits, including those related to resilience, indicated potential for selection, while swarming drive and gentleness showed no heritability. 
This thesis work aims to guide bee breeders in the genetic improvement and inbreeding control of their stock. The simulation tool developed in this thesis is publicly available to explore breeding plans for honeybees further. 

Keywords : quantitative genetics, honeybees, stochastic simulation, animal breeding, genetic parameters