Inferring Species Trees Directly from Biallelic Genetic Markers: Bypassing Gene Trees in a Full Coalescent Analysis

TL;DR

This paper introduces a polynomial-time algorithm that infers species trees directly from biallelic genetic markers, bypassing the need to consider gene trees, and demonstrates its effectiveness through simulations and real data analysis.

Contribution

It presents a novel, efficient method for species tree inference directly from genetic markers, avoiding complex gene tree sampling.

Findings

Successfully infers species trees from simulated data.

Accurately estimates divergence times and population sizes.

Applied to AFLP data from Ourisia species with promising results.

Abstract

The multi-species coalescent provides an elegant theoretical framework for estimating species trees and species demographics from genetic markers. Practical applications of the multi-species coalescent model are, however, limited by the need to integrate or sample over all gene trees possible for each genetic marker. Here we describe a polynomial-time algorithm that computes the likelihood of a species tree directly from the markers under a finite-sites model of mutation, effectively integrating over all possible gene trees. The method applies to independent (unlinked) biallelic markers such as well-spaced single nucleotide polymorphisms (SNPs), and we have implemented it in SNAPP, a Markov chain Monte-Carlo sampler for inferring species trees, divergence dates, and population sizes. We report results from simulation experiments and from an analysis of 1997 amplified fragment length polymorphism (AFLP) loci in 69 individuals sampled from six species of {\em Ourisia} (New Zealand native foxglove).