Reliable ABC model choice via random forests.

Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.

Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms.

DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data.

Motivation: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods.

The effect of RAD allele dropout on the estimation of genetic variation within and between populations.

Inexpensive short-read sequencing technologies applied to reduced representation genomes is revolutionizing genetic research, especially population genetics analysis, by allowing the genotyping of massive numbers of single-nucleotide polymorphisms (SNP) for large numbers of individuals and populations. Restriction site-associated DNA (RAD) sequencing is a recent technique based on the characterization of genomic regions flanking restriction sites. One of its potential drawbacks is the presence of polymorphism within the restriction site, which makes it impossible to observe the associated SNP allele (i.

Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics.

Comparison of demo-genetic models using Approximate Bayesian Computation (ABC) is an active research field. Although large numbers of populations and models (i.e.

Test of colonisation scenarios reveals complex invasion history of the red tomato spider mite Tetranychus evansi.

The spider mite Tetranychus evansi is an emerging pest of solanaceous crops worldwide. Like many other emerging pests, its small size, confusing taxonomy, complex history of associations with humans, and propensity to start new populations from small inocula, make the study of its invasion biology difficult. Here, we use recent developments in Approximate Bayesian Computation (ABC) and variation in multi-locus genetic markers to reconstruct the complex historical demography of this cryptic invasive pest.

Lack of confidence in approximate Bayesian computation model choice.

Approximate Bayesian computation (ABC) have become an essential tool for the analysis of complex stochastic models. Grelaud et al. [(2009) Bayesian Anal 3:427-442] advocated the use of ABC for model choice in the specific case of Gibbs random fields, relying on an intermodel sufficiency property to show that the approximation was legitimate.

Genetic inferences about the population dynamics of codling moth females at a local scale.

Estimation of demographic parameters is important for understanding the functioning of natural populations and the underlying ecological and evolutionary processes that may impact their dynamics. Here, we used sibship assignment methods to shed light on the local dynamics of codling moth females in eight orchards in a 90-ha domain near Valence, France. Based on full-sib inference among 1,063 genotyped moths, we estimated (1) the effective number of females that had offspring, (2) their fertility and (3) the distribution of their oviposition sites within and among orchards.

Combining genetic, historical and geographical data to reconstruct the dynamics of bioinvasions: application to the cane toad Bufo marinus.

We developed a spatially explicit model of a bioinvasion and used an approximate Bayesian computation (ABC) framework to make various inferences from a combination of genetic (microsatellite genotypes), historical (first observation dates) and geographical (spatial coordinates of introduction and sampled sites) information. Our method aims to discriminate between alternative introduction scenarios and to estimate posterior densities of demographically relevant parameters of the invasive process. The performance of our landscape-ABC method is assessed using simulated data sets differing in their information content (genetic and/or historical data).

Meiotic recombination dramatically decreased in thelytokous queens of the little fire ant and their sexually produced workers.

The little fire ant, Wasmannia auropunctata, displays a peculiar breeding system polymorphism. Classical haplo-diploid sexual reproduction between reproductive individuals occurs in some populations, whereas, in others, queens and males reproduce clonally. Workers are produced sexually and are sterile in both clonal and sexual populations.

Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0).

Background: Approximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms increasingly used to make model-based inference on complex evolutionary scenarios that have acted on natural populations. The software DIYABC offers a user-friendly interface allowing non-expert users to consider population histories involving any combination of population divergences, admixtures and population size changes. We here describe and illustrate new developments of this software that mainly include (i) inference from DNA sequence data in addition or separately to microsatellite data, (ii) the possibility to analyze five categories of loci considering balanced or non balanced sex ratios: autosomal diploid, autosomal haploid, X-linked, Y-linked and mitochondrial, and (iii) the possibility to perform model checking computation to assess the "goodness-of-fit" of a model, a feature of ABC analysis that has been so far neglected.

Bridgehead effect in the worldwide invasion of the biocontrol harlequin ladybird.

Recent studies of the routes of worldwide introductions of alien organisms suggest that many widespread invasions could have stemmed not from the native range, but from a particularly successful invasive population, which serves as the source of colonists for remote new territories. We call here this phenomenon the invasive bridgehead effect. Evaluating the likelihood of such a scenario is heuristically challenging.

In defence of model-based inference in phylogeography.

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference.

Inferring introduction routes of invasive species using approximate Bayesian computation on microsatellite data.

