Phylogenetic least squares estimation without genetic distances

Abstract

Least squares estimation of phylogenies is an established family of methods with good statistical properties. State-of-the-art least squares phylogenetic estimation proceeds by first estimating a distance matrix, which is then used to determine the phylogeny by minimizing a squared-error loss function. Here, we develop a method for least squares phylogenetic inference that does not rely on a pre-estimated distance matrix. Our approach allows us to circumvent the typical need to first estimate a distance matrix by forming a new loss function inspired by the phylogenetic likelihood score function; in this manner, inference is not based on a summary statistic of the sequence data, but directly on the sequence data itself. We use a Jukes-Cantor substitution model to show that our method leads to improvements over ordinary least squares phylogenetic inference, and is even observed to rival maximum likelihood estimation in terms of topology estimation efficiency. Using a Kimura 2-parameter model, we show that our method also allows for estimation of the global transition/transversion ratio simultaneously with the phylogeny and its branch lengths. This is impossible to accomplish with any other distance-based method as far as we know. Our developments pave the way for more optimal phylogenetic inference under the least squares framework, particularly in settings under which likelihood-based inference is infeasible, including when one desires to build a phylogeny based on information provided by only a subset of all possible nucleotide substitutions such as synonymous or non-synonymous substitutions.