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

[Jombart, Thibaut]

Hi again,

thanks for joining the discussion. To comment quickly on this, I would not say that running a multivariate analysis on mtDNA implies independent loci. There is no theoretical/numerical constraint in that respect. On the contrary, multivariate analyses are often use to handle redundancy. So it is not clear what kind of bias this would induce, but I suspect a very mild one to none. This being said, I agree that mtDNA should be analysed as one locus (with lots of alleles).

As for what can be inferred from mtDNA, well... it does make sense to me that the multivariate analysis of mtDNA data will retrieve mtDNA haplogroups. I am not sure we should call this 'artefactual', this is merely what is contained in the data (and possibly not much more). mtDNA for demographic or phylogenetic inference strikes me as limited, at best. I can only recommend reading this short and fun comment on the topic by Francois Balloux in Heredity, called "The worm in the fruit of the mitochondrial DNA tree":
http://www.nature.com/hdy/journal/v104/n5/full/hdy2009122a.html

All the best

Thibaut

________________________________
From: valeria montano [mirainoshojo at gmail.com]
Sent: 17 April 2011 19:28
To: Jombart, Thibaut
Cc: Mac Campbell; adegenet-forum at r-forge.wu-wien.ac.at
Subject: Re: [adegenet-forum] Combining mtDNA and Nuclear Data for find.clusters() and DAPC

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<mailto: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<mailto:adegenet-forum-bounces at r-forge.wu-wien.ac.at> [adegenet-forum-bounces at r-forge.wu-wien.ac.at<mailto:adegenet-forum-bounces at r-forge.wu-wien.ac.at>] on behalf of Mac Campbell [macampbell2 at alaska.edu<mailto:macampbell2 at alaska.edu>]
Sent: 15 April 2011 04:20
To: adegenet-forum at r-forge.wu-wien.ac.at<mailto: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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