Directional genetic differentiation and relative migration. Issue 11 (20th April 2016)

• Record Type: Journal Article
• Authors: Sundqvist, Lisa; Keenan, Kevin; Zackrisson, Martin; Prodöhl, Paulo; Kleinhans, David
• Is Part Of: Ecology and evolution. Volume 6:Issue 11(2016:Jun.)
• Page Start: 3461
• Page End: 3475
• Publication Date: 2016-04-20
• Subjects: Allele frequency data -- asymmetric migration -- directional gene flow -- dispersal; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.2096
• Languages: English
• ISSNs: 2045-7758

Abstract

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. As 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 study. 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. Abstract : This new approach allows the estimation of directional components of genetic differentiation 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.