[adegenet-forum] Population clustering idea
vmikryukov at gmail.com
Wed May 4 09:23:10 CEST 2011
Please correct me if I'm wrong,
but I think that viewing population differentiation with Fst has many
limitations as well.
Why one should switch from a more robust method (DAPC doesn't care about
Hardy-Weinberg equilibrium and linkage disequilibrium, isn't it?) to the
other (Fst) approach?
Probably it's possible to utilize obtained principal component scores for
Or this method will overestimate the differentiation?
Using other genetic distance measures (especially those which assume
particular mutation model, i.e. IAM or SSM for microsatellites) for the real
data could be tricky as well.
PS. a brief summary of Fst's assumptions one may find here:
Or at least I'll suggest to use bias-corrected differentiation index (Dest)
like in DEMEtics package (see reference). However, in my practice usually it
is highly correlated with Fst (Mantel's r = 0.7 - 0.96)
Gerlach G., Jueterbock A., Kraemer P., Deppermann J., Harmand P.
Calculations of population differentiation based on Gst and D: forget Gst
but not all of statistics! // Molecular Ecology. 2010. V. 19. № 18. P.
On Tue, May 3, 2011 at 10:53 PM, Mac Campbell <macampbell2 at alaska.edu>wrote:
> Yes, I agree there are many limitations to viewing populations in a tree
> like perspective. Initially, I was interested in quantifying how far apart
> the groups are on a scatter plot because it was hard to tell. I think the
> code Vladimir sent me does just that, at least it tells me which ones are
> closer to each other.
> It will be cool to have a more biologically significant (Fst based) way
> implemented. One thing that came to mind too was if I wanted to use
> something like IMa2, I would need to have an assumption in tree form of how
> the populations are related.
> On Sat, Apr 30, 2011 at 8:56 AM, Jombart, Thibaut <
> t.jombart at imperial.ac.uk> wrote:
>> that's a good question. Actually I thought about implementing something
>> along these lines for the dapc scatterplot. I agree with Russell's point
>> that relationships between populations are not necessarily best presented by
>> fully bifurcating trees. However, linking the populations which are the
>> closest according to a given distance measure (e.g. Fst ) does make sense. I
>> would go for a minimum spanning tree, which is a nice way of showing which
>> are the closest neighbours in terms of genetic distances. It won't be too
>> much of a pain to code either.
>> I will be working on the next adegenet release over the weeks to come, so
>> will probably give it a go soon.
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