[adegenet-forum] Combining mtDNA and Nuclear Data for find.clusters() and DAPC

[valeria montano]

Hi all,

sorry for the participation a bit off-topic, it's just to do a few
considerations which may be interesting for you (I hope so).

Regarding mtDNA, using the individual sequence in a multivariate analysis as
PCs implies that the sequence is considered as composed by independent loci,
which is actually not so. Performing a cluster analysis on individuals, what
one would detect is a structure related to haplogroup phylogeny. It is
intuitive that an undividual with a certain haplogroup will be closer to
another one presenting a sequence of the same haplogroup but belonging to a
different population than to an individual of the same population
characterized by a haplotype phylogenetically more distant.  That would mean
to obtain artifactual haplogroup-driven populations (in this paper
http://www.springerlink.com/content/q225678542442u22/ there is a quite clear
example since they applied PCs analysis to mtDNA complete sequences to
investigate phylogenetic relations among haplogroups).

It's definitely cool to have a method like DAPC to use unilinear loci as
mtDNa and Y chromosome for structure analysis, but, theoretically
speaking, I think that to correctly do it one should use the matrix of
haplogroup frequencies calculated for populations, when these are previously
known, since that is the only way to treat the data as a multiallelic single
locus. Otherwise that would be better to avoid using them.

Another concern is about sex biased dispersal. If this phenomenon strongly
occurs in the species under study, it's possible that autosomal loci and
mtDNA present a different spatial distribution and consequently a different
population structure, since mtDNA would probably keep the information
regarding only the distribution of female individuals. It could be
interesting to verify if it is actually mirrored by population structure
depending on the dataset considered. After assigning individuals to
populations with autosomal loci, the matrix of population allelic
frequencies for both mtDNA and autosomal can be calculated and then the
population genetic relations compared through a simple approach like Fst.

Ok...sorry again for the invasion, I hope you won't find it too dull. I'd be
glad to know your opinion about these considetations, since mtDNA and Y
chomosome will be my cross for still a bit of time and I wouldn't like to
have made a blunter on the whole line (would be fun but unpleasent...).

Best regards

Valeria

On 15 April 2011 15:11, Jombart, Thibaut <t.jombart at imperial.ac.uk> wrote:

>
> Hello,
>
> to combine these data, you can use scaleGen to get scaled allele
> frequencies and then use cbind to obtain one general matrix.
>
> The more concerning problem is that you may be merging information of
> different nature by doing so. Also, it is likely that the results will
> mainly be driven by the dataset with the most variability. That may be fine
> ("I want to take the information where it is.") or not ("I want both types
> of data to contribute equally to the analysis"), depending on what you want
> to do.
>
> I would advise at least checking that the analysis done on the entire
> dataset matches the results of the separate analyses. Running two separate
> PCAs and checking for similarities between them using coinertia analysis
> (function coinertia in ade4) should also be useful.
>
> All the best
>
> Thibaut
>  ------------------------------
> *From:* adegenet-forum-bounces at r-forge.wu-wien.ac.at [
> adegenet-forum-bounces at r-forge.wu-wien.ac.at] on behalf of Mac Campbell [
> macampbell2 at alaska.edu]
> *Sent:* 15 April 2011 04:20
> *To:* adegenet-forum at r-forge.wu-wien.ac.at
> *Subject:* [adegenet-forum] Combining mtDNA and Nuclear Data for
> find.clusters() and DAPC
>
>  Hi,
>
> I have searched for an answer to this, but haven't found one.  Would
> someone be able to help me the following?
>
> I have two data sets, mitochondrial and nuclear.  I have created two Genind
> objects (X and Y, pasted below) with the same individuals in the same order.
>
> Is it reasonable to combine the two data sets for use in find.clusters()
> and DAPC?  Is there a way to combine two genind objects within adegenet
> easily?  I've tried several general approaches for S4 objects.
>
> Thanks in advance,
>
> Mac
> > X
>
>    #####################
>    ### Genind object ###
>    #####################
> - genotypes of individuals -
>
> S4 class:  genind
> @call: df2genind(X = x[, -1], ind.names = x[, 1], ploidy = 1)
>
> @tab:  72 x 121 matrix of genotypes
>
> @ind.names: vector of  72 individual names
> @loc.names: vector of  67 locus names
> @loc.nall: number of alleles per locus
> @loc.fac: locus factor for the  121 columns of @tab
> @all.names: list of  67 components yielding allele names for each locus
> @ploidy:  1
> @type:  codom
>
> Optionnal contents:
> @pop:  - empty -
> @pop.names:  - empty -
>
> @other: - empty -
>
> > Y
>
>    #####################
>    ### Genind object ###
>    #####################
> - genotypes of individuals -
>
> S4 class:  genind
> @call: df2genind(X = y[, -1], sep = "/", ind.names = x[, 1])
>
> @tab:  72 x 32 matrix of genotypes
>
> @ind.names: vector of  72 individual names
> @loc.names: vector of  18 locus names
> @loc.nall: number of alleles per locus
> @loc.fac: locus factor for the  32 columns of @tab
> @all.names: list of  18 components yielding allele names for each locus
> @ploidy:  2
> @type:  codom
>
> Optionnal contents:
> @pop:  - empty -
> @pop.names:  - empty -
>
> @other: - empty -
>
>
>
> --
> Matthew A Campbell
> Department of Biology and Wildlife
> University of Alaska, Fairbanks
>
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