Hello,<br>Please correct me if I'm wrong,<br>but I think that viewing population differentiation with Fst has many limitations as well.<br>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?<br>
Probably it's possible to utilize obtained principal component scores for that?<br>Or this method will overestimate the differentiation?<br>
<br>Using other genetic distance measures (especially <span id="result_box" class="short_text" lang="en"><span class="" title="Нажмите, чтобы увидеть альтернативный перевод">those</span> <span title="Нажмите, чтобы увидеть альтернативный перевод" class="hps">which assume particular </span></span>mutation model, i.e.<span id="result_box" class="short_text" lang="en"><span title="Нажмите, чтобы увидеть альтернативный перевод" class="hps"> IAM or SSM for microsatellites)</span></span> for the real data could be tricky as well.<br>
<br>Vladimir.<br><br><br>PS. a brief summary of Fst's assumptions one may find here:<br>
<a href="https://anthrogenetics.wordpress.com/2010/10/11/problems-with-fst-based-methods-human-populations-violate-important-assumptions/" target="_blank">https://anthrogenetics.wordpress.com/2010/10/11/problems-with-fst-based-methods-human-populations-violate-important-assumptions/</a><br>
<br>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)<br>
<br>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. 3845-3852.<br>
<br><br><div class="gmail_quote">On Tue, May 3, 2011 at 10:53 PM, Mac Campbell <span dir="ltr"><<a href="mailto:macampbell2@alaska.edu" target="_blank">macampbell2@alaska.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi,<div><br></div><div>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.</div>
<div><br></div><div>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. </div>
<div><br></div><font color="#888888"><div>Mac</div></font><div><div></div><div><div><br><div class="gmail_quote">On Sat, Apr 30, 2011 at 8:56 AM, Jombart, Thibaut <span dir="ltr"><<a href="mailto:t.jombart@imperial.ac.uk" target="_blank">t.jombart@imperial.ac.uk</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div>
<div style="direction:ltr;font-family:Tahoma;color:#000000;font-size:10pt">Hello,
<br>
<br>
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.<br>
<br>
I will be working on the next adegenet release over the weeks to come, so will probably give it a go soon.<br>
<br>
Cheers<br>
<br>
Thibaut<br>
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