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

[Nathan Truelove]

Dear Valeria,

Thank you for your advice and encouragement. I definitely need it working with flat populations! The overall aim of my research is to see if ocean currents are shaping spatial patterns of genetic variation in spiny lobster species. For example, we would like to investigate if individuals are more genetically different to their neighbors in advective oceanographic regions and more genetically similar to their neighbors in retentive regions. I was also interested in trying to figure out if any individuals that are genetically different from their neighbors happen to be immigrants from a distant population. After reading Thibaut's response to my first email it looks like I was heading down the wrong path by trying to force my data into 2 dimensions and basing my identification of 'outliers' based upon distance from the centroid.  Some new techniques are definitely needed to search for the presence of any genetically unique individuals within my data.

I really like your ideas of creating a tree using nj in ape and also using dist.gene in ape to calculate the mean number of pairwise distances of every individual from all others. I took your advice and ran your analyses. The tree in ape had 3 nodes. A long branch in the second node contained 22 individuals and stood out from all other branches. I then calculated the mean of the distances of every individual from all others and it came out to be 41. I sorted out all individuals that where 'arbitrarily' 2X above this threshold and almost all of these individuals also belonged to the branch that stood out in the second node of the tree.

It would be great to get your opinion on these results. Perhaps it would be best if I sent you an image of the tree since it's a little tricky to describe it properly. Just let me know what you prefer. Your advice has been really helpful.

Best Wishes,

Nate


On Apr 30, 2013, at 6:16 PM, Valeria Montano wrote:

Hi Nate,

I think Thibaut's answer is already more than appropriate and actually points out the main question among your questions. As I understand, your population is not really easy to deal with since you have this high genetic homogeneity which does not leave much room to imagination (a bit frustrating I believe). Focusing on outliers can be an option, but it really depends on your scientific aim. If I were you, I would try with a statistics estimating individual genetic distances (for instance the mean number of pairwise distances using dist.gene in the ape package), calculate the mean of the distances of every ind from all the others, and than put a threshold to define 'outliers', does it make sense? A wee bit arbitrary maybe...moreover, in this case you would have 'outliers' compared to the general population, and I am not sure it would help...

On the other hand, to understand whether outliers are immigrants from distant pops, you could build a network or use any phylogenetic reconstruction and see if outliers appear to be long but derived branches within their geographic neighbours or if they are more basal. This is the only tool that comes to my mind.

Anyway good luck with it, flat populations are upsetting.

with the occasion, happy Labor day everybody! (or happy transition from Spring to Summer - just in case you follow the Celtic tradition)

Valeria

On 30 April 2013 12:14, Jombart, Thibaut <t.jombart at imperial.ac.uk<mailto:t.jombart at imperial.ac.uk>> 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<mailto:adegenet-forum-bounces at lists.r-forge.r-project.org> [adegenet-forum-bounces at lists.r-forge.r-project.org<mailto:adegenet-forum-bounces at lists.r-forge.r-project.org>] on behalf of Nathan Truelove [nathan.truelove at manchester.ac.uk<mailto:nathan.truelove at manchester.ac.uk>]
Sent: 23 April 2013 13:46
To: adegenet-forum at lists.r-forge.r-project.org<mailto: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 severa
 l 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



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