Keeping repeated MLGs

[Stacy Krueger-Hadfield]

Hi!

I am analyzing several datasets of organisms in which we are calculating Psex (Parks and Werth 1993_AmJBot, Arnaud-Haond et al. 2007_Mol Ecol).  Briefly, P_sex is the probability for a given multi locus genotype (microsatellites) to be observed in N samples as a consequence of different sexual reproductive events.  We calculated this on our entire data set before running the genotypes through poppr in order to estimate rbarD.  There are some instances where there are repeated MLGs but the Psex values are > 0.05 so we have to treat the "individual" as a different genet.  Thus, it is maintained in the dataset.  However, I realized after running samples through poppr, that all repeated MLGs were removed prior to obtaining basic summary statistics, including rbarD.  It doesn't appear possible to override this and keep repeated MLGs that are considered different genets.

I was wondering if I am missing something or if there is a way in which we can maintain these repeated genotypes.  We have a workflow in place in which the truly repeated MLGs are removed before we get to the poppr rbarD.  

Any help would be greatly appreciated!

Thanks!

Stacy

[Zhian Kamvar]

Hi Stacy,

If I am reading this correctly, you have samples with duplicated MLGs with Psex values greater than a threshold indicating that these genotypes are not necessarily clone-mates. The problem you encounter is that clone correction is occurring and removing one of the duplicated MLGs before conducting analyses. 

If you have an assigned genet to each MLG, then I would suggest to add that as a hierarchical level with the addhierarchy function. After you use that, you can use it as the lowest level of your population hierarchy and duplicated MLGs that exist within different genets will not be removed.

for example:

addhierarchy(myData) <- data.frame(Genet = myGenets)
poppr(myData, clonecorrect = TRUE, hier = ~Population/Genet, keep = 1)

The first line assumes that you have your data in a genclone object and that you have your genet assignments in a vector called "myGenets".

The second line calls the summary function. Clone correction is turned on, and the hierarchy is set to a population that you've pre-defined and the Genets that you defined in line 1. The argument keep = 1 indicates that you only want to keep the top level of the hierarchy after clone correction for analysis. 

I hope that helps, but if it doesn't, please let me know if I missed the target completely.

Cheers,

Zhian