[adegenet-forum] Detecting Genetically Unique Individuals in a Well Mixed Population

[Nathan Truelove]

Dear Thibaut,

Thank you for your email and your advice. I agree I should have been much more clear about what is meant by 'outliers'. The main research question of my PhD is to test the hypothesis that large-scale ocean currents shape spatial patterns of genetic variation in Caribbean spiny lobster species. We specifically collected genetic samples from regions with contrasting ocean currents, in particular advective regions (fast moving currents: Gulf Stream Current, Yucatan Current, and Caribbean Current) and retentive regions (slow moving circular currents, that retain marine larvae). Oceanographic and biological modeling studies have predicted that retentive regions have high levels of self recruitment (lobster larvae returning to their natal spawning site after spending 6 months in the open ocean), whilst advective regions have low levels of self recruitment. Therefore, for comparing the genetics data from my PhD to previous modeling studies, the definition of 'outlier' is spatial. I would specifically like to test the hypothesis that in retentive regions individuals are more genetically similar to their neighbors, whilst in advective regions they are more genetically different. 


I recently went through all the steps in your sPCA vignette to look for spatial patterns of global or local structure. None of the tests for spatial structure came out to be significant (mantel.randtest, global.rtest, local.rtest). I continued along with the sPCA vignette and tried using both the Delaunay triangulation and neighborhood by distance connection networks. However, I'm assuming that I shouldn't be very confident of any sPCA results since none of the initial statistical tests indicated the presence of spatial structure. Using the Delaunay triangulation network, the s.value results indicated global structure in one large advective region and local structure in the rest of the locations. When I used the neighborhood by distance network, I allowed the maximum distance between neighbors to be high enough that all sites could be connected to each other. This was probably too connected, whilst the Delaunay probably wasn't connected enough. When I used s.value for this analysis the all sites expect for Bermuda (the most distanct) displayed global structure.

It would be great to get your opinion on using sPCA for Caribbean spiny lobster. Does the lack of spatial structure according to the mantel.randtest indicate that sPCA shouldn't be used? If you think sPCA should be pursued, I should be able get access to oceanographic modeling data that could be used to create a potentially more realistic connectivity network than either the Delaunay or neighborhood networks. Also on the topic of 'outliers' perhaps it would be more appropriate to focus on individuals with rare/original alleles since the spatial signal appears to be relatively weak. 

Thanks again for all your time and advice. It's been really helpful.

Best Wishes,

Nate




On Apr 30, 2013, at 6:14 AM, Jombart, Thibaut wrote:

> Dear Nate, 
> 
> the problem here is that it is not clear what is meant by 'outliers'. If we're talking about a few migrants from another population, then they should fall in a small cluster of there own (e.g. using find.clusters). If the definition is spatial, then 'outliers' may be individuals that are genetically distinct from their neighbours (without having to be migrants from another population). Or, 'outliers' can be individuals with rare/original alleles (without having to be any of the above). Or 'outliers' can be whatever does not fall within the inertia ellipse, and in this case you will always have 'outliers' with the default parameters of s.class.
> 
> All of these definitions of 'outliers' would require different techniques to pin them down. I would really avoid anything based on the distance from the centroid. This implies that the cloud of point of the population is well represented in only 2D and more importantly is spherical, which is very unlikely. Detection based on inertia ellipses (not intertia - inertia is the squared length of a vector, which in PCA is the variance of the corresponding scores) is bound to fail to. There the assumption is that the cloud of point of the population is bivariate normal, which again is unlikely. But if it is the case, the default inertia ellipse in s.class contains 2/3 of the points. It would be far-fetched to call the remaining third 'outliers'. One can change this parameter, but again, that means arbitrarily deciding of a fixed number of outliers.
> 
> But again, the problem here as I understand it is not technical (for now) - what is meant by 'outliers' needs to be clarified first.
> 
> All the best
> 
> Thibaut
> 
> ________________________________________
> From: adegenet-forum-bounces at lists.r-forge.r-project.org [adegenet-forum-bounces at lists.r-forge.r-project.org] on behalf of Nathan Truelove [nathan.truelove at manchester.ac.uk]
> Sent: 23 April 2013 13:46
> To: adegenet-forum at lists.r-forge.r-project.org
> Subject: [adegenet-forum] Detecting Genetically Unique Individuals in a Well    Mixed Population
> 
> 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.
> 
> Best Wishes,
> 
> Nate
> 
> 
>