Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and their selection...

Because exact likelihood is often intractable, likelihood-free inference plays an important role in population genetics. Indeed, several methodological developments in Approximate Bayesian computation (ABC) were inspired by applications in population genetics. Here, we explore a novel combination of recently proposed ABC tools capable of handling high-dimensional summary statistics and apply them to infer selection strength and the number of selected loci from experimental evolution data. While several methods infer selection strength at the single-nucleotide polymorphism (SNP) level, our approach provides additional information about the selective architecture, including the number of selected positions in a candidate window of interest. Providing such additional information is non-trivial, as the spatial correlation induced by genomic linkage can produce selection signals at neighbouring SNPs. A further advantage of our approach is that it readily quantifies uncertainty via the ABC posterior. On both simulated and real data, we demonstrate promising performance. Our results suggest that this ABC variant may also prove useful in broader applications.