Phylogenetic Derivative: A Tool for Assessing Local Tree Reconstruction in the Presence of Recombination

TL;DR

This paper introduces the phylogenetic derivative, a new efficient metric for assessing local tree variation and recombination events along chromosomes, improving analysis of large genomic datasets.

Contribution

The paper presents a novel phylogenetic derivative metric that effectively detects recombination and local tree differences, addressing scalability and realism issues in existing methods.

Findings

Performs well on simulated data

Effective in analyzing real mouse data

Scalable to large genomic datasets

Abstract

Recently, much attention has been given to understanding recombination events along a chromosome in a variety of field. For instance, many population genetics problems are limited by the inaccuracy of inferred evolutionary histories of chromosomes sampled randomly from a population. This evolutionary history differs among genomic locations as an artifact of recombination events along a chromosome. Thus, much recent attention has been focused on identifying these recombination points. However, many proposed methods either make simplifying, but unrealistic, assumptions about recombination along a chromosome, or are unable to scale to large genome-wide data like what has become commonplace in statistical genetics. Here, we introduce a \emph{phylogenetic derivative} to describe the relatedness of neighboring trees along a chromosome. This phylogenetic derivative is a computationally efficient, flexible metric that can be also be used assess the prevalence of recombination across a chromosome. These proposed methods are tested and perform well in analyzing both simulated data and a real mouse data set.