Developing a statistically powerful measure for quartet tree inference using phylogenetic identities and Markov invariants

TL;DR

This paper introduces a new statistically robust method for phylogenetic quartet inference using Markov invariants, demonstrating superior power and satisfying key statistical properties for reliable tree reconstruction.

Contribution

It develops a bias-corrected Markov invariants approach and extends phylogenetic invariants to improve inference accuracy and robustness.

Findings

Bias-corrected Markov invariants outperform existing methods in simulations

The new approach satisfies key statistical properties for phylogenetic inference

Extensions to phylogenetic invariants improve detection of process violations

Abstract

Recently there has been renewed interest in phylogenetic inference methods based on phylogenetic invariants, alongside the related Markov invariants. Broadly speaking, both these approaches give rise to polynomial functions of sequence site patterns that, in expectation value, either vanish for particular evolutionary trees (in the case of phylogenetic invariants) or have well understood transformation properties (in the case of Markov invariants). While both approaches have been valued for their intrinsic mathematical interest, it is not clear how they relate to each other, and to what extent they can be used as practical tools for inference of phylogenetic trees. In this paper, by focusing on the special case of binary sequence data and quartets of taxa, we are able to view these two different polynomial-based approaches within a common framework. To motivate the discussion, we present three desirable statistical properties that we argue any phylogenetic method should satisfy: (1) sensible behaviour under reordering of input sequences; (2) stability as the taxa evolve independently according to a Markov process; and (3) ability to detect if the conditions of a continuous-time process are violated. Motivated by these statistical properties, we develop and explore several new phylogenetic inference methods. In particular, we develop a statistical bias-corrected version of the Markov invariants approach which satisfies all three properties. We also extend previous work by showing that the phylogenetic invariants can be implemented in such a way as to satisfy property (3). A simulation study shows that, in comparison to other methods, our new proposed approach based on bias-corrected Markov invariants is extremely powerful for phylogenetic inference.