Detecting Evolutionary Change-Points with Branch-Specific Substitution Models and Shrinkage Priors

TL;DR

This paper introduces a scalable, automatic method for detecting evolutionary change-points using branch-specific substitution models combined with shrinkage priors, improving inference efficiency significantly.

Contribution

It develops an analytical gradient algorithm for high-dimensional models, enabling automatic change-point detection without prior knowledge and vastly improving computational speed.

Findings

Achieved up to 126-fold speedup in maximum likelihood optimization.

Real-world application to BRCA1 gene evolution and mpox viral sequences.

Enhanced Bayesian inference with up to 2026-fold performance improvement.

Abstract

Branch-specific substitution models are popular for detecting evolutionary change-points, such as shifts in selective pressure. However, applying such models typically requires prior knowledge of change-point locations on the phylogeny or faces scalability issues with large data sets. To address both limitations, we integrate branch-specific substitution models with shrinkage priors to automatically identify change-points without prior knowledge, while simultaneously estimating distinct substitution parameters for each branch. To enable tractable inference under this high-dimensional model, we develop an analytical gradient algorithm for the branch-specific substitution parameters where the computational time is linear in the number of parameters. We apply this gradient algorithm to infer selection pressure dynamics in the evolution of the BRCA1 gene in primates and mutational dynamics in viral sequences from the recent mpox epidemic. Our novel algorithm enhances inference efficiency, achieving up to a 126-fold speedup per iteration in maximum likelihood optimization when compared to central difference numerical gradient method and up to a 2026-fold improvement in computational performance within a Bayesian framework using Hamiltonian Monte Carlo sampler compared to conventional univariate random walk sampler.