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

## TL;DR

diplo-locus is a Python toolkit for simulating and analyzing time-series genetic data under diploid selection models.

## Contribution

It introduces a user-friendly, open-source implementation for inference and simulation under general diploid selection.

## Key findings

- diplo-locus provides tools for simulating genetic data under the Wright–Fisher diffusion model.
- The package supports inference of selection parameters from time-series allele frequency data.
- It includes both Python modules and command-line tools for accessibility and scalability.

## 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.