Hi all,<div><br></div><div>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). </div><div><br></div><div>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 <a href="http://www.springerlink.com/content/q225678542442u22/">http://www.springerlink.com/content/q225678542442u22/</a> there is a quite clear example since they applied PCs analysis to mtDNA complete sequences to investigate phylogenetic relations among haplogroups).</div>
<div><div><div class="gmail_quote"><br></div><div class="gmail_quote">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.</div>
<div class="gmail_quote"><br></div><div class="gmail_quote">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. </div>
<div class="gmail_quote"><br></div><div class="gmail_quote">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...).</div>
<div class="gmail_quote"><br></div><div class="gmail_quote">Best regards</div><div class="gmail_quote"><br></div><div class="gmail_quote">Valeria</div><div class="gmail_quote"><br></div><div class="gmail_quote">On 15 April 2011 15:11, 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">
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<div>Hello, <br>
<br>
to combine these data, you can use scaleGen to get scaled allele frequencies and then use cbind to obtain one general matrix.<br>
<br>
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.<br>
<br>
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.<br>
<br>
All the best<br>
<br>
Thibaut<br>
</div>
<div style="font-family:Times New Roman;color:rgb(0, 0, 0);font-size:16px">
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<div style="direction:ltr"><font color="#000000" face="Tahoma" size="2"><b>From:</b> <a href="mailto:adegenet-forum-bounces@r-forge.wu-wien.ac.at" target="_blank">adegenet-forum-bounces@r-forge.wu-wien.ac.at</a> [<a href="mailto:adegenet-forum-bounces@r-forge.wu-wien.ac.at" target="_blank">adegenet-forum-bounces@r-forge.wu-wien.ac.at</a>] on behalf of Mac Campbell [<a href="mailto:macampbell2@alaska.edu" target="_blank">macampbell2@alaska.edu</a>]<br>
<b>Sent:</b> 15 April 2011 04:20<br>
<b>To:</b> <a href="mailto:adegenet-forum@r-forge.wu-wien.ac.at" target="_blank">adegenet-forum@r-forge.wu-wien.ac.at</a><br>
<b>Subject:</b> [adegenet-forum] Combining mtDNA and Nuclear Data for find.clusters() and DAPC<br>
</font><br>
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<div></div>
<div>Hi,<br>
<br>
I have searched for an answer to this, but haven't found one. Would someone be able to help me the following?<br>
<br>
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.<br>
<br>
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.<br>
<br>
Thanks in advance,<br>
<br>
Mac<br clear="all">
> X<br>
<br>
#####################<br>
### Genind object ### <br>
#####################<br>
- genotypes of individuals - <br>
<br>
S4 class: genind<br>
@call: df2genind(X = x[, -1], ind.names = x[, 1], ploidy = 1)<br>
<br>
@tab: 72 x 121 matrix of genotypes<br>
<br>
@ind.names: vector of 72 individual names<br>
@loc.names: vector of 67 locus names<br>
@loc.nall: number of alleles per locus<br>
@loc.fac: locus factor for the 121 columns of @tab<br>
@all.names: list of 67 components yielding allele names for each locus<br>
@ploidy: 1<br>
@type: codom<br>
<br>
Optionnal contents: <br>
@pop: - empty -<br>
@pop.names: - empty -<br>
<br>
@other: - empty -<br>
<br>
> Y<br>
<br>
#####################<br>
### Genind object ### <br>
#####################<br>
- genotypes of individuals - <br>
<br>
S4 class: genind<br>
@call: df2genind(X = y[, -1], sep = "/", ind.names = x[, 1])<br>
<br>
@tab: 72 x 32 matrix of genotypes<br>
<br>
@ind.names: vector of 72 individual names<br>
@loc.names: vector of 18 locus names<br>
@loc.nall: number of alleles per locus<br>
@loc.fac: locus factor for the 32 columns of @tab<br>
@all.names: list of 18 components yielding allele names for each locus<br>
@ploidy: 2<br>
@type: codom<br>
<br>
Optionnal contents: <br>
@pop: - empty -<br>
@pop.names: - empty -<br>
<br>
@other: - empty -<br>
<br>
<br>
<br>
-- <br>
Matthew A Campbell<br>
Department of Biology and Wildlife<br>
University of Alaska, Fairbanks<br>
</div>
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