1.2 | Approximate Bayesian Computation demographic
inference
Approximate Bayesian Computation (ABC) approaches
(Beaumont, Zhang, & Balding, 2002;
Tavaré, Balding, Griffiths, & Donnelly,
1997), represent a promising alternative to infer complex admixture
histories from genetic data. Indeed, ABC has been successfully used
previously to formally test alternative demographic scenarios
hypothesized to be underlying observed genetic patterns, and to
estimate, a posteriori , the parameters of the winning models,
when ML methods could not operate (Boitard,
Rodriguez, Jay, Mona, & Austerlitz, 2016;
Fraimout et al., 2017;
Verdu et al., 2009).
ABC model-choice and posterior-parameter estimation rely on comparing
observed summary statistics to the same set of statistics calculated
from simulations produced under competing demographic scenarios
(Beaumont et al., 2002;
Blum & François, 2010;
Csilléry, François, & Blum, 2012;
Pudlo et al., 2016;
Sisson, Fan, & Beaumont, 2018;
Wegmann, Leuenberger, & Excoffier,
2009). Each simulation, and corresponding vector of summary statistics,
is produced using model-parameters drawn randomly from prior
distributions explicitly specified by the user. This makes ABC a
priori particularly well suited to investigate highly complex
historical admixture scenarios for which likelihood functions are very
often intractable, but for which genetic simulations are feasible
(Gravel, 2012;
Pritchard et al., 1999;
Verdu & Rosenberg, 2011;
Buzbas & Verdu, 2018).