Low-ABC: a robust demographic inference from low-coverage whole-genome data through ABC

The reconstruction of past demographic histories relies on the pattern of genetic variation shown by the sampled populations; this means that an accurate estimation of genotypes is crucial for a reliable inference of past processes. A commonly adopted approach to reconstruct complex demographic dynamics is the Approximate Bayesian Computation (ABC) framework. It exploits coalescent simulations to generate the expected level of variation under different evolutionary scenarios. Demographic inference is then performed by comparing the simulated data with the genotypes called in the sampled individuals. Low sequencing coverage drastically affects the ability to reliably call genotypes, thus making low-coverage data unsuitable for such powerful inferential approaches.

Here, we present Low-ABC, a new ABC approach to infer past population processes using low-coverage whole-genome data. Under this framework, both observed and simulated genetic variation are not directly compared using called genotypes, but rather obtained using genotype likelihoods to consider the uncertainty caused by the low sequencing coverage. We first evaluated the inferential power of this procedure in distinguishing among different demographic models and in inferring model parameters under different experimental conditions, including a wide spectrum of sequencing coverage (1x to 30x), number of individuals, number, and size of genetic loci.

We showed that the use of genotype likelihoods integrated into an ABC framework provides a reliable inference of past population dynamics, thus making possible the application of model-based inference also for low-coverage data. We then applied Low-ABC to shed light on the relationship between Mesolithic and Early Neolithic European populations.