Joint Likelihood-Free Inference of the Number of Selected SNPS and the Selection Coefficient in an Evolving Population

Abstract

AbstractWith the exact likelihood often intractable, likelihood-free inference plays an important role in the field of population genetics. Indeed, several methodological developments in the context of Approximate Bayesian Computation (ABC) were inspired by population genetic applications. Here we explore a novel combination of recently proposed ABC tools that can deal with high dimensional summary statistics and apply it to infer selection strength and the number of selected loci for data from experimental evolution. While there are several methods to infer selection strength that operate on a single SNP level, our window based approach provides additional information about the selective architecture in terms of the number of selected positions. This is not trivial, since the spatial correlation introduced by genomic linkage leads to signals of selection also at neighboring SNPs. A further advantage of our approach is that we can easily provide an uncertainty quantification using the ABC posterior. Both on simulated and real data, we demonstrate a promising performance. This suggests that our ABC variant could also be interesting in other applications.