Determining the routes of introduction provides not only information about the history of an invasion process, but also information about the origin and construction of the genetic composition of the invading population. It remains difficult, however, to infer introduction routes from molecular data because of a lack of appropriate methods. We evaluate here the use of an approximate Bayesian computation (ABC) method for estimating the probabilities of introduction routes of invasive populations based on microsatellite data.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation.

Unlabelled: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples.

Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history.

Fermented beverages and foods have played a significant role in most societies worldwide for millennia. To better understand how the yeast species Saccharomyces cerevisiae, the main fermenting agent, evolved along this historical and expansion process, we analysed the genetic diversity among 651 strains from 56 different geographical origins, worldwide. Their genotyping at 12 microsatellite loci revealed 575 distinct genotypes organized in subgroups of yeast types, i.

A third-generation microsatellite-based linkage map of the honey bee, Apis mellifera, and its comparison with the sequence-based physical map.

Background: The honey bee is a key model for social behavior and this feature led to the selection of the species for genome sequencing. A genetic map is a necessary companion to the sequence. In addition, because there was originally no physical map for the honey bee genome project, a meiotic map was the only resource for organizing the sequence assembly on the chromosomes.

The genome of Apis mellifera: dialog between linkage mapping and sequence assembly.

Two independent genome projects for the honey bee, a microsatellite linkage map and a genome sequence assembly, interactively produced an almost complete organization of the euchromatic genome. Assembly 4.0 now includes 626 scaffolds that were ordered and oriented into chromosomes according to the framework provided by the third-generation linkage map (AmelMap3).

A note on the accuracy of PAC-likelihood inference with microsatellite data.

Stephens and Donnelly have introduced a simple yet powerful importance sampling scheme for computing the likelihood in population genetic models. Fundamental to the method is an approximation to the conditional probability of the allelic type of an additional gene, given those currently in the sample. As noted by Li and Stephens, the product of these conditional probabilities for a sequence of draws that gives the frequency of allelic types in a sample is an approximation to the likelihood, and can be used directly in inference.

Inference on microsatellite mutation processes in the invasive mite, Varroa destructor, using reversible jump Markov chain Monte Carlo.

Varroa destructor is a parasitic mite of the Eastern honeybee Apis cerana. Fifty years ago, two distinct evolutionary lineages (Korean and Japanese) invaded the Western honeybee Apis mellifera. This haplo-diploid parasite species reproduces mainly through brother-sister matings, a system which largely favors the fixation of new mutations.

Assortative mating in sympatric host races of the European corn borer.

Although a growing body of work supports the plausibility of sympatric speciation in animals, the practical difficulties of directly quantifying reproductive isolation between diverging taxa remain an obstacle to analyzing this process. We used a combination of genetic and biogeochemical markers to produce a direct field estimate of assortative mating in phytophagous insect populations. We show that individuals of the same insect species, the European corn borer Ostrinia nubilalis, that develop on different host plants can display almost absolute reproductive isolation-the proportion of assortative mating was >95%-even in the absence of temporal or spatial isolation.

The invasive Korea and Japan types of Varroa destructor, ectoparasitic mites of the Western honeybee (Apis mellifera), are two partly isolated clones.

Varroa destructor, now a major pest of the Western honeybee, Apis mellifera, switched from its original host, the Eastern honeybee, A. cerana, ca. 50 years ago.

Bayesian analysis of an admixture model with mutations and arbitrarily linked markers.

We introduce here a Bayesian analysis of a classical admixture model in which all parameters are simultaneously estimated. Our approach follows the approximate Bayesian computation (ABC) framework, relying on massive simulations and a rejection-regression algorithm. Although computationally intensive, this approach can easily deal with complex mutation models and partially linked loci, and it can be thoroughly validated without much additional computation cost.

Genetic analysis of complex demographic scenarios: spatially expanding populations of the cane toad, Bufo marinus.

Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960.

GENECLASS2: a software for genetic assignment and first-generation migrant detection.

GENECLASS2 is a software that computes various genetic assignment criteria to assign or exclude reference populations as the origin of diploid or haploid individuals, as well as of groups of individuals, on the basis of multilocus genotype data. In addition to traditional assignment aims, the program allows the specific task of first-generation migrant detection. It includes several Monte Carlo resampling algorithms that compute for each individual its probability of belonging to each reference population or to be a resident (i.

Analytical bayesian approach for assigning individuals to populations.

We propose a general formulation of the Bayesian method for assigning individuals to a population among a predetermined set of reference populations using molecular marker information. Compared to previously published methods, ours allows us to consider different types of prior information about allele frequencies by using a Dirichlet prior probability distribution. It also makes it possible to assign a set of individuals assumed to belong to the same population with increased accuracy using their pooled genotype data.