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dc.contributor.authorJacobs, Arne
dc.contributor.authorDe Noia, M
dc.contributor.authorPræbel, Kim
dc.contributor.authorKanstad-Hanssen, Øyvind
dc.contributor.authorPaterno, M
dc.contributor.authorJackson, D
dc.contributor.authorMcGinnity, P
dc.contributor.authorSturm, Armin
dc.contributor.authorElmer, K. R.
dc.contributor.authorLlewellyn, Martin
dc.date.accessioned2018-09-04T11:55:08Z
dc.date.available2018-09-04T11:55:08Z
dc.date.issued2018-01-19
dc.description.abstractCaligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic <i>Lepeophtheirus salmonis</i>, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among four <i>L. salmonis</i> populations from Ireland, Scotland and Northern Norway. Only weak global structure was observed based on the full SNP dataset. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment, increased global genetic structure and resulted in significant genetic population differentiation. A large proportion of SNPs found to be under directional selection were also identified to be highly discriminatory. Our data suggest that it is possible to discriminate between nearby <i>L. salmonis</i> populations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.en_US
dc.description.sponsorshipRCUK projecten_US
dc.descriptionSource at <a href=https://doi.org/10.1038/s41598-018-19323-z> https://doi.org/10.1038/s41598-018-19323-z</a>.en_US
dc.identifier.citationJacobs, A., De Noia, M., Præbel, K., Kanstad-Hanssen, Ø., Paterno, M., Jackson, D., ... Llewellyn, M.S. (2018). Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classication approach. Scientific Reports, 8(1203). https://doi.org/10.1038/s41598-018-19323-zen_US
dc.identifier.cristinIDFRIDAID 1562576
dc.identifier.doi10.1038/s41598-018-19323-z
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/13647
dc.language.isoengen_US
dc.publisherNature Publishing Groupen_US
dc.relation.journalScientific Reports
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7-PEOPLE/302503/EU/Metacommunity dynamics of the fish surface mircobiome in health and disease: pathogens and probiotics/FISHPROBIO/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920en_US
dc.subjectVDP::Agriculture and fishery disciplines: 900::Fisheries science: 920en_US
dc.titleGenetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classication approachen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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