ADMIXTOOLS

In this post, I will build a transparent admixture-screening workflow from scratch in R using f4-statistics and constrained regression. The main advantage is automation: instead of hand-writing every candidate model, the script tests many 2-way, 3-way, and 4-way source combinations in one pass and ranks them by fit. The result is not a replacement for qpAdm, but a fast screening layer that can help you identify promising models before you validate them more formally. ADMIXTOOLS 2 already includes batch tools such as qpadm_multi() and qpadm_rotate(), so the point here is not that qpAdm cannot be automated. The point is that this custom workflow is compact, transparent, easy to modify, and useful for exploratory model search. ...

This post covers using qpAdm in R to test ancestry models and estimate admixture proportions. qpAdm builds on f4-statistics and provides a framework for evaluating whether proposed source populations can explain a target population’s genetic makeup. For R and admixtools setup instructions on Debian/Ubuntu, see my previous post: Running f4-Statistics with Admixtools in R. Windows users can find R installation instructions on the R website. What is qpAdm? qpAdm is a method for testing ancestry models and estimating admixture proportions. It determines whether a target population can be modeled as a mixture of specified source populations (“left populations”), and if the model fits, calculates the contribution from each source. The method builds on f4-statistics (covered in my previous post) to evaluate these ancestry models. ...

This post covers how to run f4-statistics using the admixtools package for R. Compared with the original ADMIXTOOLS workflow, the R implementation is more convenient for testing multiple population combinations because it can be used interactively, without repeatedly editing parameter files. It is also much faster in practice, which makes it possible to work directly with the full AADR dataset without creating subsets. What are f4-statistics? F4-statistics measure asymmetry in allele sharing among four populations. For four populations AAA, BBB, CCC, and DDD, the statistic is written as: ...