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Murviel-lès-Montpellier, France

Cruaud A.,French National Institute for Agricultural Research | Gautier M.,French National Institute for Agricultural Research | Gautier M.,Institute Of Biologie Computationnelle | Galan M.,French National Institute for Agricultural Research | And 7 more authors.
Molecular Biology and Evolution | Year: 2014

Next-generation sequencing opened up new possibilities in phylogenetics; however, choosing an appropriate method of sample preparation remains challenging. Here, we demonstrate that restriction-site-associated DNA sequencing (RADseq) generates useful data for phylogenomics. Analysis of our RAD library using current bioinformatic and phylogenetic tools produced 400 more sites than our Sanger approach (2,262,825 nt/species), fully resolving relationships between 18 species of ground beetles (divergences up to 17 My). This suggests that RAD-seq is promising to infer phylogeny of eukaryotic species, though potential biases need to be evaluated and new methodologies developed to take full advantage of such data. © The Author 2014. Source


Rousset F.,Montpellier University | Rousset F.,Institute Of Biologie Computationnelle | Ferdy J.-B.,CNRS Biological Evolution and Diversity Laboratory
Ecography | Year: 2014

Spatial autocorrelation is a well-recognized concern for observational data in general, and more specifically for spatial data in ecology. Generalized linear mixed models (GLMMs) with spatially autocorrelated random effects are a potential general framework for handling these spatial correlations. However, as the result of statistical and practical issues, such GLMMs have been fitted through the undocumented use of procedures based on penalized quasi-likelihood approximations (PQL), and under restrictive models of spatial correlation. Alternatively, they are often neglected in favor of simpler but more questionable approaches. In this work we aim to provide practical and validated means of inference under spatial GLMMs, that overcome these limitations. For this purpose, a new software is developed to fit spatial GLMMs. We use it to assess the performance of likelihood ratio tests for fixed effects under spatial autocorrelation, based on Laplace or PQL approximations of the likelihood. Expectedly, the Laplace approximation performs generally slightly better, although a variant of PQL was better in the binary case. We show that a previous implementation of PQL methods in the R language, glmmPQL, is not appropriate for such applications. Finally, we illustrate the efficiency of a bootstrap procedure for correcting the small sample bias of the tests, which applies also to non-spatial models. © 2014 The Authors. Source


Rousset F.,Montpellier University | Rousset F.,Institute Of Biologie Computationnelle
Genetics | Year: 2013

A canon of population genetics concerns the properties of FST, a descriptor of spatial genetic structure. Interest for FST arose from Wright's early insights linking FST to dispersal parameters as well as to his concept of effective population size (e.g., Wright 1938, 1951). Although there is continued interest in this topic, FST also serves in other applications, such as detecting selected markers in natural populations (Beaumont and Nichols 1996) and more often in routine descriptive works. Remarkably, it is the latter use that seems to attract most discussion. Alternative descriptors have been proposed. Conversely, attempts have been made to draw biological inferences from FST properties that do not depend on biological processes. A reconsideration of its properties under biological scenarios underlines the weaknesses of such approaches. © 2013 by the Genetics Society of America. Source


Gautier M.,Montpellier SupAgro | Gautier M.,Institute Of Biologie Computationnelle
Molecular Ecology Resources | Year: 2014

The recent democratization of next-generation-sequencing-based approaches towards nonmodel species has made it cost-effective to produce large genotyping data sets for a wider range of species. However, when no detailed genome assembly is available, poor knowledge about the organization of the markers within the genome might hamper the optimal use of this abundant information. At the most basic level of genomic organization, the type of chromosome (autosomes, sex chromosomes, mitochondria or chloroplast in plants) may remain unknown for most markers which might be limiting or even misleading in some applications, particularly in population genetics. Conversely, the characterization of sex-linked markers allows molecular sexing of the individuals. In this study, we propose a Bayesian model-based classifier named detsex, to assign markers to their chromosome type and/or to perform sexing of individuals based on genotyping data. The performance of detsex is further evaluated by a comprehensive simulation study and by the analysis of real data sets from various origins (microsatellite and SNP data derived from genotyping assay designs and NGS experiments). Irrespective of the origin of the markers or the size of the data set, detsex was proved efficient (i) to identify the sex-linked markers, (ii) to perform molecular sexing of the individuals and (iii) to perform basic quality check of the genotyping data sets. The underlying structure of the model also allows to consider each of these potential applications either separately or jointly. © 2014 John Wiley & Sons Ltd. Source


Gautier M.,Montpellier SupAgro | Gautier M.,Institute Of Biologie Computationnelle | Vitalis R.,Montpellier SupAgro | Vitalis R.,Institute Of Biologie Computationnelle | Vitalis R.,Montpellier University
Molecular Biology and Evolution | Year: 2013

The recent development of high-throughput genotyping technologies has revolutionized the collection of data in a wide range of both model and nonmodel species. These data generally contain huge amounts of information about the demographic history of populations. In this study, we introduce a new method to estimate divergence times on a diffusion time scale from large single-nucleotide polymorphism (SNP) data sets, conditionally on a population history that is represented as a tree. We further assume that all the observed polymorphisms originate from the most ancestral (root) population; that is, we neglect mutations that occur after the split of the most ancestral population. This method relies on a hierarchical Bayesian model, based on Kimura's time-dependent diffusion approximation of genetic drift. We implemented a Metropolis-Hastings within Gibbs sampler to estimate the posterior distribution of the parameters of interest in this model, which we refer to as the Kimura model. Evaluating the Kimura model on simulated population histories, we found that it provides accurate estimates of divergence time. Assessing model fit using the deviance information criterion (DIC) proved efficient for retrieving the correct tree topology among a set of competing histories. We show that this procedure is robust to low-to-moderate gene flow, as well as to ascertainment bias, providing that the most distantly related populations are represented in the discovery panel. As an illustrative example, we finally analyzed published human data consisting in genotypes for 452,198 SNPs from individuals belonging to four populations worldwide. Our results suggest that the Kimura model may be helpful to characterize the demographic history of differentiated populations, using genome-wide allele frequency data. © 2012 The Author. Source

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