Bayesian Computation and Model Selection in Population Genetics

TL;DR

This paper advances Bayesian inference in population genetics by reformulating regression adjustments within a General Linear Model framework, enabling model selection and application to chimpanzee population subdivision.

Contribution

It introduces a GLM-based reformulation of ABC regression adjustment, integrating Bayesian model selection methods into population genetics analysis.

Findings

Successful application to chimpanzee population subdivision

Enhanced accuracy of Bayesian inference with GLM reformulation

Facilitated model comparison using Bayes factors

Abstract

Until recently, the use of Bayesian inference in population genetics was limited to a few cases because for many realistic population genetic models the likelihood function cannot be calculated analytically . The situation changed with the advent of likelihood-free inference algorithms, often subsumed under the term Approximate Bayesian Computation (ABC). A key innovation was the use of a post-sampling regression adjustment, allowing larger tolerance values and as such shifting computation time to realistic orders of magnitude (see Beaumont et al., 2002). Here we propose a reformulation of the regression adjustment in terms of a General Linear Model (GLM). This allows the integration into the framework of Bayesian statistics and the use of its methods, including model selection via Bayes factors. We then apply the proposed methodology to the question of population subdivision among western chimpanzees Pan troglodytes verus.