Bayesian inference of species trees using diffusion models

TL;DR

This paper introduces exttt{SNAPPER}, a Bayesian method leveraging diffusion models for efficient species tree and demographic inference from large-scale genetic data, implemented in Beast2.

Contribution

It presents a novel diffusion-based Bayesian approach and algorithms for scalable inference of species trees from extensive unlinked genetic markers.

Findings

exttt{SNAPPER} performs well on simulated datasets.

The method successfully analyzes SNP data from rattlesnakes and turtles.

It enables analysis of large datasets with hundreds or thousands of individuals.

Abstract

We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with a new algorithm for numerically computing likelihoods of quantitative traits. The diffusion approach allows for analysis of datasets containing hundreds or thousands of individuals. The method, which we call \snapper, has been implemented as part of the Beast2 package. We introduce the models, the efficient algorithms, and report performance of \snapper on simulated data sets and on SNP data from rattlesnakes and freshwater turtles.