Efficient Forward Simulation of Fisher-Wright Populations with Stochastic Population Size and Neutral Single Step Mutations in Haplotypes

TL;DR

This paper introduces an efficient algorithm for exact forward simulation of Fisher-Wright populations with stochastic size and neutral mutations, implemented in an open-source R package, facilitating large-scale population genetics studies.

Contribution

The paper presents a novel, efficient algorithm for simulating Fisher-Wright populations using haplotype-based data structures, enabling large population simulations with stochastic sizes and neutral mutations.

Findings

Able to simulate very large populations efficiently

Supports stochastic population size with flexible growth

Ideal for studying lineage markers like Y-STR

Abstract

In both population genetics and forensic genetics it is important to know how haplotypes are distributed in a population. Simulation of population dynamics helps facilitating research on the distribution of haplotypes. In forensic genetics, the haplotypes can for example consist of lineage markers such as short tandem repeat loci on the Y chromosome (Y-STR). A dominating model for describing population dynamics is the simple, yet powerful, Fisher-Wright model. We describe an efficient algorithm for exact forward simulation of exact Fisher-Wright populations (and not approximative such as the coalescent model). The efficiency comes from convenient data structures by changing the traditional view from individuals to haplotypes. The algorithm is implemented in the open-source R package 'fwsim' and is able to simulate very large populations. We focus on a haploid model and assume stochastic population size with flexible growth specification, no selection, a neutral single step mutation process, and self-reproducing individuals. These assumptions make the algorithm ideal for studying lineage markers such as Y-STR.