Applying <i>f</i><sub>4</sub>‐statistics and admixture graphs: Theory and examples
Mark Lipson
Abstract
Abstract A popular approach to learning about admixture from population genetic data is by computing the allele‐sharing summary statistics known as f ‐statistics. Compared to some methods in population genetics, f ‐statistics are relatively simple, but interpreting them can still be complicated at times. In addition, f ‐statistics can be used to build admixture graphs (multi‐population trees allowing for admixture events), which provide more explicit and thorough modelling capabilities but are correspondingly more complex to work with. Here, I discuss some of these issues to provide users of these tools with a basic guide for protocols and procedures. My focus is on the kinds of conclusions that can or cannot be drawn from the results of f 4 ‐statistics and admixture graphs, illustrated with real‐world examples involving human populations.