Directional genetic differentiation and asymmetric migration

TL;DR

This paper introduces a simple, efficient method and a web tool to detect and analyze asymmetric gene flow between populations using genetic differentiation measures, addressing a common limitation in population genetics.

Contribution

A novel, computationally efficient approach for estimating directional genetic differentiation and asymmetric migration from genetic data, implemented in a user-friendly web application.

Findings

Method accurately detects complex migration patterns in simulations.

Approach works with classical and modern genetic differentiation measures.

Web tool facilitates accessible analysis of gene flow asymmetries.

Abstract

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. Since information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user-friendly web application called divMigrate-online introduced in this paper. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.