I am doing theoretical population genetics and my work is essentially focussed on the inference of demographic parameters from genetic data. Most of my work concerns allelic data such a microsatellites or SNPs.
I am especially interested in realistic demographic models such as isolation by distance models. In this context, I tested the precision and robustness of a Fst-based method for the inference of density and dispersal parameters in continuous populations under isolation by distance (Rousset 1997's regression method). I tested the influence of demographic, mutational and sampling factor and showed that the regression method is precise and robust allowing precise inferences of present and local demographic parameters (see Leblois et al. 2003, Leblois et al. 2004). I also tested the influence of local isolation by distance structure on the detection of past reduction in population sizes and habitat contraction (Leblois et al. 2006). For all those simulation tests and others, I developped a special simulation programme based on a generation-by-generation coalescent algorithm. The program is very flexible so that many different models of isolation by distance could be considered, with various heterogeneities in space and time of the demographic parameters. It is implemented in the software package IBDSim (Leblois et al. 2008).
Recently, we developped a maximum likelihood method for the inference of demographic parameters based on Importance Sampling (IS) algorithms developped by R. Griffiths and collaborators. This work is published in DeIrio et al. 2006, Rousset & Leblois 2007, Rousset & Leblois 2012 and the inference method is implemented in the software package Migraine. Migraine is designed to make inferences about populations sizes, migration rates and/or dispersal parameters under various models : a one- or two-dimensional Isolation by distance model, two populations with migrations, a single panmictic population and will soon be extended to other demographic models.
Importance Sampling algorithms showed good performances and fast computations compared to alternative approaches. However, up to day, we only considered allelic type data (e.g. Microsatellites/SNPS/Allozymes) and few demographic models. I am currently working on the inference of past changes in population size (Leblois, Pudlo, Vitalis & Rousset in Prep), and large part of my futur work will be developing/adapting new algorithms and implementing new demographic and mutational models in the software package Migraine. To this aim, Champak Reddy Beeravolu is currently working with me as a Postdoc on a ANR/INRA contract.
Beside isolation by distance models, I am also interested in using/adapting population genetic tools to the study of recently diverging species. This lead me to work on DNA Barcoding (Frezal & Leblois 2008, Austerlitz et al. 2009, David et al. 2011) and I am currently developping maximum likelihood inferences under Isolation with Migration models (IM models, Beeravolu, Rousset & Leblois In Prep). All those implementations will soon be published and availlable in Migraine.
Postdoc and PhD opportunities :
I have currently no open postdoc or PhD position but several national and international funding bodies (Marie Curie, Human Frontiers, EMBO, AXA, INRA) provide fellowships for young researchers who wish to conduct a postdoc in France. Funding for PhD positions can be obtained with the INRA. So, if you are interested in applying for a PhD or a postdoc position and if your research interests are broadly similar to mine, do not hesitate to contact me (raphael.leblois@supagro.inra.fr)
PRESENT POSITION :
Researcher (CR1) at the Center for Biology and Management of Populations, National Institute for Agronomical Research (CBGP - INRA), Montpellier, France
2006-2010 : Assistant Professor at the National Museum of Natural History of Paris (MNHN).
2005-2006 : PostDoc (CNRS grant) with Pr. Evelyne Heyer et Dr. Renaud Vitalis, Musée de l'Homme - MNHN, Paris, France.
2004-2005 : PostDoc (Lavoisier grant) with Pr. Montgomery Slatkin, University of California, Berkeley, United-States.
2000-2004 : PhD in Integrative Biology under the supervision of Dr. François Rousset and Dr. Arnaud Estoup. Montpellier Supagro – University of Montpellier II, France.
IBDSim (Leblois et al. 2008) is a computer package for the simulation of genotypic data under general isolation by distance models.
IBDSim can consider a large panel of subdivided population models representing discrete subpopulations as well as a large continuous population. Many dispersal distributions, with different tails, can be considered as well as various heterogeneities in space and time of the demographic parameters. For examples of various applications see Leblois et al. (2003), Leblois et al. (2004), Leblois et al. (2006), Rousset & Leblois (2007), Rousset & Leblois (2012).
The program runs on PC under Windows, Mac or Linux systems, and we provide the source code that can be easily compiled under any system using C++ ISO compiler.
Full distribution: (including documentation, sources, Windows executable) download IBDSimV2.0.zip
Documentation (Windows and Linux) : download IBDSimV2.0_UserGuide.pdf
Sources (ISO C++, ready to be compiled with g++) : download IBDSimV2.0_Sources.zip
See the documentation for all further information.
Old version (v1.4 ) full distribution: download IBDSimv1.4.zip
IBDSim © Raphael Leblois, François Rousset, Champak Reddy Beeravolu 2008-2012
IBDSim is freeware (i.e. you don't need to pay). It is free software covered by the CeCILL licence (GPL compatible), i.e. it can be used, copied, studied, modified, and redistributed in other free software (i.e. also covered by a GPL-compatible licence, with freely available source code, even if commercial software) provided the IBDSim source is acknowledged.
Migraine implements coalescent algorithms for maximum likelihood analysis of population genetic data. Currently, only a limited set of models are implemented, as described in DeIorio et al. (2005), Rousset and Leblois (2007) and Rousset and Leblois (2011): essentially a simple model of isolation by distance in one or two dimension, including the island model as a sub-case; a two population mith migration model and a modelof a single panmictic population. The data handled are allelic counts (e.g. microsatellite or allozyme data). We plan to add more models in the future.
UE GMBE33A : Data analysis in population genetics, December 2012
Genetic Data Analysis, March 2012
