New preprint: population genomics to discriminate salmon louse populations despite high gene flow
New paper available as a preprint on biorxiv with Arne Jacobs and IBAHCM colleague Martin Llewellyn (and other great sea louse collaborators) on
Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach
Arne Jacobs, Michele De Noia, Kim Praebel, Øyvind Kanstad-Hanssen, Marta Paterno, Dave Jackson, Philip McGinnity, Armin Sturm, Kathryn R. Elmer, Martin S. Llewellyn
http://www.biorxiv.org/content/early/2017/08/21/179218
Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Lepeophtheirus salmonis is the predominant species that occurs in the Northern Hemisphere. Dispersal of sea lice between marine aquaculture sites and geographic regions is thought to occur rapidly via planktonic transport of larvae. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic L. salmonis, frustrating efforts to track louse populations, improve targeted control measures and understand local adaption to environmental conditions. 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 (N=2, 27 individuals), Scotland (N=1, 11 individuals) and North Norway (N=1, 12 individuals). Weak global structure (FSC = 0.018, p<0.0001) and only one significant pairwise FST comparison was observed (Scotland vs Kenmare Bay, (FST = 0.018, p<0.0001)) using all 1286 SNPs. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment (DAPC assignment probability = 1), increased global Fsc = 0.098, (p<0.0001) and resulted in pairwise comparisons that all showed highly significant Fst-values (range = 0.081-0.096, p<0.0001). Out of 19 SNPs found to be under directional selection between populations, 12 corresponded to the discriminatory SNPs identified using random forest. Taken together our data suggest that L. salmonis SNP diversity exists with which it is possible to discriminate differences between nearby 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.
Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach
Arne Jacobs, Michele De Noia, Kim Praebel, Øyvind Kanstad-Hanssen, Marta Paterno, Dave Jackson, Philip McGinnity, Armin Sturm, Kathryn R. Elmer, Martin S. Llewellyn
http://www.biorxiv.org/content/early/2017/08/21/179218
Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Lepeophtheirus salmonis is the predominant species that occurs in the Northern Hemisphere. Dispersal of sea lice between marine aquaculture sites and geographic regions is thought to occur rapidly via planktonic transport of larvae. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic L. salmonis, frustrating efforts to track louse populations, improve targeted control measures and understand local adaption to environmental conditions. 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 (N=2, 27 individuals), Scotland (N=1, 11 individuals) and North Norway (N=1, 12 individuals). Weak global structure (FSC = 0.018, p<0.0001) and only one significant pairwise FST comparison was observed (Scotland vs Kenmare Bay, (FST = 0.018, p<0.0001)) using all 1286 SNPs. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment (DAPC assignment probability = 1), increased global Fsc = 0.098, (p<0.0001) and resulted in pairwise comparisons that all showed highly significant Fst-values (range = 0.081-0.096, p<0.0001). Out of 19 SNPs found to be under directional selection between populations, 12 corresponded to the discriminatory SNPs identified using random forest. Taken together our data suggest that L. salmonis SNP diversity exists with which it is possible to discriminate differences between nearby 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.
