Abstracts (first author)

Talk 

How to use RADseq data to infer past demography in a multi-locus coalescent-based framework

Author(s): Trucchi E, Gratton P, Le Bohec C, Stenseth NC

Summary:

Next-generation sequencing (NGS) methods that allow for genome scan in multiple individuals are expected to trigger a new golden age in phylogeography, phylogenetics and population genetics likely merging these fields with ecological genomics. Neutral and adaptive processes are now likely to be addressed with the same toolkit of molecular markers. However, the implementation of whole-genome data in ecological adaptations studies is far advanced than in the investigation of neutral evolutionary dynamics. This is mainly due to the most common output data type of most NGS methods (e.g. unlinked SNPs) in contrast with the historical importance of using gene trees, mainly through coalescent-based analytical approaches, in phylogeography and phylogenetics. Nevertheless, solutions that can make the set of excellent statistical tools for DNA sequences analysis, employed in phylogeography and phylogenetics so far, available to deal with NGS data are of the utmost importance. Here we will show how RAD sequencing data can be used to address classical questions in phylogeography and we will also propose a novel method to use RADseq data to infer past demography in a coalescent-based framework. Two applications of the proposed approach will be shown: i) the analysis the past population history of a king penguin (Aptenodytes patagonicus) colony breeding on Crozet archipelago and ii) the study of the past evolutionary dynamics of two populations of African porcupines (Hystrix cristata and H. africaeaustralis).


Video


Contacts

Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
Fax: 00 351 217500028
email: mail@eseb2013.com

Address

XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon
Portugal

Website

Computational Biology & Population Genomics Group 
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