[adegenet-forum] Detecting Genetically Unique Individuals in a Well Mixed Population
nathan.truelove at manchester.ac.uk
Tue Apr 23 14:46:27 CEST 2013
Dear Thibaut and Adegenet Users,
I would like to begin by thanking Thibaut and everyone else who created Adegenet, it has to be the most useful data analysis tool that I have used for my PhD research.
I am PhD student working on the population genetics of Caribbean spiny lobster using 16 microsatellite markers. The species has a huge potential for migration since it can spend up to a year floating/swimming in ocean currents before settling in shallow coastal habitat. Adults can also migrate 10s to 100s of km. It's no big surprise that I am finding very little differentiation in PCA, PCoA, and DAPC analyses. The trend that comes out in all these analyses is that ~80% of individuals from all sampling sites fall within the interia ellipse (s.class) or the contour polygon (s.chull). Several of the individuals outside the interia ellipse (or polygons) are located quite far away from the "core" of individuals within the ellipse. These outlier individuals are not associated with any particular site, however on the spatial level, there appear to be more outliers in southern sites than in northern sites. I've been trying a variety of techniques to try and figure out the ecological importance of these outlier individuals. For example, a recent paper by Elphie et al. entitled "Detecting immigrants in a highly genetically homogeneous spiny lobster population (Palinurus elephas) in the northwest Mediterranean Sea" explores a similar issue in a different species of lobster. In this paper the authors use non-metric multidimensional scaling to separate out the genetic distances of their individuals in multivariate space. They then classified all individuals within a 50% radius of the barycentre as the "reference population" and all individuals outside the 50% radius as an "assignment population". They then used Geneclass2 to run assignment tests and any individuals that had a p-value < 0.05 are considered "genetically different". The authors argue that the most likely explanation for the genetic differences is that the genetically unique individuals detected in Geneclass are migrants from populations that have genetically diverged. I imagine there are several other ecological or selective processes that could also lead to genetically unique individuals, so calling them migrants is up for debate.
For my data I ran a similar analysis in Adegenet using the functions s.class and s.chull along with dudi.pca to select the reference and assignment populations for Genclass2. I compared these results to a similar analysis using non-metric multidimensional scaling in the Vegan package. The Adegenet PCA analyses contained about twice as many individuals in the reference population than the nMDS technique, yet the overall trend of Geneclass finding more unique individuals in the south than the north was consistent among all techniques. Also, most of the distant outliers in PCA analysis in Adegenet were also significantly different in the Geneclass analysis.
It would be excellent to get your opinions on this technique and discuss potential options for improving it:
1) Would it be possible to get additional information using Adegenet on how different the outliers in PCA are from the "core" of individuals inside the inertia ellipse? It would be nice to run the entire analysis in Adegenet and not have to use Geneclass2 at all.
2) Is there a simple way to identify each individual within an inertia ellipse. I have been using the function identify to select the individuals that are located within the ellipse, yet it is rather clunky since you have to click on every point.
3) Any additional advice concerning how to detect genetic outliers in homogeneous populations using Adegenet would be greatly appreciated.
Thank you very much for your time.
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