diplo-locus: a lightweight toolkit for inference and simulation of time-series genetic data under general diploid selection

Abstract

Whole-genome time-series allele frequency data are becoming more prevalent as ancient DNA sequences and data from evolve-and-resequence experiments are generated at a rapid pace. Such data present unprecedented opportunities to elucidate the dynamics of genetic variation under selection. However, despite many methods to infer parameters of selection models from allele frequency trajectories available in the literature, few provide user-friendly implementations for large-scale empirical applications. Here, we present diplo-locus, an open-source Python package that provides functionality to simulate and perform inference from time-series data under the Wright–Fisher diffusion with general diploid selection. The package includes Python modules as well as command-line tools and is available at: https://github.com/steinrue/diplo_locus. Keywords: time-series genetic data, diploid selection, hidden Markov model