Disentangling the effects of geographic and ecological isolation on genetic differentiation

TL;DR

This paper introduces BEDASSLE, a Bayesian method that quantifies how geographic and ecological differences contribute to genetic differentiation, improving upon traditional methods like the partial Mantel test.

Contribution

The paper presents a novel Bayesian approach, BEDASSLE, for disentangling geographic and ecological effects on genetic differentiation using Gaussian process models.

Findings

BEDASSLE accurately estimates the relative effects of geography and ecology.

The method outperforms traditional partial Mantel tests in simulation.

Empirical applications demonstrate its utility with human and plant datasets.

Abstract

Populations can be genetically isolated both by geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user-friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte datasets.