Bayesian varying coefficient model with selection: An application to functional mapping

Heuclin Benjamin, Mortier Frédéric, Trottier Catherine, Denis Marie. 2021. Bayesian varying coefficient model with selection: An application to functional mapping. Journal of the Royal Statistical Society. Series C, Applied Statistics, 70 (1) : 24-50.

https://doi.org/10.1111/rssc.12447

Article de revue ; Article de recherche ; Article de revue à facteur d'impact

Prévisualisation

Version post-print - Anglais
Utilisation soumise à autorisation de l'auteur ou du Cirad.
Bayesian_varying_coefficient_model_with_selection__An_ap__plication_to_functional_mapping.pdf
Télécharger (545kB) | Prévisualisation

Version Online first - Anglais
Accès réservé aux personnels Cirad
Utilisation soumise à autorisation de l'auteur ou du Cirad.
597342.pdf
Télécharger (872kB) | Demander une copie

Version publiée - Anglais
Accès réservé aux personnels Cirad
Utilisation soumise à autorisation de l'auteur ou du Cirad.
597342-ed.pdf
Télécharger (874kB) | Demander une copie

Quartile : Q2, Sujet : STATISTICS & PROBABILITY

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Economie-gestion

Résumé : How does the genetic architecture of quantitative traits evolve over time? Answering this question is crucial for many applied fields such as human genetics and plant or animal breeding. In the last decades, high‐throughput genome techniques have been used to better understand links between genetic information and quantitative traits. Recently, high‐throughput phenotyping methods are also being used to provide huge information at a phenotypic scale. In particular, these methods allow traits to be measured over time, and this, for a large number of individuals. Combining both information might provide evidence on how genetic architecture evolves over time. However, such data raise new statistical challenges related to, among others, high dimensionality, time dependencies, time varying effects. In this work, we propose a Bayesian varying coefficient model allowing, in a single step, the identification of genetic markers involved in the variability of phenotypic traits and the estimation of their dynamic effects. We evaluate the use of spike‐and‐slab priors for the variable selection with either P‐spline interpolation or non‐functional techniques to model the dynamic effects. Numerical results are shown on simulations and on a functional mapping study performed on an Arabidopsis thaliana (L. Heynh) data which motivated these developments.

Mots-clés Agrovoc : Arabidopsis thaliana, théorie Bayésienne, méthode statistique, variation phénotypique, locus des caractères quantitatifs, marqueur génétique, phytogénétique

Mots-clés complémentaires : Modélisation Bayésienne, cartographie fonctionnelle

Mots-clés libres : Functional mapping., Group Spike-and- Slab;, P-Splines, Time Varying Parameters, Varying coefficient models

Classification Agris : U10 - Informatique, mathématiques et statistiques
F30 - Génétique et amélioration des plantes

Champ stratégique Cirad : CTS 1 (2019-) - Biodiversité

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) Adaptive BREEDING for productive, sustainable and resilient FORESTs under climate change

Editeurs scientifiques et affiliations