Hi,
I have ran the XGMix in a set of admixed samples that I have simulated for whom I know the true ancestry for each of the individual markers and the switch points.
I noticed that the output of XGMix is given in intervals of genetic positions and I'm wondering if there's a way for the program to give the local ancestry for each individual marker?
If a sum all the markers from all the ranges generated I get all the markers from the intersecting SNPs. But this information is not much useful for downstream analysis as I believe the individual maker's ancestry is important...
The same happens with the probability output file: Is there a way to have the probability of individual markers to belong to a certain ancestry?
I'm asking because I intent to use Local Ancestry Inference in conjunction with the Tractor program that allow to include LAI tracks for association analysis and etc...
Hi guidebortoli!
Transforming the output into the individual markers you have shouldn't be too hard with standard python but this is a good feature request.
We've just launched a new codebase, Gnomix, where I'll make sure to add this feature this very week.
Hi, I have ran the XGMix in a set of admixed samples that I have simulated for whom I know the true ancestry for each of the individual markers and the switch points.
I noticed that the output of XGMix is given in intervals of genetic positions and I'm wondering if there's a way for the program to give the local ancestry for each individual marker? If a sum all the markers from all the ranges generated I get all the markers from the intersecting SNPs. But this information is not much useful for downstream analysis as I believe the individual maker's ancestry is important...
The same happens with the probability output file: Is there a way to have the probability of individual markers to belong to a certain ancestry?
I'm asking because I intent to use Local Ancestry Inference in conjunction with the Tractor program that allow to include LAI tracks for association analysis and etc...
Thank you!