Non-stationary patterns of isolation-by-distance: inferring measures of local genetic differentiation with Bayesian kriging

TL;DR

This paper introduces a Bayesian kriging method to infer local genetic differentiation, revealing non-stationary isolation-by-distance patterns caused by spatial demographic variations, applicable at population or individual levels.

Contribution

The study develops a novel Bayesian kriging approach to characterize non-stationary isolation-by-distance patterns without predefined populations, applicable to landscape genetics.

Findings

Maps of local genetic differentiation can identify barriers to gene flow.

The method detects continuous variations in gene flow across habitats.

Application to human and plant data demonstrates the method's effectiveness.

Abstract

Patterns of isolation-by-distance arise when population differentiation increases with increasing geographic distances. Patterns of isolation-by-distance are usually caused by local spatial dispersal, which explains why differences of allele frequencies between populations accumulate with distance. However, spatial variations of demographic parameters such as migration rate or population density can generate non-stationary patterns of isolation-by-distance where the rate at which genetic differentiation accumulates varies across space. To characterize non-stationary patterns of isolation-by-distance, we infer local genetic differentiation based on Bayesian kriging. Local genetic differentiation for a sampled population is defined as the average genetic differentiation between the sampled population and fictive neighboring populations. To avoid defining populations in advance, the method can also be applied at the scale of individuals making it relevant for landscape genetics. Inference of local genetic differentiation relies on a matrix of pairwise similarity or dissimilarity between populations or individuals such as matrices of FST between pairs of populations. Simulation studies show that maps of local genetic differentiation can reveal barriers to gene flow but also other patterns such as continuous variations of gene flow across habitat. The potential of the method is illustrated with 2 data sets: genome-wide SNP data for human Swedish populations and AFLP markers for alpine plant species. The software LocalDiff implementing the method is available at http://membres-timc.imag.fr/Michael.Blum/LocalDiff.html