[adegenet-forum] interpretation sPCA

[valeria montano]

Hi there,

sorry for my usual gratuitous intervention. I just wanted to suggest the
eventuality to use the dapc groups as an alternative to population labels
(summary statistics of internal diversity on them would already help getting
an idea of the overall situation). I actually think that at the
intraspecific level previous grouping of individuals based on sample
location criteria or whatever are usually quite biased. Genetic structure
grouping might sound as a circular reasoning, but I have the feeling it is
less arbitrary than any other approach. The "sliding windows" is definitely
an alternative but still a bit arbitrary and maybe scheming, although it may
be worth comparing the results of the both. Moreover, to individuate the
most probable point of origin of the species, it may be useful to also
explore the phylogeography as a support to the results obtained with the
population approach.

Best

Valeria

On 4 October 2011 14:37, Jombart, Thibaut <t.jombart at imperial.ac.uk> wrote:

> Dear Evert,
>
> I don't think the existence of a cline can be used to infer the origin of
> an organism. Surely in this case the cline you obtain is compatible with a
> 'central' origin, but the origin could as well be at either extremities of
> the cline, or anywhere in between. All the pattern says is that gene flow is
> somehow negatively related to geographic distance. More generally, no
> multivariate analysis result is directional. It would be reassuring if the
> outcome of sPCA roughly match that of DAPC, although both methods are
> different. This can be easily checked by DAPC scores on the map.
> Discrepancies can be due to, for instance, the fact that non-spatial genetic
> structures are the strongest (then DAPC will pick that up first). Another
> one would be the absence of spatial structure. It is safer to perform a
> global.rtest (although it lacks power) and to check the screeplot of sPCA
> before interpreting structures.
>
> Test the origin of your populations would need population-level data. The
> idea is that within-population diversity decreases when we get away from the
> origin due to repeated bottlenecks. If you don't have population data, one
> workaround would be using moving windows to map diversity geographically,
> and then use a simple optimisation procedure to find the 'optimal' origin. I
> don't know if this has been done before, so it might be newish. I have
> developed a package "geoGraph" (on Rforge, not on CRAN:
> https://r-forge.r-project.org/R/?group_id=348) which does this (apart from
> the moving windows) and has a vignette illustrating the whole process.
>
> Cheers
>
> Thibaut.
>
>
>
> ________________________________________
> From: Thomas, Evert (Bioversity-Colombia) [E.Thomas at CGIAR.ORG]
> Sent: 03 October 2011 21:48
> To: Jombart, Thibaut; Linda Rutledge; adegenet-forum at r-forge.wu-wien.ac.at
> Subject: interpretation sPCA
>
> Dear Thibaut,
>
> I have a question regarding the interpretation of the sPCA scores as
> visualized in a color plot or interpolated lagged scores. I am working with
> intraspecific species data at continental level and found a strong gradient
> in my data  with a clear separation of a northern and southern group. Based
> on a number of grounds I believe that the center of origin of the species I
> am working with is located  at the "genotone" (or what to call this, I mean
> the grey area between both groups where the genetic differentiation is the
> steepest) . Does this make sense with the theory behind sPCA? I think the
> species moved north and south from the putative center of origin and
> developed into different genotypes which becomes apparent in the
> visualization of the sPCA...
>
> And should the outcome of an sPCA be somewhat reflected in the outcomes of
> discriminant analysis of principal components or are these really two
> different methods? (I apologize for my ignorance)
>
> Many thanks in advance
>
> Evert
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