Predicting evolution from the shape of genealogical trees

TL;DR

This paper introduces a method to predict the future evolution of asexual populations by analyzing genealogical tree shapes, successfully applied to influenza virus data to forecast dominant strains.

Contribution

It presents a novel approach that uses genealogical tree structures to predict evolutionary outcomes without species-specific data, applicable to any asexual population under selection.

Findings

Predicts influenza progenitor lineages with near optimal accuracy in 30% of cases.

Makes informative predictions in 16 out of 19 years.

Demonstrates persistent fitness variation among circulating viruses.

Abstract

Given a sample of genome sequences from an asexual population, can one predict its evolutionary future? Here we demonstrate that the branching patterns of reconstructed genealogical trees contains information about the relative fitness of the sampled sequences and that this information can be used to predict successful strains. Our approach is based on the assumption that evolution proceeds by accumulation of small effect mutations, does not require species specific input and can be applied to any asexual population under persistent selection pressure. We demonstrate its performance using historical data on seasonal influenza A/H3N2 virus. We predict the progenitor lineage of the upcoming influenza season with near optimal performance in 30% of cases and make informative predictions in 16 out of 19 years. Beyond providing a tool for prediction, our ability to make informative predictions implies persistent fitness variation among circulating influenza A/H3N2 viruses.