Title :

Inference with selection, varying population size and evolving population structure: Application of ABC to a forward-backward coalescent process with interactions

Author(s) :

Lepers, Clotilde [Auteur]
Infection, Anti-microbiens, Modélisation, Evolution [IAME (UMR_S_1137 / U1137)]
Billiard, Sylvain [Auteur]
Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 [Evo-Eco-Paléo (EEP)]
Porte, Matthieu [Auteur]
Institut National de l'Information Géographique et Forestière [IGN] [IGN]
Méléard, Sylvie [Auteur]
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Tran, Chi [Auteur]
Laboratoire d'Analyse et de Mathématiques Appliquées [LAMA]

Journal title :

Heredity

Pages :

335–350

Publisher :

Nature Publishing Group

Publication date :

2021

ISSN :

0018-067X

English keyword(s) :

Phylogenies
nested coalescent processes
genealogical inference
ABC
Mutation-selection
competition and interactions
neutral diversity
adaptive dynamics
multiple merges
neutrality tests

HAL domain(s) :

Statistiques [stat]/Applications [stat.AP]
Sciences de l'environnement/Biodiversité et Ecologie
Sciences du Vivant [q-bio]/Génétique/Génétique des populations [q-bio.PE]
Mathématiques [math]/Probabilités [math.PR]

English abstract : [en]

Genetic data are often used to infer demographic history and changes or detect genes under selection. Inferential methods are commonly based on models making various strong assumptions: demography and population structures ...
Show more >Genetic data are often used to infer demographic history and changes or detect genes under selection. Inferential methods are commonly based on models making various strong assumptions: demography and population structures are supposed \textit{a priori} known, the evolution of the genetic composition of a population does not affect demography nor population structure, and there is no selection nor interaction between and within genetic strains. In this paper, we present a stochastic birth-death model with competitive interactions and asexual reproduction. We develop an inferential procedure for ecological, demographic and genetic parameters. We first show how genetic diversity and genealogies are related to birth and death rates, and to how individuals compete within and between strains. {This leads us to propose an original model of phylogenies, with trait structure and interactions, that allows multiple merging}. Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data. We apply our procedure to simulated data from a toy model, and to real data by analyzing the genetic diversity of microsatellites on Y-chromosomes sampled from Central Asia human populations in order to test whether different social organizations show significantly different fertility.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Centre Européen pour les Mathématiques, la Physique et leurs Interactions
Models and algorithms: from the discrete to the continuous

Collections :

• Évolution, Écologie et Paléontologie (Evo Eco Paleo) - UMR 8198

Source :

Harvested from HAL