Congrats to Rich and co-authors! Our new method, genomic posterior predictive simulations of Fst and dxy (GppFst) was recently published in Bioinformatics.

This approach uses the R statistical programming language and allows the user to simulate loci from divergent populations under a strict neutral model of mutation and genetic drift, using population genetic (diversity and divergence time) estimates from empirical datasets. By simulating loci under this model, users can then compare levels of allelic differentiation between the neutral distribution and their empirical distribution to identify levels of differentiation that are poorly explained by drift.

We recently demonstrated the use of this method in our paper on the role of selection in divergence and secondary contact in rattlesnakes.

To learn more about this exciting new method, follow the link below to download a copy of the paper, and check out Rich’s Github.

Adams, R.H., D.R. Schield, D.C. Card, H. Blackmon, and T.A. Castoe. 2017. GppFst: Genomic posterior predictive simulations of Fst and dXY for identifying outlier loci from population genomic data. Bioinformatics 33: 1414-1415. PDF.

 

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