Hi there,
I have ddRADseq data, with RAD-loci assembled de-novo using the STACKS pipeline for two species and their hybrids. As such, I was wondering how I could estimate Fst for each of those putative loci without using BayeScan. This is because many of my samples are hybrids which violate the model assumptions of the program.
I have seen a method mentioned which uses the ' Bayesian implementation of the F-model' by Gompert et al 2012, but I am unsure how to practically put this into use. I have also tried using outFLANK, but it needs a data-set containing no missing data.
Thanks a lot.
Rowan
Hi Rowan, not sure if this helps at all because I don't have experience with it myself but in this paper: https://www.nature.com/articles/s41559-018-0606-3?fbclid=IwAR0jG9EvSekx_JJVS58CTQJhJKqaZ2MlYIE34ei44EK5I3XPBRSbr41RqXY Morales et al. measure per-SNP differentiation with the diffCalc function in the R package DiveRsity, it might be worth a try!
Hi, Rowan, you can try the permutation test. Briefly, you may permute the individuals and obtain simulated Fst values for each locus. Then you may declaring a outlier through comparing the observed Fst and the distribution of simulated values.
You can also check this paper Article Evolutionary Metabolomics Reveals Domestication-Associated C...
Good luck!
Hey! this is a super old question, but still here my answer in case someone else stumbles upon your question.
Fst per Locus can be calculated directly with STACKs in the population function/pipeline. Then, you can use that stuff to run OutFlank in R either using this script and your FST values: https://github.com/whitlock/OutFLANK/blob/master/R/OutFLANK.R
our run OutFlank on a genind object that you can convert in R from a genepop or structure input file using the import2genind function and then this other script to calculate all the parameters required from there: https://rpubs.com/lotterhos/outflank
Hope this helps! PS: I prefer the second...
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