Biology subjects

Leblois, R.

Publications and source records attributed to Leblois, R..

3 recordsLinked to original sources

bioRxiv2018

Measuring genetic differentiation from Pool-seq data

The recent advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many non-model species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of FST for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased, and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Last, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased FST estimator may question the interpretation of population structure inferred from previous analyses.

evolutionary biology↗

bioRxiv2017

Likelihood analysis of population genetic data under coalescent models: computational and inferential aspects

Likelihood methods are being developed for inference of migration rates and past demographic changes from population genetic data. We survey an approach for such inference using sequential importance sampling techniques derived from coalescent and diffusion theory. The consistent application and assessment of this approach has required the re-implementation of methods often considered in the context of computer experiments methods, in particular of Kriging which is used as a smoothing technique to infer a likelihood surface from likelihoods estimated in various parameter points, as well as reconsideration of methods for sampling the parameter space appropriately for such inference. We illustrate the performance and application of the whole tool chain on simulated and actual data, and highlight desirable developments in terms of data types and biological scenarios.\n\nResumeDiverses approches ont ete developpees pour linference des taux de migration et des changements demo-graphiques passes a partir de la variation genetique des populations. Nous decrivons une de ces approches utilisant des techniques dechantillonnage pondere sequentiel, fondees sur la modelisation par approches de coalescence et de diffusion de levolution de ces polymorphismes. Lapplication et levaluation systematique de cette approche ont requis la re-implementation de methodes souvent considerees pour lanalyse de fonctions simulees, en particulier le krigeage, ici utilise pour inferer une surface de vraisemblance a partir de vraisemblances estimees en differents points de lespace des parametres, ainsi que des techniques dechantillonage de ces points. Nous illustrons la performance et lapplication de cette serie de methodes sur donnees simulees et reelles, et indiquons les ameliorations souhaitables en termes de types de donnees et de scenarios biologiques.\n\nMots-cleshistoire demographique, processus de coalescence, importance sampling, genetic polymorphism\n\nAMS 2000 subject classifications92D10, 62M05, 65C05

evolutionary biology↗

bioRxiv2017

Demographic inference through approximate-Bayesian-computation skyline plots

The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

evolutionary biology↗