[Choix de modèles par calcul bayésien approximé et forêts aléatoires : analyses basées sur le groupement de modèles pour inférer l’histoire génétique des populations Pygmées]
En biologie évolutive, les méthodes d’inférence fondées sur la simulation, comme le calcul bayésien approché (ABC), sont particulièrement adaptées pour traiter les modèles complexes. Pudlo et al. (2016) ont récemment développé une nouvelle approche basée sur les forêts aléatoires (RF) denommée ABC-RF. Nous présentons ici les résultats d’analyses basées sur la méthodologie ABC-RF pour inférer l’histoire des populations humaines pygmées d’Afrique centrale occidentale à partir d’un ensemble de données génétiques issues de marqueurs microsatellites. Une nouveauté notable de nos analyses statistiques concerne l’application des techniques ABC-RF pour choisir des groupes prédéfinis de modèles. Nous avons formalisé huit scénarios évolutifs complexes intégrant (ou non) trois événements majeurs : (i) l’existence d’une population pygmée ancestrale commune, (ii) la possibilité d’événements de mélange génétique / migration entre populations pygmées et non-pygmées, et (iii) la possibilité d’un changement de taille dans le passé de la population non pygmée. Nous montrons que notre approche de regroupement de scénarios permet de discerner avec une forte confiance les principaux événements évolutifs qui caractérise l’histoire populationelle d’intérêt. Le scénario sélectionné final corresponds à une origine commune de tous les groupes pygmées d’Afrique centrale occidentale, la population pygmée ancestrale ayant divergé, avec des mélanges génétiques asymétriques, d’une population non-pygmée en expansion démographique.
In evolutionary biology, simulation-based methods such as Approximate Bayesian Computation (ABC) are well adapted to make statistical inferences about complex models of natural population histories. Pudlo et al. (2016) recently developed a novel approach based on the Random Forests method (RF): the ABC-RF algorithm. Here we present the results of analyses based on ABC-RF to make inferences about the history of Pygmy human populations from Western Central Africa from a microsatellite genetic dataset. A noticeable novelty of the statistical analyses presented here is the application of ABC-RF methodology to make model choice on predefined groups of models. We formalized eight complex evolutionary scenarios which incorporate (or not) three major events: (i) whether there exists an ancestral common Pygmy population, (ii) the possibility of introgression/migration events between Pygmy and non-Pygmy populations, and (iii) the possibility of a change in size in the past in the non-Pygmy African population. We show that our grouping approach allows disentangling with strong confidence the main evolutionary events characterizing the population history of interest. The selected final scenario corresponds to a common origin of all Western Central African Pygmy groups, with the ancestral Pygmy population having diverged, with asymmetrical genetic introgression, from a demographically expanding non-Pygmy population.
Mot clés : calcul bayésien approché, biologie évolutive, variations génétiques, microsatellites, sélection de modèle(s), génétique des populations, forêts aléatoires
@article{JSFS_2018__159_3_167_0,
author = {Estoup, Arnaud and Raynal, Louis and Verdu, Paul and Marin, Jean-Michel},
title = {Model choice using {Approximate} {Bayesian} {Computation} and {Random} {Forests:} analyses based on model grouping to make inferences about the genetic history of {Pygmy} human populations},
journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
pages = {167--190},
publisher = {Soci\'et\'e fran\c{c}aise de statistique},
volume = {159},
number = {3},
year = {2018},
zbl = {1410.62177},
language = {en},
url = {http://www.numdam.org/item/JSFS_2018__159_3_167_0/}
}
TY - JOUR AU - Estoup, Arnaud AU - Raynal, Louis AU - Verdu, Paul AU - Marin, Jean-Michel TI - Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations JO - Journal de la société française de statistique PY - 2018 SP - 167 EP - 190 VL - 159 IS - 3 PB - Société française de statistique UR - http://www.numdam.org/item/JSFS_2018__159_3_167_0/ LA - en ID - JSFS_2018__159_3_167_0 ER -
%0 Journal Article %A Estoup, Arnaud %A Raynal, Louis %A Verdu, Paul %A Marin, Jean-Michel %T Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations %J Journal de la société française de statistique %D 2018 %P 167-190 %V 159 %N 3 %I Société française de statistique %U http://www.numdam.org/item/JSFS_2018__159_3_167_0/ %G en %F JSFS_2018__159_3_167_0
Estoup, Arnaud; Raynal, Louis; Verdu, Paul; Marin, Jean-Michel. Model choice using Approximate Bayesian Computation and Random Forests: analyses based on model grouping to make inferences about the genetic history of Pygmy human populations. Journal de la société française de statistique, Tome 159 (2018) no. 3, pp. 167-190. http://www.numdam.org/item/JSFS_2018__159_3_167_0/
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