Dans cet article, nous présentons plusieurs familles de modèles hiérarchiques bayésiens dédiés à l’analyse de la structure génétique des populations à partir de génotypes multi-locus. L’analyse bayésienne de la structure génétique résout des problèmes de classification non supervisée à partir de données catégorielles. L’une des spécificités des modèles de la génétique des populations vient du fait que le génome d’un individu peut provenir de plusieurs groupes génétiques en raison du métissage. L’originalité des modèles présentés réside dans l’utilisation d’un contexte bayésien hiérarchique qui permet d’inclure, avec une couche de régression cachée, des covariables spatiales et environnementales pour modéliser le métissage. De plus, nous présentons différents critères de choix de modèles qui permettent de choisir le nombre de groupes génétiques ainsi que l’ensemble des covariables spatiales et environnementales. Une première application de ces modèles concerne la détection de la structure génétique des populations humaines et les relations entre structure génétique et classifications linguistiques pour les populations amérindiennes. Une deuxième application concerne l’estimation de la structure d’espèces de plantes et les prévision des modèles en fonction de différents scénarios de changement climatique.
In this study, we review Bayesian methods of inference of population genetic structure using multi-locus genotypic data sets. The Bayesian analysis of population genetic structure typically addresses unsupervised classification problems for categorical data. However, peculiarities of population genetic data sets arise from a process called genetic admixture, in which the genome of any individual can contain DNA from several groups of populations. A common feature of the methods presented here is the use of a hierarchical framework which allows their users to implement models of admixture based on hidden regressions of genetic clusters on geographic and ecological variables. In addition, we present techniques for choosing the number of clusters and for selecting informative subsets of ecological variables with respect to population structure. Then we survey applications of Bayesian methods to human and plant genetic data. For humans, we review previous works that examined relationships between genetic structure and languages in Native American populations using two distinct linguistic classifications. For plants, we estimate population genetic structure in an alpine species, and we provide an example of forecasting potential modifications in intra-specific genetic variation in response to global climatic change.
Keywords: population genetic structure, bayesian inference, ecological modeling
@article{JSFS_2011__152_3_3_0,
author = {Jay, Flora and GB Blum, Michael and Frichot, Eric and Fran\c{c}ois, Olivier},
title = {Mod\`eles \`a variables latentes en g\'en\'etique des populations},
journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
pages = {3--20},
publisher = {Soci\'et\'e fran\c{c}aise de statistique},
volume = {152},
number = {3},
year = {2011},
mrnumber = {2871174},
zbl = {1316.92049},
language = {fr},
url = {http://archive.numdam.org/item/JSFS_2011__152_3_3_0/}
}
TY - JOUR AU - Jay, Flora AU - GB Blum, Michael AU - Frichot, Eric AU - François, Olivier TI - Modèles à variables latentes en génétique des populations JO - Journal de la société française de statistique PY - 2011 SP - 3 EP - 20 VL - 152 IS - 3 PB - Société française de statistique UR - http://archive.numdam.org/item/JSFS_2011__152_3_3_0/ LA - fr ID - JSFS_2011__152_3_3_0 ER -
%0 Journal Article %A Jay, Flora %A GB Blum, Michael %A Frichot, Eric %A François, Olivier %T Modèles à variables latentes en génétique des populations %J Journal de la société française de statistique %D 2011 %P 3-20 %V 152 %N 3 %I Société française de statistique %U http://archive.numdam.org/item/JSFS_2011__152_3_3_0/ %G fr %F JSFS_2011__152_3_3_0
Jay, Flora; GB Blum, Michael; Frichot, Eric; François, Olivier. Modèles à variables latentes en génétique des populations. Journal de la société française de statistique, Tome 152 (2011) no. 3, pp. 3-20. http://archive.numdam.org/item/JSFS_2011__152_3_3_0/
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