Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach

Overview
TitleGenetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach
AuthorsJacobs A, De Noia M, Praebel K, Kanstad-Hanssen Ø, Paterno M, Jackson D, McGinnity P, Sturm A, Elmer KR, Llewellyn MS
TypeJournal Article
Journal NameScientific reports
Volume8
Issue1
Year2018
Page(s)1203
CitationJacobs A, De Noia M, Praebel K, Kanstad-Hanssen Ø, Paterno M, Jackson D, McGinnity P, Sturm A, Elmer KR, Llewellyn MS. Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach. Scientific reports. 2018 Jan 19; 8(1):1203.

Abstract

Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic Lepeophtheirus salmonis, 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 L. salmonis 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 L. salmonis 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.

Author Details
Additional information about authors:
Details
1A Jacobs
2M De Noia
3K Praebel
4Ø Kanstad-Hanssen
5M Paterno
6D Jackson
7P McGinnity
8A Sturm
9K R Elmer
10M S Llewellyn
Properties
Additional details for this publication include:
Property NameValue
Publication ModelElectronic
ISSN2045-2322
eISSN2045-2322
Publication Date2018 Jan 19
Journal AbbreviationSci Rep
DOI10.1038/s41598-018-19323-z
Elocation10.1038/s41598-018-19323-z
LanguageEnglish
Language Abbreng
Publication TypeJournal Article
Journal CountryEngland
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PMID: PubMedPMID:29352185