title: Maximum-likelihood inference of population size contractions from microsatellite data.
abstract: Understanding the demographic history of populations and species is a central issue in evolutionary biology and molecular ecology. In this work, we develop a maximum-likelihood method for the inference of past changes in population size from microsatellite allelic data. Our method is based on importance sampling of gene genealogies, extended for new mutation models, notably the generalized stepwise mutation model (GSM). Using simulations, we test its performance to detect and characterize past reductions in population size. First, we test the estimation precision and confidence intervals coverage properties under ideal conditions, then we compare the accuracy of the estimation with another available method (MSVAR) and we finally test its robustness to misspecification of the mutational model and population structure. We show that our method is very competitive compared with alternative ones. Moreover, our implementation of a GSM allows more accurate analysis of microsatellite data, as we show that the violations of a single step mutation assumption induce very high bias toward false contraction detection rates. However, our simulation tests also showed some limits, which most importantly are large computation times for strong disequilibrium scenarios and a strong influence of some form of unaccounted population structure. This inference method is available in the latest implementation of the MIGRAINE software package.
authors.affiliation: INRA, UMR 1062 CBGP (INRA-IRD-CIRAD-Montpellier Supagro), Montpellier, France Muséum National d'Histoire Naturelle, CNRS, UMR OSEB, Paris, France Institut de Biologie Computationnelle, Montpellier, France raphael.leblois@supagro.inra.fr.
authors.forename: Raphaël
authors.initials: R
authors.lastname: Leblois
authors.affiliation: INRA, UMR 1062 CBGP (INRA-IRD-CIRAD-Montpellier Supagro), Montpellier, France Institut de Biologie Computationnelle, Montpellier, France Université Montpellier 2, CNRS, UMR I3M, Montpellier, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Pudlo
authors.affiliation: Muséum National d'Histoire Naturelle, CNRS, UMR OSEB, Paris, France.
authors.forename: Joseph
authors.initials: J
authors.lastname: Néron
authors.affiliation: Muséum National d'Histoire Naturelle, CNRS, UMR OSEB, Paris, France INRIA Paris-Rocquencourt, BANG Team, Le Chesnay, France.
authors.forename: François
authors.initials: F
authors.lastname: Bertaux
authors.affiliation: INRA, UMR 1062 CBGP (INRA-IRD-CIRAD-Montpellier Supagro), Montpellier, France.
authors.forename: Champak
authors.initials: C
authors.lastname: Reddy Beeravolu
authors.affiliation: INRA, UMR 1062 CBGP (INRA-IRD-CIRAD-Montpellier Supagro), Montpellier, France Institut de Biologie Computationnelle, Montpellier, France.
authors.forename: Renaud
authors.initials: R
authors.lastname: Vitalis
authors.affiliation: Institut de Biologie Computationnelle, Montpellier, France Université Montpellier 2, CNRS, UMR ISEM, Montpellier, France.
authors.forename: François
authors.initials: F
authors.lastname: Rousset
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D016013:Likelihood Functions; D018895:Microsatellite Repeats; D008957:Models, Genetic; D009154:Mutation; D057079:Pongo / Q000235:genetics*; D011156:Population Density; D012984:Software
keywords: bottleneck; coalescent; demographic inference; importance sampling; maximum likelihood; microsatellites; mutation processes; population contraction; population structure