[adegenet-forum] DAPC analyis and interpretation

[Elodie Blanchet]

Dear Dr. Jombart and Adegenet users,

I have some questions about DAPC analysis.

I worked on tetraploid plant, with 11 SSR markers, 15 populations 
sampled with 30 individuals each.

1) When I ran 'find.clusters' function, elbow in the curve of BIC values 
was not very clear so I ran it many time. But I obtained different 
optimal number of cluster even if I increase "max.n.cluster" option.

I agree that it is made with Bayesian computation, but in this case how 
can I choose the "best" optimal number of cluster?

Maybe, these non-homogenous results between different runs are due to 
the sampling pattern of my populations which were along a corridor (thus 
suggesting a stepping-stone model of dispersal?)

2) Besides, if I took into account the most frequent "k" after ten runs 
of "find.clusters" function (k=8), I observed that actual groups did not 
correspond to inferred group. I mean that in the best case, only 17,5 % 
of my actual group are inferred to clusters revealed by the analysis. 
Even if individual posterior membership was upper than 75% in most of 
case, I did not know if the genetic structure revealed by the analysis 
is supported or not?

3) Moreover, some of the clusters revealed by the analysis, are made 
with individuals having posterior membership probability <60%, how 
interpreting these clusters? I would tend to run again the analysis and 
reduce "k"...?

Sorry for this long mail, I hope it is sufficiently clear.

Thanks in advance for your help.

Elodie

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