1.3 | An ABC framework for reconstructing complex admixture histories
In this paper, we show how ABC can be successfully applied to reconstruct, from genetic data, highly complex admixture histories beyond models with a single or two pulses of admixture classically explored with ML methods. To do so, we propose a novel forward-in-time genetic data simulator and a set of parameter-generator and summary-statistics calculation tools, embedded in an open source C software package called MetHis . It performs independent SNPs or microsatellites simulations under any two-source populations versions of the Verdu and Rosenberg (2011) general model of admixture; and is adapted to conduct ABC inferences with existing machine-learning ABC tools implemented in R (R Development Core Team, 2017).
We show that our MetHis -ABC framework can accurately distinguish major classes of complex historical admixture models, involving multiple admixture-pulses, recurring increasing or decreasing admixture over time, or combination of these models, and provides conservative posterior parameter inference under chosen models. Furthermore, we introduce the quantiles and higher moments of the distribution of admixture fractions in the admixed population as highly informative summary-statistics for ABC model-choices and posterior-parameter estimations.
We exemplify our approach by reconstructing the complex admixture histories underlying observed genetic patterns separately for the African American (ASW) and Barbadian (ACB) populations. Both populations are known to be admixed populations of European and African descent in the context of the Transatlantic Slave Trade, whose histories of admixture remain largely unknown (e.g. Baharian et al., 2016; Martin et al., 2017). In this case-study, we find that the ACB and ASW populations’ admixture histories are much more complex than previously inferred, and further reveal the diversity of histories undergone by these admixed populations during the TAST in the Americas.