Abstracts (first author)

Talk 

Footprints of directional selection in wild populations of Atlantic salmon: evidence for parasite-driven evolution?

Author(s): Zueva KJ, Lumme J, Veselov AE, Kent MP, Lien S, Primmer CR

Summary:

European populations of Atlantic salmon (Salmo salar) exhibit natural variance in susceptibility levels to the ectoparasite Gyrodactylus salaris, ranging from resistance to extreme susceptibility, and thus are a good model for studying the evolution of virulence and resistance. Advances in genome technologies provide new opportunities for obtaining a genome-scale view of the action of natural selection in wild populations. However, distinguishing the molecular signatures of genetic drift and environment-associated selection may challenge the search for specific pathogen driven selection. We used a novel genome-scan analysis approach aimed at i) identifying signals of selection in salmon populations affected by genetic drift at varying levels; and ii) separating the potentially selected loci identified into those affected by pathogen (G. salaris)-driven selection and salinity-driven selection. 4631 single nucleotide polymorphisms (SNPs) were screened in 472 salmon individuals from 12 different north European salmon populations. We identified several genomic regions potentially affected exclusively by parasite-driven selection, as well as several regions affected by salinity mediated directional selection. Functional annotation of candidate SNPs supported the participation of the detected genomic regions in immune defense and osmoregulation. These results provide new insights in genetic basis of pathogen susceptibility/resistance and adaptation to various salinity levels in Atlantic salmon, and open possibilities for specific candidate gene search.


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Contacts

Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
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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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