Fine-tuning of Approximate Bayesian Computation for human population genomics

Approximate Bayesian Computation (ABC) is a flexible statistical tool widely applied to addressing a variety of questions regarding the origin and evolution of humans. The significant growth of genomic scale data from diverse geographic populations has facilitated the use of ABC in modelling the complex processes that underlie human demography and local adaptation. However, a fundamental issue still remains in how to efficiently capture patterns of genetic variation with a set of summary statistics in order to achieve better approximation of Bayesian inference. Here, we review recent advances in ABC methodology and its applications for human population genomics, with a particular focus on optimal tuning of ABC approaches for different types of genetic data and different sets of evolutionary parameters.