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Statistical and evolutionary population genomics

Organizers: Miguel Navascués & Renaud Vitalis

Characterizing the biodiversity of organisms of agro-economic interest, tracing back the demographic history of their populations (population changes, dating of divergence and mixing episodes, etc.), identifying the loci under natural/artificial selection, etc.

All these aims of research require the development of analytical statistical methods to infer the demographic and adaptive history of populations from genetic polymorphisms. This group is thus interested in the development of innovative methods, particularly on:

  • Bayesian hierarchical models using Markov chains Monte Carlo (MCMC) to infer the demographic history of the populations or characterize their adaptation to local environmental conditions;
  • calculation of likelihood by importance sampling (IS) to characterize the dispersal in a continuous habitat;
  • models where the calculation of likelihood is replaced by approximate Bayesian calculation (ABC) to make inferences from complex demographies including numerous populations.

We mainly cover the following issues:

  • Extending the scope of the developed analysis methods to the new data obtained from sequencing technologies and high-throughput genotyping, particularly those used in our facilities such as sequencing of restriction site associated DNA (RAD),
  • Evaluating the information given by the populations haplotype structure, to better take into account of the genetic connection between molecular markers along the genome,
  • Developing new methods to identify loci under selection, that will make possible the characterization of the genetic architecture of phenotype characters and life history traits involved in the adaptation of organisms to their environment, and to better understand the dynamics of adaptation,
  • Producing user-friendly software (see list), that will facilitate the methodological transfer to geneticists and genomicists.

Selected publications:

  • Cornuet J.-M., Pudlo P., Veyssier K.J., Dehne-Garcia A., Gautier M., Leblois R., Marin J.-M. & Estoup A. (2014) DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics, 30:1187-1189 doi:10.1093/bioinformatics/btt763
  • Vitalis R., Gautier M., Dawson K.J. & Beaumont M.A. (2014) Detecting and measuring selection from gene frequency data. Genetics, 196:799-817 doi:10.1534/genetics.113.152991
  • Leblois R., Pudlo P., Néron J., Bertaux F., Reddy Beeravolu C., Vitalis R. & Rousset F. (2014) Maximum-likelihood inference of population size contractions from microsatellite data. Molecular Biology and Evolution, 31: 2805-2823. doi:10.1093/molbev/msu212
  • Navascués M., Legrand D., Campagne C., Cariou M.-L. & Depaulis F. (2014) Distinguishing migration from isolation using genes with intragenic recombination: detecting introgression in the Drosophila simulans species complex. BMC Evolutionary Biology 14:89 doi:10.1186/1471-2148-14-89