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Sélection bayésienne de variables pour données longitudinales avec effets différentiels dans le temps : application à l’amélioration génétique

Abstract : In agronomy, and more specifically in genetic breeding, high throughput genotyping has been widely used for more than 20 years to access increasingly rich and abundant genetic information. This has allowed the identification of positions along the genome involved in the variability of traits of interest. More recently, high throughput phenotyping methods have been developed. They give access to the monitoring of the evolution of several phenotypic traits over time. These longitudinal data allow a fine study of the dynamics of these traits, while identifying the environmental factors that influence their variability according to developmental stages.However, the analysis of such data raises several statistical challenges. This thesis proposes methodological developments in order to take into account the dependencies between observations and between variables, to select relevant genetic or environmental variables, or to estimate effects that evolve over time. The Bayesian framework is an elegant statistical formalism to address these different issues, especially through the construction of priors. We study and compare different priors to simultaneously infer and select fixed and/or random effects when they can be numerous. We consider different statistical modeling frameworks classically used for longitudinal data analysis. In particular, we focus on linear mixed models, varying coefficient models or signal regression.This work was motivated by various practical applications concerning the QTL detection, the temporal evolution of genetic architecture or the impact of climatic variations on phenotypic variability. Three datasets, from various agronomical contexts, are used to illustrate these new approaches.
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https://theses.hal.science/tel-03435094
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Submitted on : Thursday, November 18, 2021 - 3:28:35 PM
Last modification on : Friday, August 5, 2022 - 10:51:51 AM
Long-term archiving on: : Saturday, February 19, 2022 - 7:27:02 PM

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  • HAL Id : tel-03435094, version 1

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Benjamin Heuclin. Sélection bayésienne de variables pour données longitudinales avec effets différentiels dans le temps : application à l’amélioration génétique. Applications [stat.AP]. Université Montpellier, 2021. Français. ⟨NNT : 2021MONTS039⟩. ⟨tel-03435094⟩

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